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Eradicating invasive species from islands is a proven method for safeguarding threatened and endangered species from extinction. Island eradications can deliver lasting benefits, but require large up‐front expenditure of limited conservation resources. The choice of islands must therefore be prioritised. Numerous tools have been developed to prioritise island eradications, but none fully account for the risk of those eradicated species later returning to the island: reinvasion.In this paper, we develop a prioritisation method for island eradications that accounts for the complexity of the reinvasion process. By merging spatially explicit metapopulation modelling with stochastic dynamic optimisation techniques, we construct a decision‐support tool that optimises conservation outcomes in the presence of reinvasion risk.We applied this tool to two different case studies—rat (Rattus rattus) invasions in the Seaforth archipelago in New Zealand, and cane toad (Rhinella marina) invasions in the Dampier archipelago in Australia—to illustrate how state‐dependent optimal policies can maximise expected conservation gains.In both case studies, incorporating reinvasion risk dramatically altered the optimal order of island eradications, and improved the potential conservation benefits. The increase in benefits was larger in Dampier than Seaforth (42% improvement versus 6%), as a consequence of both the characteristics of the invasive species, and the arrangement of the islands.Synthesis and applications. Our results illustrate the potential consequences of ignoring reinvasion risk. We recommend that reinvasion risk be explicitly included in any island eradication prioritisation involving an archipelago, particularly when some islands are close to the mainland. [ABSTRACT FROM AUTHOR]

The objective of this research was to present a platform to identify the optimal staffing of a multiskilled workforce in the construction industry. The novelty of our model is its capability in developing a customized and context specific multiskilled workforce staffing strategy that identifies the best compromise between multiskilling costs and benefits with consideration of unique features of different production environments. Another novelty of our multiskilled workforce staffing is its capacity to explore every possible schedule associate with every possible staffing strategy. Our model chooses the multiskilling staffing strategy corresponding to the schedule that leads to the best results in terms of cost and time. The paper examined optimal multiskilling staffing and scheduling and compared it with well-regarded existing multiskilling strategies in the construction industry, such as chaining and direct capacity balancing, using an optimization technique. The parameters of the optimization model were informed by a small hypothetical case and a real case off-site construction factory producing bathroom pods in Australia. Developed an optimal multiskilling staffing strategy continuously and in some cases significantly outweighed the existing well-regarded multiskilling strategies. The optimal multiskilling staffing strategy is highly context-specific and can be very complex because achieving it needs having an appropriate staffing platform. Subjective decision making on a multi skilling staffing strategy that does not deploy the workforce to the most appropriate workstations can lead to a significant productivity loss. [ABSTRACT FROM AUTHOR]

Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations. [ABSTRACT FROM PUBLISHER]

In this paper, we consider dynamic programming for the election timing in the majoritarian parliamentary system such as in Australia, where the government has a constitutional right to call an early election. This right can give the government an advantage to remain in power for as long as possible by calling an election, when its popularity is high. On the other hand, the opposition's natural objective is to gain power, and it will apply controls termed as ‘boosts’ to reduce the chance of the government being re-elected by introducing policy and economic responses. In this paper, we explore equilibrium solutions to the government, and the opposition strategies in a political game using stochastic dynamic programming. Results are given in terms of the expected remaining life in power, call and boost probabilities at each time at any level of popularity. [ABSTRACT FROM AUTHOR]

