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The world targets carbon neutral attracting more attention to renewable energy sources and their effective application. Hydrogen as a primary form of fuel is of crucial importance to achieve global aiming, which requires high efficiency, renewable power generation technologies but facing difficulty on its production and storage. Plenty of hydrogen carriers bring a new horizon for future application but its terminal application technology with high efficiency and demonstration doesn't appear. Solid oxide fuel cell has the natural advantage on multi-fuel selectivity which bring its wide application with different fuel sources and future power generation technology with promising future. The commercial SOFC system request comprehensive property understanding, diagnosis and system design. The hotbox unit requests the validation for the thermal balance of the SOFC system which integrates with the pre-reformer, SOFC unit, exhaust combustor and heat exchanger. A comprehensive and commercial-aimed model design and simulations are necessary for the SOFC system property understanding of its multi-fuel property. In this thesis, a complicated and validated semi-empirical SOFC module has been conducted with multi-function to accurately predict the real working condition for the SOFC system design. The hydrogen, ammonia, DME, methanol, and CH4 as primary hydrogen carrier presents its SOFC property difference and operation strategy. The SOFC system emulation result shows the control strategy and feasibility of SOFC application for domestic applications.

Computer-aided engineering methods have been increasingly used in various industries to support the design of complex systems and validate their performance by simulating their physical behaviour. This created a need to generate fit-for-purpose analysis models with a turnaround time comparable to the time required to generate the design model. However, most existing tools for computer-aided design and engineering have been developed independently to address different issues and therefore lack integration. As a consequence, many current simulation workflows rely on experienced users to adapt, or create from scratch, an appropriate analysis model to represent the physics to be solved. Preparing complex multi-physics analysis models for very large assemblies can take up to several person-years, limiting the tools for validation rather than having the maximum impact on design decisions. This thesis builds on previous research on automatic hex-mesh generation and CAD-CAE integration to develop a novel framework for generating CAE analysis models from CAD design models. The framework uses ‘virtual topology’ to bridge the representation gap between CAD and CAE tools, overcome geometric limitations and accommodate the multiple different representations necessary for analysis workflows. Virtual topology is also a key enabler for the flexible modification and updating of the analysis models. Robust information management is achieved by implementing a cellular model within the virtual topology framework, which hosts and interconnects the different representations while capturing the information necessary for downstream processes. In this work, 9 pre-processing reasoners are defined within the virtual topology framework to automatically generate hex-dominant meshes by incrementally identifying and extracting virtual regions for which a simple hex-meshing strategy can be applied. A new translator tool is introduced to query virtual models using virtual geometry curves. Additional reasoners are then used to automatically process the different meshing strategies and generate a conformal mesh, reducing the CAD to mesh time from several hours to several minutes compared to manual decomposition and existing meshing methods. Incorporating meshing constraints early in the pre-processing workflow also enables the enhancement of the pre-processing tools. A new method to propagate design modifications is developed, so that a decomposed analysis model and its associated mesh can be updated after topological changes on the design model. This is up to 95% faster than re-starting the automated pre-processing from the beginning. Finally, examples of aeronautical components are processed using the virtual topology framework developed to demonstrate the benefits of virtual topology based workflows for hex-dominant mesh generation.

In this thesis the application of autonomous control concepts to logistics networks is studied by means of a simulation model. This simulation model is based on an actual outbound bulk product supply network of a commodity company. Logistics planning and operation is facing growing challenges, such as increasing complexity and distribution, driven by Megatrends such as globalisation and integration. Decentralisation through autonomous control seems to offer to a promising approach to address these challenges. The idea for the supply network at hand is therefore, to enable individual transportation units to autonomously take operational decisions, thus shifting control of the supply network from a central to a local perspective. In surveying the literature and the academic discussion on autonomous control in logistics, software agents are identified as a suitable and well-studied approach to implement such a concept. Therefore, a multi-agent-based simulation model of the supply network is constructed to execute and test the solution. The model is built using data based on empirical observations and offers a full-scale simulation of the actual supply network. In the model, software agents represent the individual transportation units, allowing them to communicate and interact autonomously, effectively decentralising operational control. A comparative simulation experiment is designed and carried out, contrasting several different control scenarios. The simulation results obtained show, that autonomous control can positively impact the performance of this supply network. Autonomous control scenarios require a lower number of trucks to achieve full order delivery and help to increase robustness of the supply network regarding the impact of environmental factors. Additionally, the more efficient use of transportation capacity may lead to a reduction in cost for transportation. The findings are verified with an industry subject matter expert and potential barriers onthe path towards implementation are described.

The global oil reserve reduction and the unavailability of easy to reach oil have necessitated the need for oil and gas producers and countries to look into marginal oil and gas fields. These are fields defined as abandoned, not commercially and economically viable, and not categorised as a major find. New and emerging smaller companies, especially NOCs and Indigenous oil companies, have seen these fields as opportunities to become truly oil & gas exploration and production companies. However, investment decisions were made by most of these companies without the right financial and economic tools thereby creating gaps in their decision quality and day to day knowledge of their investment profitability indices while in operation. This necessitated the need for the development of an Integrated Techno-Economic/Financial Model that can be used for investment decision and operating of Marginal oil and gas fields. In the development of this tool/model, a structured process of research and project execution known as the gate system and the PMI-5 Project Management Process was used to carry out this PhD research to develop model/tool with the required flexibility to support robust decisions on investments and operating of Marginal Field Facilities Several Techno-Economic Assessment (TEA) Methods were reviewed and the Discounted Cash Flow TEA type selected to be the best fit method for the analysis because of its ability to look at the entire field life of the opportunity. The integration of these methods with an Integrated Risk Management System led to the development of the UZO-MARG Economic and Financial Model which is one of the key deliverable for this research. The Model tool was validated with an actual operating Marginal Field development data with acceptable error Margin even while not been privy to the entire project execution data for this field. On using the UZO-MARG tool to evaluate the Shekinah field, it was observed that the PSC agreement returns better VIR, NPV and ROACE, compared to the JV agreement for the operator e.g. NPV $1219.1mln; IRR 18% for PSC terms versus NPV $35.3mln; IRR 2% for JV terms. In addition, the Model tool is also applicable for the determination of economic and financial parameters for various technology applications i.e. using Hybrid Power Generation technology (for reduced CO2 emissions) instead of a standalone renewable energy or conventional energy technology systems.

Oxaliplatin is an anticancer drug widely used in cancer chemotherapy. This thesis evaluates whether a specific dose-banding scheme for oxaliplatin could replace the individual dosing method that is currently used in the oxaliplatin administration. Dose-banding was introduced into UK clinical practice in 2001, as it reduces delays in patients receiving their treatment and, through quality control and end-product testing, safeguards the infusion quality and patient safety. A range of studies were included in this thesis: an extended stability study on oxaliplatin infusions using a sequential temperature cycling design; studies on oxalate, a potential degradation product and metabolite of oxaliplatin which has been linked to oxaliplatin neurotoxicity and the development of an ex vivo pharmacokinetic (PK) simulation model to compare the effect of different oxaliplatin dosing methods on its therapeutic outcomes. The shelf-life of oxaliplatin infusions over a concentration range of 0.2 mg/mL – 0.7 mg/mL is extended to 84 days when stored at 2 – 8℃ plus a further 7 days after being left at room temperature (25℃) for 24 hours. This ensures the unused oxaliplatin infusions are safe to be re-issued to patients, which could reduce drug wastage. The oxalate study suggests that the dose-limiting neurotoxicity of oxaliplatin is unlikely to be directly related to the oxalate produced from oxaliplatin degradation in infusions or from the non-enzymic transformation of oxaliplatin in vivo because the oxalate levels from these routes are minor compared to the endogenous level. The safety and efficacy of dose-banding schemes was demonstrated by comparing the simulated PK characteristics gained from the ex vivo model. Dose-banding with the +10% maximum deviation was selected as the most promising dosing scheme for oxaliplatin. Finally, recommendations are made concerning the introduction of oxaliplatin dose-banding scheme into clinical practice, and on the benefits of harmonised dose-banding schemes.

Mathematical relationships have long been used to describe many aspects of muscle function such as the relationship between muscle force and muscle length, muscle force and velocity of contraction or the degree of muscle activation during a contraction. During this work various mathematical expressions have been employed in order to gain an insight into different aspects of muscle activity. The first part of the work examined whether performing a strength protocol on a dynamometer can lead to an increase in eccentric strength output as well as in the neuromuscular activation of the quadriceps group of muscles that appears inhibited during slow concentric and fast eccentric contractions. Neuromuscular activation was modelled via a three-parameter sigmoid function that was also tested for robustness to perturbations in the maximum activation values. During the second part of the study the "functional" hamstrings to quadriceps ratio H:Qfun was expressed as a function of two variables i.e., angular velocity and joint angle. Initially nine-parameter torque-angular velocity-angle profiles were obtained for the knee extensors and flexors from a group of participants. A theoretical 17- parameter H:Qfun function was then derived for each dataset. Subsequently, a simpler, 6-parameter function was derived, RE = aexp(bωn + cθm)-dω1/2θ2 that best reproduced the original 17-parameter fit. Finally, a six-segment subject specific torque-driven model of the Snatch lift was developed in order to investigate the optimal mechanics of the lift. The model simulated the lift from its initiation until the end of the second pull when the feet of the athlete momentarily leave the platform. The six-segment model comprised of foot, shank, thigh, torso (head + trunk), arm and forearm segments with torque generators at the ankle, knee, hip and shoulder joints respectively. The torque profiles were obtained using an isokinetic dynamometer.