Introduction: Australia's first proton beam therapy (PBT) centre will house a fixed-beam room and two gantry rooms. As the only PBT facility in Australia for at least the short term, there is a need to efficiently allocate treatment appointments between the gantry and fixed-beam rooms. This planning study assesses the dosimetric differences between fixed-beam and gantry-based treatment plans for base of skull chordoma, one of the core indications likely to be referred for PBT in Australia. Methods: Retrospective gantry-based and fixed-beam treatment plans were generated for five patients with base of skull chordoma. Fixed-beam plans were generated with a conventional horizontal patient positioning system. Robust intensity modulated proton therapy (IMPT) optimisation and evaluation techniques were used for both delivery systems. Plans were designed to maximise target coverage while adhering to maximum dose constraints to neighbouring critical organs at risk. Results: Robust target coverage and integral dose were found to be approximately equivalent for the gantry-based and fixed-beam plans. Doses to specific organs at risk could be reduced with the gantry-based geometry; however, the gantry-based plans did not exhibit a general decrease in doses to organs at risk. Conclusion: A fixedbeam treatment plan was found to be non-inferior to a gantry-based treatment plan for all base of skull patients included in the current study. [ABSTRACT FROM AUTHOR]

The article presents abstracts related to electricity including "Estimator-based sliding mode control strategy design," by J. D. Lee, J. F. Lin, and A. J. Li, "A discrete multi-objective particle swarm optimization dynamic self-regulating energy management scheme for smart grid applications," by A. A. A. Elgammal and A. M. Sharaf, "Upgrading and uprating of hydro generators: An Australian perspective," by M. M. Znidarich .

Locata Corporation's positioning technology 'Locata' is a terrestrial-based RF-ranging technology that provides high accuracy position solutions using a network (LocataNet) of time synchronised pseudolite-like transceivers (LocataLites). This technology provides centimetre level accurate position solutions for static positioning using carrier phase measurement data which provides an advantage over other technology for monitoring structural movement in many applications. The Locata network can be deployed in such a configuration around a structure that optimal network geometry could be ensured despite site constraints. This paper describes the first deformation trial using Locata technology carried out at Tumut Pond Dam, in the state of New South Wales, Australia. This trial was run for 22 hours and yielded millimetre-level precision for horizontal positions, and centimetre-level vertical precision for all observed epochs. The accuracy of the coordinate solutions was at the few millimetre level with standard deviations of 2.5 mm, 2.1 mm and 18 mm for the east, north and height components respectively. The position solutions were generated by an iterative least squares estimation process of Locata carrier phase observations per epoch. Then mean of sixty seconds coordinate solutions was used as input for a Kalman filter. Standard deformation detection methods were applied. No significant displacement was observed during this trial. [ABSTRACT FROM AUTHOR]

Transmission system structure has an essential effect on the reliability of the power system and electricity market performance, especially when producers bid strategically. As part of on-going research on the design of a robust algorithm for expansion planning of the transmission grid in the Australian electricity market, this paper presents a framework which addresses: (1) the security of power delivery to the load points of the transmission system in case of single line outages; (2) the minimization of transmission system lost load; (3) an efficient electricity market for market participants; (4) construction and maintenance costs of transmission augmentation options; and (5) operation efficiency of the transmission grid. The suggested algorithm benefits from the dynamic programming and sensitivity analysis approaches along with the aggregation method in its multi-criteria decision-making to locate the optimum configuration of a future transmission system. A set of indices, which account for impacts of the augmentation options of the transmission grid on five aforementioned reliability and market criteria, are proposed and used in the optimum framework for expansion planning of the transmission grid. Although the methodology is promising for expansion planning of the transmission system, considering the sensitivity analysis concept employed, the proposed methodology would be suitable to detect the sensitive areas of the transmission system to be expanded. The tool would be very useful in the case of large scale power systems for a smart reduction of the transmission expansion options. The proposed methodology has been applied to a 6-bus and a modified IEEE 30-bus test system to show the effectiveness of the sensitivity-based algorithm. [ABSTRACT FROM AUTHOR]