Distributed shared memory (DSM) systems have been recognised as a compelling platform for parallel computing due to the programming advantages and scalability. DSM systems allow applications to access data in a logically shared address space by abstracting away the distinction of physical memory location. As the location of data is transparent, the sources of overhead caused by accessing the distant memories are difficult to analyse. This memory locality problem has been identified as crucial to DSM performance. Many researchers have investigated the problem using simulation as a tool for conducting experiments resulting in the progressive evolution of DSM systems. Nevertheless, both the diversity of architectural configurations and the rapid advance of DSM implementations impose constraints on simulation model designs in two issues: the limitation of the simulation framework on model extensibility and the lack of verification applicability during a simulation run causing the delay in verification process. This thesis studies simulation modelling techniques for memory locality analysis of various DSM systems implemented on top of a cluster of symmetric multiprocessors. The thesis presents a simulation technique to promote model extensibility and proposes a technique for verification applicability, called a Specification-based Parameter Model Interaction (SPMI). The proposed techniques have been implemented in a new interpretation-driven simulation called DSiMCLUSTER on top of a discrete event simulation (DES) engine known as HASE. Experiments have been conducted to determine which factors are most influential on the degree of locality and to determine the possibility to maximise the stability of performance. DSiMCLUSTER has been validated against a SunFire 15K server and has achieved similarity of cache miss results, an average of +-6% with the worst case less than 15% of difference. These results confirm that the techniques used in developing the DSiMCLUSTER can contribute ways to achieve both (a) a highly extensible simulation framework to keep up with the ongoing innovation of the DSM architecture, and (b) the verification applicability resulting in an efficient framework for memory analysis experiments on DSM architecture.

This thesis estimates potential runway throughput gains using a reduced wake separation based on the 123 most prevalent aircraft in the United States fleet. The analysis considers Runway Occupancy Time (ROT) constraint factors and existing geometric design factors. This research extracts the historic data from Airport Surface Detection Equipment Model X (ASDE-X) for analysis. The Runway Exit Design Interactive Model (REDIM) is used to optimize the runway exit locations and reduce ROT. The runway throughput and safety factors are generated from a Monte Carlo runway simulator. This thesis focuses on selected US airport runways that could benefit from geometric optimization. The study aims to estimate ROT improvements through improved runway exit locations and the changes in runway throughput considering ROT constraint factors. The results of the thesis show that Dallas Fort Worth International Airport (DFW) runway 35C and Denver International Airport (DEN) runway 16R have the potential to improve the ROT. After the optimization to locate runway exits, the ROT time of the RECAT group F and G aircraft (greater than 90% of the arrivals) was reduced by three to five seconds (a very significant effect). After the ROT reductions and with the application of reduced wake separation criteria with the ROT constraint factor applied, the arrival-only capacity of DFW runway 35C improved by 3.5 arrivals per hour. The arrival-only capacity on DEN runway 16R improved by 2.14 arrivals per hour. Both runways maintained a probability of violation between time-based separation and ROT time at around 1.5%. The study concludes that the application of reduced wake separation criteria alone is a necessary but insufficient condition to improve the efficiency of arrival runways. Through careful improvements of runway exit locations, reductions in ROT provide reliability and efficiency to the operation of runways.

Firefighters (FF) face high temperature conditions fighting structural and wildland fires. There are two main sources of heat that the FF must be protected against: one is environmental external heat; the other is metabolic internal heat. FF also wear thermally insulating apparel that shields against external heat, but traps metabolic heat. The external and internal heat and the insulating apparel together cause heat stress to build up that can cause heart attacks in FF. The objective of this thesis is therefore to design apparel to better address the competing requirements on one hand to shield the FF from external heat, and on the other hand to dissipate internal heat. The approach taken to develop improved apparel is to use carbon nanotube (CNT) fabric as a layer in firefighter apparel to cool the firefighter. CNT fabric has high thermal conductivity in-plane and low thermal conductivity through the thickness. This directionality in thermal conductivity can be taken advantage of in the design of FF apparel. Typical apparel for FF has low thermal conductivity in all directions which reduces the external heat entering the garment. However, the typical apparel also reduces heat dissipation from the body. CNT fabric can improve this situation. The modeling of FF apparel in this thesis includes theoretical mathematical heat transfer models constructed in MATLAB both for FF gloves and an underlayer shirt. The heat transfer algorithm developed contains heat conduction, heat convection, heat radiation, and evaporation processes. By constructing this simulation model, the thermal performance of gloves and garments with integrated CNT fabric can be predicted. Modeling provides a basis for determining the optimal thickness and design of the fabric used in glove and garment. The simulation results show that CNT fabric spreads heat in the plane of the fabric, and through the thickness of the fabric. However, water evaporation may provide the greatest cooling from fabric, especially with forced convection. Particles such as granulated activated carbon or silicon can also be incorporated into the CNT fabric to increase thermal conductivity and capillarity. Testing on a hot plate demonstrated heat spreading and cooling by the CNT fabric. The cooling provided by a garment depends on the environment in which the FF is working. The temperature gradient between the FF and environment, air humidity, and air flow strongly affect the cooling. Durability, safety and cost of CNT fabric are also considered in a preliminary way. This thesis thus produced a basic approach and algorithm for modeling cooling in composited CNT fabric. The model incorporates key environmental and material variables related to garment design and shows that CNT fabric enhances the thermal performance of traditional fabric materials and is a candidate for use in FF apparel to reduce heat stress.

An energy transition in cities is underway, bringing new technical and financial chal- lenges for multi-utility companies. The implementation of decarbonization policies has a long-term impact on the design, operation, and profitability of electrical, heat, and mobility infrastructure, resulting in significant investments and various types of risks. These risks are amplified by the individual choices of thousands of customers whose present and future behavior is uncertain. Therefore, it is necessary to analyze the inter- dependencies among the various actors in the energy transition and quantitatively assess its implications. Multi-utility companies generally address these unknowns by relying on top-down models based on historical data, statistical models, and architectural standards. These models often do not provide the granularity needed for city-scale assessments and are not well suited to model discontinuities, such as the introduction of new technologies. Thus, this study’s main contribution is the development of three predictive, integrated, bottom-up models to simulate electricity, heat, and electric mobility demand at the urban level. The most significant methodological advances of this study include the integration of the demand models into a single, high-resolution “digital twin” of an actual city, as well as the inclusion of behavioral models of individuals, obtained through the extensive and large-scale use of agent-based models. Three case studies are used to evaluate the accuracy and performance of the developed models and their suitability in supporting a sustainable energy transition. In the mobility case study, it was found that providers face considerable financial exposure at today’s low electric vehicle penetration, and the initial investment in public charging stations would only break even from a 4% electric vehicle penetration. Moreover, the study revealed that revenues based on parking duration break even faster than tariffs based on power supplied. However, the revenues from parking fees are also found to be more sensitive to user charging behavior. Furthermore, at 20% penetration, charging at public and charging at work can cause a local increase in grid load of up to 78%. In the heat case study, a model considering building occupants’ behavior was found to quantify the time- resolved heat demand better, achieving an average annual error of -4.8%. To support the city’s plans to expand the district heating network, the model was used in conjunction with a predictive model capable of quantifying the probability of a building connecting to the future network. By considering both the spatial distribution of heat demand and the probability that a building would connect to the future network, the internal rate of return of the district heat infrastructure could be increased by 25%, compared to a network extension in which the probability of connection was not modeled. Finally, the electricity study demonstrated how the implementation of behavioral models linked to location-specific habits improves electricity demand forecasts. The impact on the grid of future population dynamics and increased electricity demand due to the electrification of mobility and decarbonization of the building stock through heat pumps was quantified by identifying the areas of the city where the increase in load on the grid would be the greatest. In conclusion, this study demonstrates the superiority of bottom-up agent-based energy demand forecasting models compared to other approaches, as well as the importance of incorporating individual behavioral patterns into the models. The results of this work have already been used in the test-case city to concretely support decision-making processes, improve business models, and ensure that future investments are socially accepted.

This research integrated knowledge of lucerne crop physiology into the Agricultural Production Systems sIMulator (APSIM) next generation (APSIM NextGen) model framework to develop and verify a comprehensive lucerne simulation model (APSIM NextGen lucerne model). The model was developed to simulate the growth, development and quality of lucerne cultivars grown under different defoliation management and growth conditions. One of the major challenges for developing a lucerne crop simulation model is to capture the seasonality of perennial reserves and their effect on shoot regrowth in response to different defoliation regimes. In this thesis, model development and testing was based on long-term field datasets with multiple defoliation regimes (28 day: S; 42 day: L; and 84 day: H) and three genotypes of fall dormancy (FD; FD2, FD5 and FD10) under irrigated conditions. The APSIM Plant Modelling Framework (PMF) was used to simulate generic organs (leaf, stem and root) and represent key crop physiological processes, including crop phenological development, canopy expansion, dry matter and N accumulation, remobilization and partitioning. Development was parameterized based on thermal time (Tt) targets and a photoperiod (Pp) response. Seedling crops required a juvenile phase (Ttjuv of 215 to 547 ˚Cd). For both seedling and regrowth crops, the Tt to reach 50% buds visible (Tt0-bv) increased as Pp shortened in autumn, a minimum of 278 ˚Cd for the basic vegetative (TtBVP) period was required at Pp >14h for regrowth crops to reach buds visible stage. After crops reached buds visible stage, another 310 ˚Cd of Tt (Ttbv-fl) was required to reach flowering. Lucerne biomass supply was parameterized as the product of accumulated intercepted total radiation, and radiation use efficiency (RUEtotal, g DM MJ-1 total radiation). The intercepted total radiation was calculated by LAI and an extinction coefficient (k) of 0.81. LAI was parameterized as leaf area expansion rate (LAER) and Pp response. LAER declined as the Pp decrease, being 0.018 m2 m-2 ˚Cd-1 at 16.5 h and 0.008 m2 m-2 ˚Cd-1 at 10 h. However, a Pp response was not observed in seedling crops and regrowth crops in increasing Pp conditions. The RUEtotal was 1.1±0.31 g DM MJ−1 at 18 ˚C for both seedling and regrowth crops. Biomass supply was then allocated based on the relative demand of each organ. Leaf and stem biomass demand were parameterized as positive power functions. Root biomass showed a seasonal pattern. The APSIM NextGen lucerne model provided a mechanistic framework to model root biomass dynamics with structural and storage components. Structural root biomass was defined and estimated as the amount of root biomass (~2500 kg ha-1) that had no root maintenance respiration loss in winter. The ratio of storage to structural root differed among development stages and FD classes. In an increasing Pp, there was no storage root demand. The decrease of root biomass during this period was due to remobilization from root to shoots and root maintenance respiration. A remobilization coefficient value and a regrowth coefficient function were used to calculate root remobilization. A remobilization coefficient value was defined as the percentage of storage root biomass per day (5 for FD5, 1 for FD2 and FD10). The regrowth coefficient function includes two parameters (remobilization duration and remobilization rate). Remobilization duration was defined as Tt since harvest, whereas remobilization rate is an adjusted value for the current remobilization coefficient value (ranging from 0 to 1.5). The regrowth coefficient function represents remobilization started at the maximum remobilization rate (1.5) from the beginning of each regrowth cycle (0 ˚Cd). This remained constant until 300 ˚Cd for FD5 (250 ˚Cd for FD2 and 500 ˚Cd for FD10), and then declined to 0 at 350 ˚Cd for FD5 (300 ˚Cd for FD2 and 550 ˚Cd for FD10). In a decreasing Pp, the increasing root biomass was caused by carbon partitioning. Thus, the model was parameterized to have a maximal root demand with no remobilization. A constant root maintenance respiration coefficient (Rm_root_day) of 0.0005 g g-1.day-1 was applied to model root storage maintenance loss. The model had good prediction on shoot biomass and fair prediction on root biomass for 42 and 84 day defoliation treatments. However, the model did not accurately predict root biomass under a 28 day frequent defoliation (SS) probably due to a limitation of root N reserves. The N module was linked with DM in the PMF. The N supply was estimated as 2.5% of total biomass, whereas N demand was built as N threshold functions for each organ. Root N showed a similar seasonal pattern as root biomass. A root N remobilization coefficient value (% storage root N per day; 2 for FD5, 0.5 for FD2 and FD10) was used for remobilization calculations in an increasing Pp. Applying the N module improved biomass prediction, especially for the 28 day defoliation treatment (SS). Simulation results showed good agreement for predicting phenological development stages (NSE of 0.77 for buds visible and 0.67 for flowering stage), good agreement for canopy expansion (overall NSE = 0.61), good agreement for shoot and root biomass (NSE of 0.68 and 0.53). However, there was fair to poor agreement for leaf N (NSE of 0.16 to -0.14), stem N (NSE of 0.51 to -4.61) and root N (NSE of 0.16 to 0.29) for all three FD classes under different defoliation regimes. This was because leaf biomass was used to parameterize leaf N thresholds which resulted in systemic bias. There was a lack of measured N concentration data for the model testing for most treatments. Thus, additional measurement and a more effective approach for parameterizing N demand are required to improve the model. Overall, these results indicate that the APSIM NextGen lucerne model was successfully created to predict growth and development of crops grown under unlimited environmental conditions. Model validation is required under different climate conditions.

Transboundary animal diseases (TADs) such as classical swine fever (CSF) are highly infectious animal diseases that have the potential to easily spread across international borders. TADs present a potential threat to food security and public health, often with severe economic consequences. CSF is a World Organisation for Animal Health notifiable disease that was eradicated from the U.S. in 1978 and Canada in 1963. Re-introduction could have a substantial economic impact and long-lasting repercussions on trade. Therefore, regulatory cooperation of TAD control between the United States Department of Agriculture and the Canadian Food Inspection Agency is one area of focus being explored to facilitate trade and economic growth, while mitigating the economic, public health, or social impacts of potential outbreaks. However, emergency response planning for a TAD outbreak in U.S. and Canadian swine populations has become challenging due to significant changes in the size, structure and connectivity of the swine industry in the last half-century. Epidemic simulation models are one type of tool that can be used to help understand the scope and impact of potential TAD outbreaks as well as evaluate the effectiveness of disease control strategies. This thesis outlines the incremental development of a spatially-explicit, stochastic transboundary epidemic simulation model of CSF between U.S. and Canadian swine farms to examine the epidemiological impact of international trade restrictions following CSF detection and evaluate the use of mutually recognized zones for disease control. Individual animal and herd-level disease parameters (latent, subclinical, incubation and clinical periods) for highly and moderately virulent CSF are developed in Chapter 2. A transboundary model of CSF is developed in Chapter 3 to evaluate the scope and scale of potential CSF outbreaks while varying the index farm location (Iowa, North Carolina, Ontario or Quebec), production type (farrow-to-wean or nursery operation) and CSF virulence level (high or moderate virulence CSF). The transboundary model is used in Chapter 4 to evaluate epidemiologic outcomes of different control policies for swine movements at the U.S. and Canadian border.

This thesis evaluates the potential recovery of the runway throughput under Wake Turbulence Re-categorization (RECAT) Phase II and Time-based Separation (TBS) with a Runway Occupancy Time (ROT) constraint comparing with RECAT Phase I. This research uses aircraft performance parameters (runway occupancy time, approach speed, etc.) from the Airport Surface Detection Equipment, Model X (ASDE-X) data set. The analysis uses a modified version of the Quick Response Runway Capacity Model (RUNSIM). The main contributions of the study are: 1) identifying the technical buffer between in-trail arrivals and regenerate them in RUNSIM; 2) estimate the percentage of the arrival pairs that have wake mitigation separation times in excess of ROT; 3) developed an additional in-trail arrival separation rule based on ROT; 4) measure the risk of potential go-arounds with and without the additional 95 ROT separation rules. 5) generate a sample equivalent time-based RECAT II. The study results show that the distributions of technical buffers have significant differences for different in-trail groups and strong connectivity to airport elevations. This is critical to estimate runway capacities and safety issues especially when advanced wake mitigation separation rules are applied. Also, with decreasing of wake separations, ROT will become a limiting factor in runway throughput in the future. This study shows that by considering a 95 percentile ROT constrain, one single runway can still obtain 4 or 5 more arrivals per hour under RECAT II but keep the same level of potential go-arounds compared with current operation rules (RECAT I). TBS rules seem to benefit more under strong wind conditions compared to RECAT I, and RECAT II. TBS rules need to be tailored to every airport.

Abstract: Although previous research has emphasized the use of safety-related measures to assess and control safety performance, many construction companies continue to rely on reactive indicators for safety control. The reluctance of industry to use safety-related measures for the proactive evaluation of safety performance is a consequence of the unique characteristics of construction projects, which renders the identification and evaluation of safety-related measures difficult in practice. From a theoretical perspective, models developed to proactively assess the safety performance have difficult considering (1) the specific characteristics of an organization due to the limited amount of data points provided and (2) the dynamic nature of construction sites, which can affect measure performance. The objective of this research was to develop a framework that could proactively assess safety performance using safety-related measures. A framework, which combines Case-Based Reasoning (CBR) with simulation modelling, was proposed for this purpose. CBR was chosen as an assessment method for its ability to assess safety output under conditions of limited data, while simulation was considered for its ability to reliably reproduce project behavior. Prior to framework implementation, challenges for understanding current Safety Managements System (SMS) practices and identifying safety-related measures were researched. Then, a risk-rating approach designed to investigate the complex relationship between SMS factors and accident precursors from a holistic perspective was developed. This approach allows for not only the identification of high-priority SMS factors and accident precursors but also for the evaluation of associations between them, and a many-to-many relationship was found to exist between these groups. A case-study was then conducted to analyze and review the effective use of departmental data to control and assess safety performance. Several safety-related measures collected by several departments were found to be useful for proactively controlling safety performance. Altogether, the findings presented here strongly support the use of a holistic approach for the evaluation and control of safety performance The framework was developed and applied in an industrial construction organization. A total of 27 safety-related measures were identified from data collected from departmental databases, documents, and interviews. After reducing the dimensionality of the database through the application of statistical tests and Correlation Feature Based Selection Algorithm, the final approach considered nine measures. From a practical perspective, the model was found to reliably predict trends in safety performance, and can be used to predict how specific decisions made in practice can affect safety performance. This research has resulted in (1) the development of a CBR approach that can be used to assess the safety performance of construction projects characterized by few data points (i.e., few incidents) while allowing for the consideration of an organization’s unique conditions and (2) the integration of CBR with a simulation model, which allows managers to more easily predict how decisions, both individually and in combination, influence overall safety performance. Furthermore, the results of this research support the use of (3) a holistic approach for the establishment and evaluation of proactive SMS mitigation strategies and (4) the collection and consideration of various departmental data to more reliably evaluate proactive safety performance.

This thesis discusses enhancements to the Global Demand Model (GDM). The model addresses the need to predict: a) number of flights Worldwide by Origin-Destination (OD) airport pair, b) the number of seats (surrogate of demand) by OD airport pair, c) the fleet evolution over time, d) fuel consumption by OD pair and aircraft type, and emissions by OD pair and aircraft type. The model has developed an airline fleet assignment module to predict changes to the airline fleet in the future. Specifically, the model has the capability to examine the fuel and emission benefits of next generation N+1 aircraft and advanced NASA's N+2 aircraft are adopted in the future.

This thesis presents a performance assessment of flight operations in the North Atlantic Organized Track System (OTS) using the Global Oceanic Model. The main contributions of the study are: a) improvements to the logic of the Global Oceanic Model; b) prediction of benefits among various aircraft separation minima and operational policies to assign flights to tracks in the OTS system; and c) forecast of OTS traffic over North Atlantic from 2020 to 2040. The preliminary results show that a concept of operation with longitudinal separation minima of 15 nm and information of the flight cost matrix provides average fuel savings of 93 kilograms per flight using 2020 traffic. The fuel savings increase to 170 kilograms per flight using traffic levels expected in the year 2040. A new operational track assignment routine is developed and it could save around 40 kilograms per flight compared with the current concept of operations. The study results show a shortage of capacity of the Organized Track System in the future. The analysis shows that the OTS configuration used today and in 2020 is unable to accommodate the traffic projected in 2040. The analysis concludes that more tracks will be needed to maintain an acceptable level of service.