The Satin Bowerbird Optimization (SBO) is a recently developed meta-heuristic optimization algorithm which was inspired by the Satin Bowerbirds living in Australia's rainforests and other mesic habitats. Like other meta-heuristic algorithms, the main problem faced by the SBO is that it has been empirically demonstrated to become easily trapped into local optimal solutions, creating low precision and slow convergence speeds. Therefore, in an effort to enhance global convergence speeds and in order to obtain a better performance, this paper introduces the chaos theory into the SBO optimization process. Various chaotic maps were considered in the proposed Chaotic-SBO (CSBO) method in order to replace the main parameter's greatest step size (), which assists in controlling both exploration and exploitation. The proposed CSBO methods are benchmarked within the CEC2014 test problems. The numerical results show that these novel algorithms improve the chaotic maps, especially in the Tent map, as well as improving the performance of the original Satin Bowerbird Optimization Algorithm. Specifically, the CSBO10(Tent) achieved the best performance. [ABSTRACT FROM AUTHOR]

Effectively dealing with invasive species is a pervasive problem in environmental management. The damages that stem from invasive species are well known. However, controlling them cost-effectively is an ongoing challenge, and mathematical modeling and optimization are becoming increasingly popular as a tool to assist management. In this paper we investigate problems where optimal control theory has been implemented. We show that transforming these problems from state–costate systems to state-control systems provides the complete qualitative description of the optimal solution and leads to its theoretical expression for free terminal time problems. We apply these techniques to two case studies: one of feral cats in Australia, where we use logistic growth; and the other of wild-boars in Italy, where we include an Allee effect. [ABSTRACT FROM AUTHOR]

This paper documents the development of a time series of Australian input-output tables. It describes the construction techniques employed in order to overcome the major issues encountered. Environmentally important processes were delineated using a range of detailed commodity data, thus expanding the original tables from roughly 100 industries into a temporally consistent 344 industries. Data confidentiality and inconsistency were overcome using an iterative constrained optimisation method called KRAS - a recent modification of RAS (Lenzen et al. 2006; 2007; 2009). The article concludes by analysing the stability of input-output coefficients over time similar to work in Dietzenbacher and Hoen (2006). The issue of stability of coefficients and multipliers was investigated under the Leontief and Ghosh models of supply/demand. Finally, the predictability of the models was examined under updated final demand or primary inputs and over varying time scales. [ABSTRACT FROM AUTHOR]

Abstract Australia's east coast gas market has faced transformational shifts in demand with the commencement of three Liquefied Natural Gas facilities in Queensland. Faced with risks of high domestic prices and potential gas shortages, political intervention has been considered as a possible solution. This paper investigates the impact of network interconnectivity on domestic gas prices by employing a long-term planning model underpinned by mathematical optimisation. At optimal system cost, improved network interconnectivity can provide material and sustained price reductions for the gas market with potential flow-on reductions to the electricity market. Increased connectivity is shown to deliver reductions of over $2/GJ in average gas prices across the eastern seaboard, with a subsequent reduction in electricity prices across all mainland National Electricity Market (NEM) regions. The results also highlight the need to unlock new supply as new transmission projects, though having the potential to reduce gas prices through market connectivity, rely on adequate supply to meet long term demand, and sustain market balance. Highlights • Improved network interconnectivity can provide material and sustained price reductions. • Potential reductions of over $2/GJ in average gas prices on the eastern seaboard. • Wallumbilla-Newcastle Gas Pipeline seen to have most value from system and investor perspective. • Flow-on electricity price reductions up to 19% depending on generator bidding behaviour. [ABSTRACT FROM AUTHOR]

Focuses on the tools designed for comparative statics in Australia. Significance of comparative statics to industries; Theorems and proofs; Equations for optimization with one or more variables.