The main purpose of this thesis is to design a global supply chain network for a pharmaceutical company located in Puerto Rico, which manufactures blood sugar strips products.As a design aspect of supply chain, layered cellular manufacturing systems consist of dedicated, shared and remainder cells are considered. One of the main differences between classical cellular manufacturing systems and layered cellular manufacturing systems is, in layered cellular design, some cells may needed to be utilized by various parts of product families. Depending on the required demand and similarity in essential processes or manufacturing characteristics for each product family, products are grouped together and form a product family. If the product family assigned to one cell and just one product family utilizes that cell, the cell is a dedicated cell. Shared cells and remainder cells are employed by, two families and three or more families, respectively. In the first part of this study, a new heuristic layered-cellular manufacturing design approach is proposed and later in the second part, two mathematical models are proposed. The first one is with the objective of minimizing number of cells and cost of opening cells, and the second one is maximizing Net Present Value considering budget limitations for the whole manufacturing system. In the first step, the required number of cells are determined for a product family to meet an acceptable demand coverage (MADC) percentage. It is assumed that customer demand follows normal distribution with the established parameters mean (µ) and standard deviation (s). An attempt is made to increase the utilization of each cell by combining multiple families (thus creating shared and remainder cells) to increase the utilization of each cell as long as it is economically acceptable. As demand coverage increases, revenue also increases. However, this also increases operational costs. The expected profit is calculated based on the expected cell utilization. Cost analysis approach proposed in this study will allow us to modify cells or open new cells. The results of this heuristic approach and mathematical model are compared. Finally, simulation is employed to verify our proposed layered-cellular system based on evaluating two measurable performance factors: average flow time and work in process inventory.

Abstract: In the current construction industry, simulation is an effective technology that can assist engineers’ decision making on project planning and estimation. The key contribution of simulation is to model uncertainty and risk occurrence during the construction process; however, this method still lacks a quantitative method to determine how much risk the decision maker is willing to accept. This thesis aims to develop an approach that can assist decision making on what percentile from a cumulative distribution function (CDF) of project cost reflects the organization’s risk appetite and overall acceptance of risk. In order to enhance this quantitative risk analysis, a special purpose template was re-developed in Simphony.Net, based on program evaluation and review technique (PERT). This template provides an integrated cost/schedule model, and transforms the model to simulation-based planning, in which the uncertainties of project cost and schedule are evaluated through computer simulation.

This thesis presents a study of disturbance management in production-inventory and supply chain systems. The study focuses on generating and analysing the recovery decision after the occurrence of a disturbance on a real-time basis. In this thesis, the developed approach was divided into several steps. At first, a plan was developed without considering any disturbance. Then a mathematical model was formulated to obtain a revised plan after the occurrence of a disturbance in the system. An efficient heuristic approach was proposed for solving the mathematical model in order to obtain the recovery plan. The mathematical model and heuristic approach were also extended to consider multiple disturbances, one after another as a series, on a real-time basis. Finally, the experimentation was conducted and the heuristic results were compared with other standard solution approaches to judge and validate the results. The framework was applied for managing production disruption: (1) in a single-stage imperfect production-inventory system, (2) in a two-stage production-inventory system, (3) in a three-stage mixed production-inventory system, and (4) in a supply chain network with multiple entities in each stage. The framework was also applied to two other models: (1) for managing demand fluctuation in a supplier-retailer coordinated system, and (2) for managing supply disruption in a three-tier supply chain system. In this thesis, three different types of disturbances were explored, namely (1) production disruption, (2) raw material supply disruption, and (3) demand fluctuation. In real-life situations, multiple disturbances, as a series, can happen at any time and at any stage of the system. This thesis considered multiple disturbances, one after another in a series, that may or may not affect the plans revised after previous disturbances. If a new disturbance occurs during the recovery time window of another, a new revised plan which considers the effects of both disturbances must be derived. Accordingly, as this is a continuous process, an extended mathematical model and heuristic approach was developed to deal with a series of disturbances on a real-time basis, by incorporating a modified version of those developed for a single disturbance. The results of the experimental analysis showed that the optimal recovery plan is highly dependent on the shortage cost parameters such as, back orders and lost sales costs, and to the disturbance duration. For a certain range of disturbance duration and cost values, it was found that back orders were more attractive, and in such cases, back orders cost was less than the lost sales cost. On the other hand, when back orders cost were more than the lost sales cost, the solution had lost sales in their recovery plan. In the final work of the thesis, a simulation model was developed to analyse the effects of different types of randomly generated disturbance events that were not known in advance. The simulation model considered all three types of disturbances, namely (1) production disruption, (2) raw material supply disruption, and (3) demand fluctuation. A good number of random experiments were conducted to judge the simulation model, and to make the simulation model closer to real-world processes. The developed approaches were tested by solving a significant number of randomly generated test problems. The sensitivity analysis was carried out for the model parameters. Two of the models, developed in two-stage and three-stage production-inventory systems, were also tested using real-life cases from a pharmaceutical company. It was found that the developed approaches were more beneficial than the company’s existing practice.

Recent emerging gallium nitride (GaN) high electron mobility transistor (HEMT) is expected to be a promising candidate for high frequency power conversion techniques. Due to the advantages of the material, the GaN HEMT has a better figure of merit (FOM) compared to the state-of-the-art silicon (Si) power metal oxide silicon field effect transistor (MOSFET), which allows the GaN HEMT to switch with faster transition and lower switching loss. By applying the GaN HEMT in a circuit design, it is possible to achieve high frequency, high efficiency, and high density power conversion at the same time. To characterize the switching performance of the GaN HEMT, an accurate behavior-level simulation model is developed in this thesis. The packaging related parasitic inductance, including both self-inductance and mutual-inductance, are extracted based on finite element analysis (FEA) methods. Then the accuracy of the simulation model is verified by a double-pulse tester, and the simulation results match well with experiment in terms of both device switching waveform and switching energy. Based on the simulation model, detailed loss breakdown and loss mechanism analysis are made. The cascode GaN HEMT has high turn-on loss due to the body diode reverse recovery of the low voltage Si MOSFET and the common source inductance (CSI) of the package; while the turn-off loss is extremely small attributing to the cascode structure. With this unique feature, the critical conduction mode (CRM) soft switching technique are applied to reduce the dominant turn on loss and increase converter efficiency significantly. The switching frequency is successfully pushed to 5MHz while maintaining high efficiency and good thermal performance. Traditional packaging method is becoming a bottle neck to fully utilize the advantages of GaN HEMT. So an investigation of the package influence on the cascode GaN HEMT is also conducted. Several critical parasitic inductors are identified, which cause high turn on loss and high parasitic ringing which may lead to device failure. To solve the issue, the stack-die package is proposed to eliminate all critical parasitic inductors, and as a result, reducing turn on loss by half and avoiding potential failure mode of the cascode GaN device effectively. Utilizing the proposed stack-die package and ZVS soft switching, the GaN HEMT high frequency, high efficiency, and high density power conversion capability can be further extended to a higher level.

Emerging memory technologies are being intensively investigated for extending Moore\'s law in the next decade. The conductive bridge random access memory (CBRAM) is one of the most promising candidates. CBRAM shows unique nanoionics-based filamentary switching mechanism. Compared to flash memory, the advantages of CBRAM include excellent scalability, low power consumption, high OFF-/ON-state resistance ratio, good endurance, and long retention. Besides the nonvolatile memory applications, resistive switching devices implement the function of memristor which is the fourth basic electrical component. This research presents the characterization and modeling of Cu/TaOx/Pt resistive switching devices. Both Cu and oxygen vacancy nanofilaments can conduct current according to the polarity of bias voltage. The volatile resistive switching phenomenon has been observed on Cu/TaOx/delta-Cu/Pt devices and explained by a flux balancing model. The resistive devices are also connected in series and in anti-parallel manner. These circuit elements are tested for chaotic neural circuit. The quantum conduction has been observed in the I-V characteristics of devices, evidencing the metallic contact between the nanofilament and electrodes. The model of filament radial growth has been developed to explain the transient I-V relation and multilevel switching in the metallic contact regime. The electroforming/SET and RESET processes have been simulated according to the mechanism of conductive filament formation and rupture and validated by experimental results. The Joule and Thomson heating effects have also been investigated for the RESET processes.

Motor vehicle crashes were estimated to be the eleventh leading cause of death in United States in 2009. Using a speech interface to operate infotainment systems while driving can potentially reduce driver distraction. Unfortunately, evaluations of driver interfaces are often too late to make changes. An alternative approach is to model driver task performance when using speech interfaces and to use the model to predict system performance early in design when changes are easier to make. The purposes of this research are to understand how drivers interact with speech interfaces and based on that knowledge, develop and validate a simulation model of how drivers interact with speech interfaces to aid speech-interface development. To develop the simulation model, a survey and a driving simulator experiment were conducted to identify how these tasks are carried out and the values for the process parameters. First, using a survey, frequency data for tasks and methods, and the content in user-generated databases were collected to assure that real tasks and constraints are considered in the simulation model. Next, a driving simulator experiment was conducted to understand how drivers perform destination entry and music selection and to determine the time drivers need to construct utterances, the errors drivers make, and the probability of correction strategies are used for each type of error. Half of these data were used to create the simulation model structure and provide the model parameters for entering destinations and selecting music using speech. Finally, the simulation model was validated for these two tasks using the second half of the data from the previous experiment. This research provides a model to predict user task performance with speech interfaces in motor vehicles. Use of this model supports the design of safer and easier to use speech interfaces in vehicles that can minimize eyes-off-road time and should reduce crash risk, and thereby protect public health. This model can be exercised to examine alternative speech interface configurations months before a physical interfaces is available for user testing when changes are easier to make, which saves time, reduces cost, and improves the quality of the interface produced.

A new strategy to fight mosquito-borne disease is based on infections of the maternally-transmitted, intracellular bacterium Wolbachia pipientis. Estimates predict that Wolbachia infects nearly half of all insect species, as well as other arthropods and some nematodes. Wolbachia manipulates the reproduction of its host to promote infection, most commonly causing a form of conditional sterility known as cytoplasmic incompatibility. Generally, Wolbachia infections are benign and do not inflict significant costs upon its host. However, studies demonstrate that some infections are associated with substantial costs to its host. These same infections can also induce pathogen interference and decrease vector competency of important disease vectors. Theory predicts that organisms that incur costs relative to conspecifics are less competitive and their competitive exclusion is expected. In the case of Wolbachia, the bacterium can influence reproduction such that phenotypes with lower fitness may still reach fixation in natural populations. In this dissertation, I describe theoretical and empirical experiments that aim to understand the invasion and stability of Wolbachia infections that impose costs on their host. Particular attention is paid to immature insect lifestages, which have been previously marginalized. These results are discussed in relation to ongoing vector control strategies that would use Wolbachia to manipulate vector populations. Specifically, I discuss the cost of novel Wolbachia infections in Aedespolynesiensis, which decreases larval survival and overall fitness relative to wild-type mosquitoes. Then, a theoretical framework was developed to determine the significance of reductions in larval viability in relation to the population replacement disease control strategy. Further theoretical studies determined that Wolbachia infections, once established, resist re-invasion by uninfected individuals despite relatively high costs associated with infection so long as the infection produces reproductive manipulations. Additional studies determined that larvae hatched from old eggs experience reduced survival in mosquito strains with novel Wolbachia infections when compared to the wild-type. To validate the theoretical studies, model predictions were tested empirically to determine the importance of the larval viability. Finally, a COPAS PLUS machine was evaluated and its role in understanding early larval development in mosquitoes is discussed. The importance of integrated research in disease control is highlighted.

Computer simulation tools for education and research are making increasingly effective use of the internet. To facilitate these activities in neuroendocrinology, we implemented a sophisticated and well-validated simulator of the dynamics of human thyroid hormone and thyrotropin regulation as a web-based application. A major goal was to accommodate access to this tool from virtually anywhere, on modern handheld devices as well as desktop computers and laptops. Development was done using well-established open source software components that together provide a simple and intuitive user interface platform for doing simulated "what-if" experiments. User-selectable "experimental" test-input stimuli (oral, IV-pulse, IV-infusion inputs) are represented by animated graphical icons. Familiar graphs of simulated time-dependent T3, T4 and TSH dynamic responses are provided on the same interface. Various disease states, e.g. hypo- and hyperthyroidism and malabsorption, are simulated by optionally changing thyroid gland secretion rates and gut absorption rates. To facilitate comparative analysis, two sets of simulated experimental results can be superimposed on the same graph.

Decision support systems for university management have had limited improvement in the incorporation of new cutting-edge techniques. Most decisionmakers use traditional forecasting methods to base their decisions in order to maintain financially affordable programs and keep universities competitive for the last few decades. Strategic planning for universities has always been related to enrollment revenues, and operational expenses. Enrollment models in use today are able to represent forecasting based on historical data, considering usual variables like student headcount, student credit, among others. No consideration is given to students’ preferences. Retention models, associated to enrollment, deal with average retention times leaving off preferences as well. Preferences play a major role at institutions where students are not required to declare their intentions (major) immediately. Even if they do, they may change it if they find another, more attractive major, or they may even decide to leave college for external reasons. Enrollment models have been identified to deal with three main purposes: prediction of income from tuition (in-state, out-of-state), planning of future courses and curriculum, and allocation of resources to academic departments, This general perspective does not provide useful information to faculty and Departments for iv detailed planning and allocation of resources for the next term or year. There is a need of new metrics to help faculty and Departments to reach a detailed and useful level in order to effectively plan this allocation of resources. The dynamics in the rate-of-growth, the preferences students have for certain majors at a specific point of time, or economic hardship make a difference when decisions have to be made for budgets requests, hiring of faculty, classroom assignment, parking, transportation, or even building new facilities. Existing models do not make difference between these variables. This simulation model is a hybrid model that considers the use of System Dynamics, Discrete-event and Agent-based simulation, which allows the representation of the general enrollment process at the University level (strategic decisions), and enrollment, retention and major selection at the College (tactical decisions) and Department level (operational decisions). This approach allows lower level to more accurately predict the number of students retained for next term or year, while allowing upper levels to decide on new students to admit (first time in college and transfers) and results in recommendations on faculty hiring, class or labs assignment, and resource allocation. This model merges both high and low levels of student’s enrollment models into one application, allowing not only representation of the current overall enrollment, but also prediction at the College and Department level. This provides information on optimal classroom assignments, faculty and student resource allocation.

State governments always want graduates who received higher education in universities and colleges to stimulate the economic growth of the state. Many state universities want to contribute more to the state’s economy as well, and state universities frequently have relevant economic development activities and offices. The state universities normally receive annual budgeted financial support and occasional special funding from the state government while the state expects contributions to the economic growth and an educated citizen in return. In this research, the economic impact of a graduate degree funded by the state government was considered in the state of Nebraska. Simulation models were utilized to attain the goal of the research. In the simulation models, the salaries of graduate engineers and the money spent by employed graduate engineers were considered, and the taxes paid by the graduate employees were used as the basic indicator to assess the direct economic impacts received by the state government. The computer simulation model was validated and verified. The simulation model utilized historical spending data patterns and calculated the Nebraska state and federal income taxes directly paid by graduates and indirectly paid as a result a of their spending, and it was economically justified to support the advanced education. The state government can regain their financial support of the university via state taxes paid by the graduates alone. Using spending simulation, it was determined that it was not beneficial for the government to reduce the income tax rates in hope of generating secondary spending to recover the tax losses by a stimulation of spending. Even if the sales taxes obtained by the government were increased, the amount cannot make up for the decreases of the income taxes. Advisor: Michael W. Riley

Recent U.S. legislation applies a precautionary approach to setting catch regulations in federal fisheries management. A transparent approach to complying with federal guidelines involves calculating the catch recommendation that corresponds to a specified probability, P*, of exceeding the "true" overfishing limit (OFL) located within an estimated distribution. The P* methodology aims to manage the risk of overfishing explicitly, but choice of P* alone does not provide sufficient information on all of the risks associated with a control rule—both the probability of overfishing and the severity of overfishing. Rather, the ramifications of P* choices depend on the amount of uncertainty in the stock assessment and on the life history of the species in question. To evaluate these effects on the risks associated with P* rules, my study simulated fishing three example species under three levels of uncertainty. Trends identified among example species were consistent with predictions from life history. Periodic strategists, which have highly variable recruitment, experienced probabilities of overfishing which exceeded P* and which increased in time. Equilibrium strategists showed more predictable risks of overfishing but may have less capacity to recover from depleted biomass levels. Differences in the size of the OFL distribution—representing differences in levels of uncertainty—led to mixed results depending on whether the distribution was biased or whether uncertainty was fully characterized. Lastly, because OFL distributions are themselves estimates and subject to uncertainty in their shape and size, lower P* values closer to the tails of the estimated distribution produced more variable resulting risks.

Construction sites are among the most common areas to experience soil erosion and sediment transport due to the mandatory foundation tasks such as excavation and land grubbing. Thus, temporary sediment barriers are installed along the perimeter to prevent sediment transport from the site. Erosion and sediment transport control measures may include, but not limited to, physical and chemical processes such as the use of a silt fence and polyacrylamide product. Runoff from construction sites and other impervious surfaces are routinely discharged into ponds for treatment before being released into a receiving water body. Stormwater harvesting from a pond for irrigation of adjacent lands is promoted as one approach to reducing pond discharge while supplementing valuable potable water used for irrigation. The reduction of pond discharge reduces the mass of pollutants in the discharge. In the dissertation, presented is the investigation of the effectiveness of temporary sediment barriers and then, development of a modeling approach to a stormwater harvesting pond to provide a comprehensive stormwater management pollution reduction assessment tool. The first part of the research presents the investigation of the performance efficiencies of silt fence fabrics in turbidity and sediment concentration removal, and the determination of flowthrough-rate on simulated construction sites in real time. Two silt fence fabrics, (1) woven and the other (2) nonwoven were subjected to material index property tests and a series of field-scale tests with different rainfall intensities and events for different embankment slopes on a tilting test-bed. Collected influent and effluent samples were analyzed for sediment concentration and turbidity, and the flow-through-rate for each fabric was evaluated. Test results revealed that the woven and nonwoven silt fence achieved 11 and 56 percent average turbidity reduction iv efficiency, respectively. Each fabric also achieved 20 and 56 percent average sediment concentration removal efficiency, respectively. Fabric flow-through-rates were functions of the rainfall intensity and embankment slope. The nonwoven fabric exhibited higher flow-throughrates than the woven fabric in both field-scale and laboratory tests. In the second part of the study, a Stormwater Harvesting and Assessment for Reduction of Pollution (SHARP) model was developed to predict operation of wet pond used for stormwater harvesting. The model integrates the interaction of surface water and groundwater in a catchment area. The SHARP model was calibrated and validated with actual pond water elevation data from a stormwater pond at Miramar Lakes, Miramar, Florida. Model evaluation showed adequate prediction of pond water elevation with root mean square error between 0.07 and 0.12 m; mean absolute error was between 0.018 and 0.07 m; and relative index of agreement was between 0.74 and 0.98 for both calibration and validation periods. The SHARP model is capable of assessing harvesting safe-yield and discharge from a pond, including the prediction of the percentage of runoff into a harvesting pond that is not discharged. The combination of silt fence and/or polyacrylamide PAM before stormwater harvesting pond in a treatment train for the reduction of pollutants from construction sites has the potential of significantly exceeding a performance standard of 85 percent reduction typically required by local authorities. In fact, the stringent requirement of equaling pre- and post-development pollutant loading is highly achievable by the treatment train approach. The significant contribution from the integration of the SHARP model to the treatment train is that real-time assessment of pollutant loading reduction by volume can be planned and controlled to achieve target performance standards.