• A novel integrated short-term power load forecasting system is proposed. • The fuzzy granulation technique simplifies the large-scale dataset and extracts the effective characteristics of the raw data. • A novel multi-objective variant of the tuna swarm optimizer is utilized to integrate multiple forecasting sequences. • The optimal weight coefficients of the ensemble model are theoretically demonstrated to be the Pareto optimal solutions. • Our proposed forecasting system can gauge the uncertainty of future electricity load. As an irreplaceable power source, electricity is responsible for sustaining the national economy and social development, and the precondition for the power system's stable operation is to perform an accurate short-term load forecast (STLF). However, with the increasing forms of social power consumption and the emergence of large-scale sustainable resources on the grid, which make STLF increasingly challenging as the power load exhibits greater stochasticity and instability. Therefore, a novel STLF system is developed in this paper, which incorporates data fuzzy granulation, a high-performance optimizer for integrating forecasting sequences, point and interval forecasts. Moreover, the performance tests of the optimization algorithm verify that our proposed optimizer can obtain more approximate solution sets to the real Pareto front and outshines the traditional optimization algorithm concerning convergence and diversity. Load data from three regions of Australia demonstrate that our developed system can remarkably contribute to the accuracy and stability of the STLF , and also quantify the volatility and uncertainty of the power load, which allows power workers to better capture the fluctuation interval of future loads and effectively enhance the flexibility of grid operation. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes an investigation in the optimisation of a steam turbine CHP (combined heat and power) system under three economic cases resulting from the introduction of a carbon price in Australia: no carbon price (case one); carbon price and not liable (case two); and a carbon price and liable (case three). A case study was considered for validation purposes. Steam turbine partial load equations and required pricing data under the various cases were derived. For this coal boiler steam turbine CHP system, the most economical situation was case two, and the least economical situation was case three. These results indicate the introduction of the carbon price has made the installation of these types of systems more attractive in terms of economic benefits as long as the system is not liable. All scenarios proved uneconomical with a natural gas boiler. The high price of fuel compared to the low cost of purchasing electricity resulted in recovered heat following the demand. This shortfall in electricity generation required the system to purchase the majority of its electricity demand from the grid in all three cases. Due to the low price of selling electricity, no excess electricity was produced in any investigation. [Copyright &y& Elsevier]

We consider time-critical supply chains (SCs) in the Australia dairy industry and recovery policies in the presence of the ripple effect. Ripple effect is the impact of a disruption on SC economic performance and disruption-based scope of changes needed in the supply structures and parameters to preserve the resilience. First, we describe the ripple effect in general and one example of the ripple effect in the dairy SC in Australia. Second, we present a model for reactive recovery policies in the dairy SC under conditions of the ripple effect and exemplify them on a simulation example. The results of this study can be used in future for comparing proactive and reactive approaches in tackling the ripple effect from resilience and flexibility views. [ABSTRACT FROM PUBLISHER]

Abstract: This paper examines the optimisation of the transport scheduling of woodchips for in-field chipping operations whose efficiency depends on a range of factors. It illustrates the advantages of optimising trucking efficiency and cost in the context of the Australian forest industry. The study was enabled using an adapted version of Simulated Annealing and a forestry domain model, and a simulator based on them, called Fast Truck, was implemented for experimental use. Insights arising from the simulator results are discussed in the context of the Australian industry. The factors worth noting are truck payload and chipper utilisation, which by optimisation, account for 52% and 29% of the total cost savings obtained, respectively. These savings arise when better transport control and management occur in chipping operations. Further work will consider ways to implement these optimisations, primarily, by adapting Fast Truck as an optimiser of daily dispatch schedules. [Copyright &y& Elsevier]

Abstract: This paper presents results from a prototype renewable energy network optimisation model, which is driven with output from a high resolution mesoscale weather model of the current climate. The network model estimates potential power output for various combinations of wind and solar farms across a large domain of several hundred kilometres and calculates the required back-up power capacity needed to meet demand. The model finds the network configuration that minimises the cost of the system, and shows that considerably less installed capacity of both renewables and back-up is required if the choice of locations for wind and solar farms takes into account the covariance in the wind and solar fields. [Copyright &y& Elsevier]

Abstract: This work defines Transit Schedule Design (TSD) as an optimization problem to construct the transit schedule with the decision variables of the location of timing points and the amount of slack time associated with each timing point. Two heuristic procedures, Ant Colony and Genetic Algorithms, are developed for constructing optimal schedules for a fixed bus route. The paper presents a comparison of the fundamental features of the two algorithms. They are then calibrated based on data generated from micro-simulation of a bus route in Melbourne, Australia, to give rise to (near) optimal schedule designs. The algorithms are compared in terms of their accuracy and efficiency in providing the minimum cost solution. Although both procedures prove the ability to find the optimal solution, the Ant Colony procedure demonstrates a higher efficiency by evaluating less schedule designs to arrive at a ‘good’ solution. Potential benefits of the developed algorithms in bus route planning are also discussed. [Copyright &y& Elsevier]