The U.S. pelagic longline fishery catches 98% of domestic swordfish landings but is also one of the three fisheries most affecting federally protected sea turtles (Crowder and Myers 2001, Witherington et al 2009). Bycatch by fisheries is considered the main anthropogenic threat to sea turtles (NRC 1990). Accurate and precise bycatch estimates are imperative for sea turtle conservation and appropriate fishery management. However, estimation is complicated by only 8% observer coverage of fishing and data that are hierarchical in structure (i.e., multiple sets per trip), zero-heavy (i.e., bycatch is rare), and often overdispersed (i.e., larger variance than expected). Therefore, I evaluated two predominant bycatch estimation methods, the delta-lognormal method and generalized linear models, and investigated improvements in uncertainty incorporation. I constructed a simulation model to evaluate bycatch estimation at two spatial scales under ten spatial models of sea turtle, fishing set, and observer distributions. Results indicated that distributing observers relative to fishing effort and using the delta-lognormal-strata method was most appropriate. The delta-lognormal-strata 95% confidence interval (CI) was wider than statistically appropriate. The delta-lognormal-all sets pooled 95% CI was narrower but simulated bycatch was above the CI too frequently. Thus, I developed a bycatch estimate risk distribution to incorporate uncertainty in bycatch estimates. It gives managers access to the entire distribution of bycatch estimates and their choice of any risk level. Results support the management agency's observer distribution and estimation method but suggest a new procedure to incorporate uncertainty. This study is also informative for many similar datasets.

The catchability coefficient is used in most marine stock assessment models, and is usually assumed to be stationary and density-independent. However, recent research has shown that these assumptions are violated in most fisheries. Violation of these assumptions will cause underestimation of stock declines or recoveries, leading to inappropriate management policies. This project assesses the soundness of stationarity and density independence assumptions using multi-species data for seven stocks and four gears in the U.S. Gulf of Mexico. This study also develops a multi-species methodology to compensate for failures of either assumption. To evaluate catchability assumptions, abundance-at-age was reconstructed and compared with catch-per-unit-effort data in the Gulf. Mixed-effects, Monte Carlo, and bootstrap analyses were applied to estimate time-varying catchability parameters. Gulf data showed large and significant density dependence (0.71, s.e. 0.07, p

The productivity of the printing industry in terms of real sales per employee has been growing at an annualized rate of 1.9% for more than a decade. The National Association for Printing Leadership (NAPL) reports that the industry is lagging, when compared to the average productivity growth of 4% from the non-durable manufacturing industries (2004). Many possible causes and reasons for the slow improvement are presented, but the final focus is on the inefficiencies of the print production system when analyzed as a whole. The technology within the printing industry has improved dramatically; however, its implementation usually delivers localized improvements with marginal effects on the whole system. Printers are focused too much on the productivity of specific equipment and not enough on the overall throughput of the system. A computer simulation model of a generic print production workflow using system dynamics was developed to address this issue. The use of simple tools known as stocks and flows, in conjunction with information feedbacks, resulted in a complete representation of the complexity of the system. Through multiple iterations and interaction with the model, opportunities for productivity improvement of individual print companies can be identified. Additionally, the model acts as a learning tool for testing mental models and for improving the understanding of the print production system. The model was used to simulate five scenarios: status quo, aggressive sales, press productivity improvement, shrinking order size, and synergy. The synergy scenario achieved the best overall results xii when compared to the status quo scenario. Under this scenario, the throughput of the whole system increased by 47% during the simulation, considering fixed capital and labor resources. Therefore, through correct policy design and implementation, real productivity gains of the whole system can be achieved.

The Conservation Security Program was authorized in the 2002 Farm Bill and is hailed by many observers as the first true ?green payments? program for working lands in the United States. Previous analysis and anecdotal evidence for similar conservation programs show that premature termination of contracts is a persistent issue. This type of producer behavior is not easily explained using the standard assumptions of profit-maximization under perfect information and perfect rationality. Rather, this unexpected termination behavior demonstrates the need for analyses that take into account biophysical complexities and alternative decision-making assumptions. An alternative set of behavioral assumptions is explored in this research that employ descriptive rather than normative participation decision rules. The objective of this research is to explore the impact of alternative behavioral assumptions and dynamic biophysical effects of conservation practices on the participation and termination decisions of New York dairy producers. A simulation model is constructed to represent the effects of selected biophysical processes on farm profitability, producers? ability to gather and utilize this information, and finally their decisions to participate in the program. The results of this simulation model provide insights concerning observed termination behavior and suggestions for policy design and implementation. The results of the dynamic participation decision model indicate that premature termination of CSP contracts is possible and even probable under certain conditions. These conditions include the complex biophysical effects of conservation practices, behavioral characteristics of decision makers, and payment schedules of the Conservation Security Program. These are all significant factors affecting if and when producers decide to termination CSP contracts. Termination decisions, in this model, are a result of learning processes, that is, the producer?s realization of new information concerning the profitability, or net revenue from participating in the Conservation Security Program. Behavioral characteristics play a significant role in shaping producers? learning processes and the formation of expectations of net revenue from the CSP contract. Most important for determining participation is the magnitude of the producer?s initial estimation error as well as delays in updating their perceptions and expectations of net revenue. These delays determine a producer?s adjustment times for perceiving new information and the incorporation of that learning into new expectations about net revenue. This expectation formation process can further encourage farmers to terminate their CSP contracts by creating greater amounts of volatility in expectations. The type of decision rule used by the farmer is also of significance when determining if or when a farmer might terminate participation in CSP. This analysis examines ten alternative decision rules. This analysis presents several implications for pragmatic policy solutions to the problem of premature termination of CSP contracts. Foremost, policy makers should look beyond typical cost/benefit models of decision-making when designing inventive structures for conservation programs. Taking into account alternative decision-making behavior, this research recommends an alternative payment schedule to what was implemented in the 2005 CSP sign-up. A more robust policy would be to vary the payment rates to compensate for the expected downturn in farmer perceptions and expectations. Evaluation of several dynamic payment schedules indicated that there are a variety of possible schedules that can increase initial sign-up rates, decrease termination rates, and decrease government expenditures simultaneously. The policy ?fix,? suggested by this analysis, is robust over variations in behavioral characteristics and for multiple decision rules. With this approach, it is possible to simultaneously decrease government expenditure and decrease cumulative termination rates.

All Optical WDM Networks are fast becoming the natural choice for future backbones and in order to meet the exponentially increasing traffic demands, it would be beneficial to support all optical multicast. One way to support multicast is to provide optical splitters at various switching nodes along the network. The main contribution of this thesis is in demonstrating that all optical multicast can be made practical for both 1:2 splitters and 1:N splitters through the proper incorporation of in-line EDFA's and other optical hardware components available off the shelf. Using electronics for 3-R regeneration at the intermediate nodes is costly and hence our model uses EDFA's. Most previous work in this direction has addressed multicast feasibility from an architectural standpoint while this thesis discusses issues from a physical designer's perspective. An All Optical CAD simulation tool from Virtual Photonics was used to simulate a variety of multicast networks taking into account relevant Nonlinear effects such as chromatic dispersion, four wave mixing, stimulated Raman scattering and all phenomena commonly encountered in Cascaded EDFA chains such as Accumulated Spontaneous emission noise, SNR Transients and Gain Saturation.

An Order Accumulation and Sortation (OA/S) system is one of three component systems of a typical distribution center. The three systems in order of material flow are (1) the storage (warehouse) system, (2) the OA/S system, and (3) the shipping system. An efficient distribution center requires a successful link of all three systems. In this study efforts are concentrated on one of the least researched, the OA/S system. In an OA/S system items are delivered to a conveyor where they are sorted into shipping lanes, consolidated and then packed. A simulation model of an OA/S used in automated distribution centers is developed. The OA/S system developed for this study uses non-recirculating conveyors; thus, once an item is introduced into the system, it has to be assigned to a shipping lane. The main concern of the study is to find which factors affect the throughput of the system and at what levels these factors give the best system throughput. Design of experiments (DOE) is a very effective and organized way to perform an experiment and has been commonly used in industry to improve the performance of manufacturing processes. The application of experimental design techniques early in process development can result in improved process yields, reduced variability, closer conformance to nominal or target requirements, reduced development time and reduced overall cost. Emphasis is given to designing quality into the process at the initial stages of research by making it robust (insensitive) to various sources of variability, which may be uncontrollable during the process or during its usage by the customer. A recently introduced approach based on response surface methodology (RSM) showed that the results of an RSM study are easier to interpret and are very efficient with respect to the number of experiments. A RSM tool, the Central Composite Design (CCD) is one of the most effective designs that can be used for robust parameter design. The CCD is basically a two-level factorial or fractional factorial design that has a number of center point and axial point runs. This design is used to analyze the results of the simulation runs and to achieve robustness. The final equation to calculate the throughput rate considering all the independent factors was calculated using design of experiments. It is concluded that the independent factors Lane Length, Items / Lane and Distribution do not affect the throughput rate of the system. Though the noise factors are insignificant in the phase two of experimentation, it is suggested to have longer lane lengths (control factor A) as seen in the screening experiment. The screening experiment shows that the lane length is insignificant. Thus longer the lanes, lesser the variability in the throughput rate is obtained.

The fate of the carbon and nitrogen in dairy farm effluent (DFE) applied onto soil was investigated through laboratory experiments and field lysimeter studies. They resulted in the development and testing of a complex carbon (C) and nitrogen (N) simulation model (CaNS-Eff) of the soil-plant-microbial system. To minimise the risk of contamination of surface waters, regulatory authorities in New Zealand promote irrigation onto land as the preferred treatment method for DFE. The allowable annual loading rates for DFE, as defined in statutory regional plans are based on annual N balance calculations, comparing N inputs to outputs from the farming system. Little information is available, however, to assess the effects that these loading rates have on the receiving environment. It is this need, to understand the fate of land-applied DFE and develop a tool to describe the process, that is addressed in this research. The microbially mediated net N mineralisation from DFE takes a central role in the turnover of DFE, as the total N in DFE is dominated by organic N. In a laboratory experiment, where DFE was applied at the standard farm loading rate of 68 kg N ha⁻¹, the net C mineralisation from the DFE was finished 13 days after application and represented 30% of the applied C, with no net N mineralisation being measured by Day 113. The soluble fraction of DFE appeared to have a microbial availability similar to that of glucose. The low and gradually changing respiration rate measured from DFE indicated a semi-continuous substrate supply to the microbial biomass, reflecting the complex nature and broad range of C compounds in DFE. The repeated application of DFE will gradually enhance the mineralisable fraction of the total soil organic N and in the long term increase net N mineralisation. To address the lack of data on the fate of faecal-N in DFE, a ¹⁵N-labelled faecal component of DFE was applied under two different water treatments onto intact soil cores with pasture growing on them. At the end of 255 days, approximately 2% of the applied faecal ¹⁵N had been leached, 11 % was in plant material, 11 % was still as effluent on the surface, and 40% remained in the soil (39% as organic N). Unmeasured gaseous losses and physical losses from the soil surface of the cores supposedly account for the remaining ¹⁵N (approximately 36%). Separate analysis of the total and ammonium nitrogen contents and ¹⁵N enrichments of the DFE and filtered sub-samples (0.5 mm, 0.2µm) showed that the faecal-N fraction was not labelled homogeneously. Due to this heterogeneity, which was exacerbated by the filtration of DFE on the soil surface, it was difficult to calculate the turnover of the total faecal-N fraction based on ¹⁵N results. By making a simplifying assumption about the enrichment of the ¹⁵N in the DFE that infiltrated the soil, the contribution from DFE-N to all plant available N fractions including soil inorganic N was estimated to have been approximately 11 % of the applied DFE-N. An initial two-year study investigating the feasibility of manipulating soil water conditions through controlled drainage to enhance denitrification from irrigated DFE was extended a further two years for this thesis project. The resulting four-year data set provided the opportunity to evaluate the sustainability of DFE application onto land, an extended data set against which to test the adequacy of CaNS-Eff, and to identify the key processes in the fate of DFE irrigated onto soil under field conditions. In the final year of DFE irrigation, 1554 kg N ha⁻¹ of DFE-N was applied onto the lysimeters, with the main removal mechanism being pasture uptake (700 kg N ha⁻¹ yr⁻¹ removed). An average of 193 kg N ha⁻¹ yr⁻¹ was leached, with 80% of this being organic N. The nitrate leaching decreased with increasing soil moisture conditions through controlled drainage. At the high DFE loading rate used, the total soil C and N, pH and the microbial biomass increased at different rates over the four years. The long-term sustainability of the application of DFE can only be maintained when the supply of inorganic N is matched by the demand of the pasture. The complex simulation model (CaNS-Eff) of the soil-plant-microbial system was developed to describe the transport and transformations of C and N components in effluents applied onto the soil. The model addresses the shortcomings in existing models and simulates the transport, adsorption and filtration of both dissolved and particulate components of an effluent. The soil matrix is divided into mobile and immobile flow domains with convective flow of solutes occurring in the mobile fraction only. Diffusion is considered to occur between the micropore and mesopore domains both between and within a soil layer, allowing dissolved material to move into the immobile zone. To select an appropriate sub-model to simulate the water fluxes within CaNS-Eff, the measured drainage volumes and water table heights from the lysimeters were compared to simulated values over four years. Two different modelling approaches were compared, a simpler water balance model, DRAINMOD, and a solution to Richards' equation, SWIM. Both models provided excellent estimation of the total amount of drainage and water table height. The greatest errors in drainage volume were associated with rain events over the summer and autumn, when antecedent soil conditions were driest. When soil water and interlayer fluxes are required at small time steps such as during infiltration under DFE-irrigation, SWIM's more mechanistic approach offered more flexibility and consequently was the sub-model selected to use within CaNS-Eff. Measured bromide leaching from the lysimeters showed that on average 18% of the bromide from an irrigation event bypassed the soil matrix and was leached in the initial drainage event. This bypass mechanism accounted for the high amount of organic N leached under DFE-irrigation onto these soils and a description of this bypass process needed to be included in CaNS-Eff. Between 80 and 90% of the N and C leached from the lysimeters was particulate (> 0.2 µm in size), demonstrating the need to describe transport of particulate material in CaNS-Eff. The filtration behaviour of four soil horizons was measured by characterising the size of C material in a DFE, applying this DFE onto intact soil cores, and collecting and analyzing the resulting leachate using the same size characterisation. After two water flushes, an average of 34% of the applied DFE-C was leached through the top 0-50 mm soil cores, with a corresponding amount of 27% being leached from the 50-150 mm soil cores. Most of the C leaching occurred during the initial DFE application onto the soil. To simulate the transport and leaching of particulate C, a sub-model was developed and parameterised that describes the movement of the effluent in terms of filtering and trapping the C within a soil horizon and then washing it out with subsequent flow events. The microbial availability of the various organic fractions within the soil system are described in CaNS-Eff by availability spectra of multiple first-order decay functions. The simulation of microbial dynamics is based on actual consumption of available C for three microbial biomass populations: heterotrophs, nitrifiers and denitrifiers. The respiration level of a population is controlled by the amount of C that is available to that population. This respiration rate can vary between low level maintenance requirements, when very little substrate is available, and higher levels when excess substrate is available to an actively growing population. The plant component is described as both above and below-ground fractions of a rye grass-clover pasture. The parameter set used in CaNS-Eff to simulate the fate of DFE irrigated onto the conventionally drained lysimeter treatments over three years with a subsequent 10 months non-irrigation period was derived from own laboratory studies, field measurements, experimental literature data and published model studies. As no systematic calibration exercise was undertaken to optimise these parameters, the parameter set should be considered as "initial best estimates" and not as a calibrated data set on which a full validation of CaNS-Eff could be based. Over the 42 months of simulation, the cumulative drainage from CaNS-Eff for the conventionally drained DFE lysimeter was always within the 95% CI of the measured value. On the basis of individual drainage bulking periods, CaNS-Eff was able to explain 92% of the variation in the measured drainage volumes. On an event basis the accuracy of the simulated water filled pore space (WFPS) was better than that of the drainage volume, with an average of 70% of the simulated WFPS values being within the 95% CI for the soil layers investigated, compared to 44% for the drainage volumes. Overall the hydrological component of CaNS-Eff, which is based on the SWIM model, could be considered as satisfactory for the purposes of predicting the soil water status and drainage volume from the conventionally drained lysimeter treatment for this study. The simulated cumulative nitrate leaching of 4.7 g NO₃-N m⁻² over the 42 months of lysimeter operation was in good agreement to the measured amount of 3.0 (± 2.7) g NO₃-N m⁻². Similarly, the total simulated ammonium leaching of 2.7g NH₄- N m⁻² was very close to the measured amount of 2.5 (± 1.35) g NH₄- N m⁻² , however the dynamics were not as close to the measured values as with the nitrate leaching. The simulated amount of organic N leached was approximately double that measured, and most of the difference originated from the simulated de-adsorption of the dissolved fraction of organic N during the l0-month period after the final DFE irrigation. The 305 g C m⁻² of simulated particulate C leached was close to the measured amount of 224 g C m⁻² over the 31 months of simulation. The dissolved C fraction was substantially over-predicted. There was good agreement in the non-adsorbed and particulate fractions of the leached C and N in DFE. However, the isothermic behaviour of the adsorbed pools indicated that a non-reversible component needed to be introduced or that the dynamics of the de-adsorption needed to be improved. Taking into account that the parameters were not calibrated but only "initial best estimates", the agreement in the dynamics and the absolute amounts between the measured and simulated values of leached C and N demonstrated that CaNS-Eff contains an adequate description of the leaching processes following DFE irrigation onto the soil. The simulated pasture N production was in reasonable agreement with the measured data. The simulated dynamics and amounts of microbial biomass in the topsoil layers were in good agreement with the measured data. This is an important result as the soil microbial biomass is the key transformation station for organic materials. Excepting the topsoil layer, the simulated total C and N dynamics were close to the measured values. The model predicted an accumulation of C and N in the topsoil layer as expected, but not measured. Although no measurements were available to compare the dynamics and amounts of the soil NO₃-N and NH₄-N, the simulated values appear realistic for an effluent treatment site and are consistent with measured pasture data. Considering the large amount of total N and C applied onto the lysimeters over the 42 months of operation (4 t ha⁻¹ of N and 42 t ha⁻¹0f C), the various forms of C and N in dissolved and particulate DFE as well as in returned pasture, and that the parameters used in the test have not been calibrated, the simulated values from CaNS-Eff compared satisfactorily to the measured data.

The Virginia Beef Cattle Simulation Model (VBCSM) is a user-friendly, dynamic, stochastic computer program whose objective is to serve as a decision-aid for Virginia cattlemen dealing with complex management issues such as whether to retain weaned calves through the stocker growth stage. Its five source-code modules are reproduction, forage, nutrition, marketing, and a tool that randomly assigns values to variables from appropriate statistical distributions. The VBCSM contains production statistics for 12 breeds, 21 forage species, and three Virginia agro-ecological zones. It simulates at the animal level using information obtained from program dialog. Help can be activated on each dialog page. It is event-driven on a daily time increment. The reproduction module simulates puberty, conception, abortion, parturition, dystocia, lactation, pregnancy testing, culling, within-herd replacement female selection, open or pregnant replacement female purchases, cow and calf mortality, and weaning. The forage module simulates daily pasture growth dependent upon month, precipitation, erosion, pasture maintenance, grazing system, farm location, weed infestation, and slope. This module interacts with the nutrition module to calculate each animal's forage intake, supplemental feed requirements, and daily gain or loss using National Research Council equations. The marketing routine sells the weanling calves to the stocker herd and sells stocker calves, orphan calves, and cull cows through user-specified markets, including the Virginia Tel-O-Market auction. After simulating for eight years to achieve equilibrium conditions, the VBCSM provides an income statement for the cow-calf operation and a partial budget for net income or loss from the stocker herd for up to three years. VBCSM was rigorously tested using a mathematical model with two calving seasons, three lengths of breeding season, four culling policies, and a year effect. Descriptive statistics suggest that the program code works in a consistent manner. However, several potential programming inconsistencies were discovered. Simulation results indicate that fall calving may be more profitable for Virginia cattle producers than spring calving for weanling calf production, but a spring calved stocker program may be more profitable that a fall calved stocker program. Perhaps, VBCSM will help cattlemen to enhance their profits by more efficient market planning and utilization of production resources.