Abstract: Marxan is the most widely used conservation planning software in the world and is designed for solving complex conservation planning problems in landscapes and seascapes. In this paper we describe a substantial extension of Marxan called Marxan with Zones, a decision support tool that provides land-use zoning options in geographical regions for biodiversity conservation. We describe new functions designed to enhance the original Marxan software and expand on its utility as a decision support tool. The major new element in the decision problem is allowing any parcel of land or sea to be allocated to a specific zone, not just reserved or unreserved. Each zone then has the option of its own actions, objectives and constraints, with the flexibility to define the contribution of each zone to achieve targets for pre-specified features (e.g. species or habitats). The objective is to minimize the total cost of implementing the zoning plan while ensuring a variety of conservation and land-use objectives are achieved. We outline the capabilities, limitations and additional data requirements of this new software and perform a comparison with the original version of Marxan. We feature a number of case studies to demonstrate the functionality of the software and highlight its flexibility to address a range of complex spatial planning problems. These studies demonstrate the design of multiple-use marine parks in both Western Australia and California, and the zoning of forest use in East Kalimantan. [Copyright &y& Elsevier]

We provide international evidence on the issue of whether the optimizing IS equation is more stable than a backward-looking alternative. This evidence consists of estimates of IS equations on quarterly data for the UK and Australia, both for the full sample of the last 40 years and for the period following major monetary policy shifts in 1979–80. Results suggest the parameters in the optimizing IS equations are more empirically stable than those of the backward-looking alternative. The use of dynamic general equilibrium modelling in empirical work does deliver material benefits, in the form of equations more suitable for policy analysis. [ABSTRACT FROM AUTHOR]

The Satin Bowerbird Optimisation (SBO) was inspired by the Satin Bowerbirds living in Australia's rainforests and other mesic habitats. Like other meta-heuristic algorithms, the main problem faced by the SBO is that it has been empirically demonstrated to become easily trapped into local optimal solutions, creating low precision and slow convergence speeds. To overcome these deficiencies, we propose herein the Adaptive Chaotic Satin Bowerbird Optimisation algorithm (AC-SBO). Within the AC-SBO algorithm, a chaotic map is introduced to modify the search process, with which to enhance global convergence speeds, and to obtain better performance. We introduced the chaos theory into the SBO optimisation process, in order to replace the main parameter's greatest step size ( α), which assists in controlling the balance between both exploration and exploitation. The search accuracy and performance of the AC-SBO algorithm were verified on ten classical benchmark functions. In addition, in the experimental CEC2014 results showed that for almost all functions, the AC-SBO technique proved superior to the other comparative algorithms optimisations. The Wilcoxon rank-sum statistical test was performed in order to judge the significance of the results, and further demonstrated the improved performance of the proposed AC-SBO algorithm. [ABSTRACT FROM AUTHOR]

Accurate electrical load forecasting always plays a vital role in power system administration and energy dispatch, which are the foundation of the smooth operation of the national economy and people's daily life. Thinking from this vision, many scholars have made great efforts to seek suitable optimization algorithms to improve the performance of existing forecasting algorithm. However, most of the studies ignore the inherent disadvantages of single optimization algorithm, which leads to sub-optimal forecasting performance. Therefore, a novel electric load forecasting system was successfully proposed in this paper by the combination of data preprocessing, hybrid optimization algorithms, and several single classical forecasting methods, which successfully overcomes the defects of single traditional forecasting models and achieves higher forecasting accuracy than that of single model optimization. Besides, the 30 min interval data of Queensland, Australia from March to April is used as illustrative examples to evaluate the performance of the developed model. The results of tests demonstrate that the proposed hybrid model can better approximate the actual value, and it can also be employed as a useful tool for smart grids dispatching planning. • Develop a novel power load forecasting system for grid safety dispatch. • Extract important information effectively from the original load time series. • Creatively propose two hybrid optimization methods to optimize forecasting models. • Set up a comprehensive evaluation system to verify the effectiveness of the models. [ABSTRACT FROM AUTHOR]

Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0-14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within C5 to 15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to the commencement of a forecast season. The system would allow for forecasts to be updated if necessary. [ABSTRACT FROM AUTHOR]

Quantifying reference evapotranspiration (ET) is essential in water resources management. Although, many methods have been developed with different level of accuracy, in this study, two new equations were developed and optimized for estimating ET using Honey-Bee Mating Optimization (HBMO) algorithm. The first eq. estimates ET from extraterrestrial radiation (R), relative humidity (RH) and mean daily temperature (T), while the second uses the same parameters except that mean daily temperatures is replaced with maximum daily air temperature (T). Both equations were developed using climatic data from eight weather stations in Western Australia and subsequently verified using data from ten sites across Australia. The estimated ET values from both equations versus the FAO56-Penman-Monteith have a coefficient of determination, R, of larger than 0.96. Moreover, the performance of six commonly used methods of estimating ET including Hargreaves-Samani, Thornthwaith, Hamon, Mc Guinness-Bordne, Irmak and Jensen-Haise were assessed and the Hargreaves-Samani method performed better than others. An attempt was made to calibrate the Hargreaves-Samani equation; however, its overall performance did not improved and the two newly proposed equations are suggested to be used in Australia. [ABSTRACT FROM AUTHOR]

The impact of water quality conditions in source reservoirs on the optimal operation of a regional multiquality water-distribution system is analyzed. The optimization model concurrently minimizes three operational objectives being pump energy costs, turbidity, and salinity deviations at customer demand nodes from allowed values. The optimization problem is solved using the optimization tool GANetXL incorporating the NSGA-II, linked with the network analysis software EPANet. The example network adapted from the literature captures some of the unique features of the Wimmera Mallee Pipeline in Australia. Six scenarios representing different water quality conditions in source reservoirs are analyzed. It was discovered that two types of trade-offs, competing and noncompeting, exist between the objectives and that the type of trade-off is not unique between a particular pair of objectives for all scenarios. These and other findings may be of particular use to system operators in their long-term operational planning and decision making. [ABSTRACT FROM AUTHOR]

Currently pastoralists in Australia view native mammal species as one of many variables that impact, usually negatively, on their productivity and therefore profitability. This does not necessarily have to be the case. The species with the largest impact, kangaroos, have a value that could be incorporated into their income stream as a method of both reducing risk and increasing biodiversity, and therefore increasing resilience. An investigation of the idea of optimally allocating stocking rates using techniques analogous to classical portfolio selection optimization is conducted. Using historical pricing data for beef, wool, and kangaroo meat, an efficient frontier is formed to analyze the best scenario dependent on an investors risk aversion. It is shown that there is a clear opportunity for pastoralists to benefit economically by the inclusion of kangaroos in a mixed-grazing strategy for Australian rangelands. [ABSTRACT FROM AUTHOR]

The dominant working structure of an underground mine is a set of interconnected tunnels which provides access to ore zones and haulage of ore from the designated ore zones to the mill. This set of interconnected tunnels forms a network. We describe a mathematical network model and the modelling features of our two software tools for designing underground mine layouts that minimise associated costs. The application of these techniques is illustrated in two industry case studies. In the first, we apply the underground network optimisation tool to design an extension to an Australian gold mine where 15 new distinct orebodies are located in a 3 km long region, several hundred metres underground. In a second case study, we design a single decline for accessing a large orebody where a turning circle constraint is a significant factor. An efficient decline is found using the decline optimisation tool. [ABSTRACT FROM AUTHOR]