Pseudofunctions used in scaled up reservoir simulation models depend on the geology, recovery process and fluid velocities. Accurate pseudofunctions can be derived in every section of the reservoir model to be coarsened by expensive fine-scale simulations with appropriate boundary conditions. Experimental design and response surface modeling provide an accurate and much cheaper framework for calculating effective properties. Effective properties are calculated at a few chosen points (the design) by simulating flow with a fine grid. Best-fit polynomials can be computed using response surface methods. Models based on outcrop exposures of the Frewens Sandstone, a fluvial-deltaic deposit in Central Wyoming are upscaled using response surfaces with few fine scale simulations. Uncertainty in effective properties and their sensitivity to factors in the reservoir description can be accurately and inexpensively investigated using response surfaces. Probability distributions of the effective properties are derived by conducting Monte Carlo simulations with the response surfaces

Prosim VII: An enhanced production simulation model

The purpose of this investigation was to study the economic implications to producers of altering the offtake pattern of lambs on irrigated farms. To permit such a study, a detailed analysis of both management and drafting strategies in the system under analysis, was required. This was necessary because of the complexity of the lamb production system and also because it allowed an assessment and comparison to be made between traditional (dryland) practices and alternative production practices under irrigation. A simulation model incorporating biological, physical and economic components of the lamb production system on an irrigated farm was constructed and was then used to experiment with alternative management and drafting strategies. These strategies were tested on a few different , ‘farms’ that is, the growth rates of the ‘model’ lamb to which all simulated lambs relate, were varied between ‘farms’. By this method, some allowance was able to be made for the different abilities of farmers in practise, to manage both stock and feed successfully. Feed supply and demand were best equated when lambing percentage was highest and stocking rates lowest. Returns could be improved upon if the lambs grew at a rate that allowed as many as possible to be drafted as PH’s by early March. Although high lambing percentages do not necessarily equate with high growth rates, this situation was improved by a delay in the mean drafting date which allowed more of the multiple birth lambs to benefit from compensatory growth. The choice of an optimum management or drafting strategy ultimately depends on each decision maker’s attitude to risk, and, in practise, the decision maker must assess the stocking rate and performance level at which he is most confident. In conclusion, while, irrigation increases feed supply, higher stocking rates and the rearing of replacements allow little scope to change lamb offtake times although a slight delay appears justified to permit a greater number of lambs to achieve the benchmark grade weight.

A common approach to the analysis of farm machinery purchase/contract and size problems has been the use of average total cost curves (a partial analysis). This study examines a whole farm approach to machinery investment problems using a simulation model. A whole farm business simulator was built by adapting the Lincoln University College Business Simulator to reflect the 1976/77 Australian income taxation laws and to allow for a more realistic simulation of machinery, loans and income taxation structure of a farm business. A wheat/sheep case study property from the Central West of N.S.W. was used to illustrate the potential of the model in evaluating a range of farm machinery strategies and to test the effect of changes in taxation and credit policy changes on the machinery strategies. Results from the simulation model were contrasted with two average cost curve approaches. The results from the low cost average total cost curve analysis indicated a similar ranking of machinery strategies to the simulation model. However it is suggested that because of the ability of the simulation model to simulate the whole farm business, it can better reflect the actual differences between strategies using a number of criteria (average cost curves normally use only one criterion). The simulation model can also be used to analyse a much wider range of farm machinery problems. Machinery strategy differences from the model were found to be very sensitive to the Income Equalization Deposits option, the elimination of the 40% Investment Allowance and a 2% reduction in loan interest rates. The study demonstrated that the development of the simulation model could have important applications in extension, research and farm management education. However it has revealed serious deficiencies in available machinery data, essential for farm managers to make informed machinery decisions.

The most important factor in the improvement of the traditional system of beef production in the “cerrado" area of the Central Brazil Region is the introduction of improved pasture. Both direct establishment and establishment through cropping have been the two approaches taken to introduce improved pasture. Risk related to crop yield and beef prices influence the choice of the management strategies. To assess the management strategies to improve the traditional system the EMBRAPA model was modified to carry out the study. The results from experimenting with a stochastic bio-economic simulation model indicated that under the current situation of beef prices, inflation and loan policies, the total replacement of the "cerrado" by way of pasture improvement is highly profitable. Although introducing pasture by way of cropping had a marked degree of risk, it still showed a better pattern of profit and cash-flow when compared with establishing pasture directly. Variation in beef prices did not appear to be an important factor in farm development, but starting the improvement of the system in the top of beef price cycle was shown to be a good strategy. The strategy of buying in breeding cows to increase stock numbers provided a better economic response than any policy comprising breeding up. Selling fat steers at 46 months of age gave better economic results than any other selling strategy. Although the total replacement of the "cerrado” by improved pasture is profitable, the rate of land development should be adjusted for each particular farm situation in order to provide a balance in the cash flow. The lack of relevant information on biological components and from the real system constituted a difficulty in building and validating the model. However, the flexibility incorporated in the model makes it potentially useful for advisory purposes.

The basic objective of this study was to develop a simulation model that would enable physical and economic evaluation of alternative farm irrigation schemes in the hill and high country. The model used the output of the engineering systems model, "HAKA" to provide a management and economic evaluation of each engineering option. The proposal of the study was to build a farm scale grazing model capable of simulating most forms of herbage production, animal types and management systems employed in the hill and high country. The model had to incorporate weather induced variability and irrigation responses over a 25 year period. The model required a framework that would allow further components to be added to it. Initially it was designed to investigate the Hakataramea but was flexible enough to study other areas with simple modification. Comprehensive experimentation to demonstrate the model was limited to one farm in the Hakataramea valley which has invested in irrigation. Dryland experimentation consisted of feeding hay to ewes pre lambing and at tupping, growing lucerne hay for sale with reduced stock numbers and increasing stock numbers. Irrigation experimentation consisted of firstly simulating unlimited water supply to get an approximation of the property’s potential and then interacting with the "HAKA" engineering systems model. Two dam sizes irrigating two areas were evaluated plus the impact of three different sets of river diversion rules for filling the large size dam. The model indicated that irrigating the large area from the large dam was financially the best option. It reduced variation in feed supply -between years and halved the incidence of feed shortfalls even with increased stock numbers. In severe drought years dam filling was inadequate and severe financial penalties were incurred in feeding stock. This would appear to indicate that the unstated assumption, that all farmers have equal and necessary management skills, must be examined carefully if irrigation water is in limited supply. The objective of this report has been to demonstrate the model constructed rather than the effectiveness or economics of hill and high country irrigation. If the results presented here are to be used as an indicator of the economics of irrigation then they can be considered to be at the worst end of the spectrum because variation in engineering costs was not considered, nor were farm management practises, and the farmers individual taxation position.

An important consideration in almost all farming enterprises the provision of seasonal finance. Tobacco growers in particular require substantial inputs of working capital throughout the growing season because of high production costs and unevenly spread income. Effective control and supervision of financial accounts is desirable both to the financier and to the tobacco grower. The objective of this thesis is to develop and test a simulation model of the tobacco enterprise which can be used for budgetary control. A 'skeleton' model is developed and incorporated into a comprehensive information system, designed to operate at low cost and with a minimum demand on management and advisory time. The major use of the model is for forecasting and analysing monthly accounts. This relieves farm advisers of the tedious and time-consuming calculations involved and provides the tobacco growers with a more accurate picture of their current financial situation. It is concluded that skeleton modelling provides a feasible concept whereby the effectiveness of an advisory service can be improved by means of a computerised information system.

Farmers make decisions in a planning environment in which planning information is frequently imperfect and outcomes uncertain. Efficient planning systems must therefore be dynamic in nature. However, available systems which are capable of being directly applied to individual farms on a whole farm basis do not reflect the true nature of the planning environment. This study explores the nature of these problems through developing a dynamic planning system for pig fattening. The study considers the nature of the pig fattening problem and specifies the important factors that planning models must allow for representing the realistic planning environment. The features of currently available models are reviewed leading to a statement of the improvements required. A realistic model of pig fattening is developed and possible solution algorithms are considered. As the models require detailed pig growth information a simulation model designed to predict response from alternative feeding patterns was developed. The output from this model together with the output from a model which calculates growth distributions and posterior probabilities on potential growth provide some of the input data for a stochastic multi-period linear programming model of the problem. The remainder, feed cost information, is obtained from least cost models formulated to represent the features of the dynamic planning problem. With imperfect information, planning involves continual re-planning as new observations are made. Consequently only the solution to the first period of the model is likely to be implemented. To ensure that the first period decision set is optimal it is shown that a minimum number of periods must be included in the planning model. The minimum number is referred to as the planning horizon. Methods of determining a planning horizon are reviewed and it is concluded that available methods do not provide a general method for all planning situations. A method is developed for the pig fattening problem. Finally, a number of planning experiments were carried out to demonstrate the value of the planning models and systems developed. The results indicate the potential value of applying sophisticated planning methods to individual farms and provide a means to examine the condition under which detailed individual farm dynamic planning can be worthwhile.