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The objective of this paper is to introduce the integration of the diverse factors that affect the performance of Photovoltaic panels and how those factors affect the performance of the system. Those factors include: environmental, PV system, installation, cost factors as well as other miscellaneous factors. Each of these factors is further classified into novel subcategories along with the reduction/increase effects of some factors on the system output. Solar irradiance, module temperature, dust accumulation, shading and soiling factors are some of the environmental concerns that have major effects. Panel's I-V characteristics, inverter, battery and panel efficiencies, panel material, atomic structure and band-gap energy are some of the system factors. As for the installation factors, cable characteristics, angle of inclination, mismatch effects, fixed/tracking PV mechanisms as well as MPPT are crucial to consider. Concerning the cost factors, the cables and the overall system costs are to be taken into consideration especially during payback period calculations. Other miscellaneous factors have a role in affecting the performance of the system such as degradations in the PV panel, panel resistances, performance ratio, maintenance and cleaning, sizing of the system as well as the surface area of the panel. The study results shall help both practitioners and researchers in having an inclusive overview about the several factors that can have an effect on the performance of the PV panels by removing the burden of having to search several studies under each category of factors. Moreover, researchers can build on this study by researching new ways to decrease the effect of certain factors which can further enhance the performance of the system. On the other hand, practitioners can introduce new systems that can be physically implemented in our daily lives to hinder the effect of factors negatively affecting PV panels’ performance.

Support Vector Regression (SVR) has already been proved to be one of the most referred and used machine learning technique in various fields. In this study, we have addressed a predictive-cum-presc...

The recent crisis of environmental pollution and resource depletion has become a major global issue, resulting in an increasing interest in remanufacturing in many countries. However, manufacturing for environmental protection is considered an additional cost factor by most companies. Therefore, governments worldwide have actively implemented remanufacturing policies to encourage the participation of numerous companies in the remanufacturing industry. As an eco-friendly technology, remanufacturing has impacted the environmental improvement and resource savings by reducing raw materials and production processes. In particular, the effect of machine tool and automobile part remanufacturing is very remarkable, since most parts are reusable. In this study, the standardization technology for remanufacturing process of machine tools is developed by remanufacturing of used vertical machining center. Based on the work operation sheet for establishing the process, the remanufacturing process chart and program were developed. The performance evaluation of machining centers before and after remanufacturing was also executed, to check the performance improvement of the remanufactured product. The performance evaluation criteria were established to improve reliability.

Fuel-power supply systems are priority areas of engineering services. They account for large capital investments and costs of energy resources. An energy supply system based on LPG provides a high level of diversification and autonomy. This article presents recommendations for determining the basic technological parameters of liquefied petroleum gas tanks. The recommendation are intended to improve the efficiency of the tanks. The economic-mathematical model of optimizing underground tanks considers own evaporative capacity of tanks, their mutual thermal effect, as well as the temporal dynamics and unstable cost factors. The model allows for revealing the optimum technological parameters of installations: a single tank volume, the number of tanks in a group installation, the calculated level of gas tanks fill up before next refueling, depending on the volume of gas consumption.

In process industry, SIL determination is a risk assessment process through which target Safety Integrity Levels (SIL) are allocated to Safety Instrumented Functions (SIF). A target SIL represents the significance of the hazard against which the SIF protects the plant. This paper introduces new SIL determination methods by taking an optimization approach. Unlike the conventional methods, which are generally focused on calculating the gap between the existing and tolerable levels of risk, the methods introduced in this paper are aimed at optimizing the marginal cost or the benefit-cost ratio. By incorporating the cost factor into the SIL determination process, these methods deliver the most reasonably practicable solutions that can minimize the risk while taking into account the cost of solution. The new methods are formulated for corporate risk and the risk to community (i.e. ALARP). Both methods are derived for demand and continuous modes of SIF operation. Furthermore, a new Safety Index is introduced to combine the SIL and the average Probability of Failure on Demand (PFD) or Frequency of Failure per Hour (PFH). The application of the mathematical models is demonstrated through a practical example from power industry.

Energy prices have become a considerable cost factor for many industry sectors. However, energetically inefficient processes exist in almost every company. The best solution for energy cost reduction is to identify energy efficiency potential and take appropriate measures. The article analyses energy consumption for the peat extraction process of a particular company located in Latvia. Energy in the peat extraction industry is mainly consumed by different types of machinery and tractors which use diesel as an energy source. The benchmark has been calculated for the peat extraction process and the results show that around 2.44 kWh diesel fuel is necessary to extract 1 m3 of peat. The comparison and analysis of differences in particular energy efficiency indicators allows for identification of inefficient use of energy, possible financial savings for the company and reduction of CO2 emissions.

In this study, an attempt has been made to study analysis of yield gap in wheat in Amravati district with view to study the economics of wheat production and resource use efficiency. The study was based on primary data on input used and there upon costs was collected from two tehsils viz., Amravati and Morshi and from each tahsil 45 samples were selected. Per hectare input utilization for wheat indicated that the medium farmers were used higher inputs as compared to other farm size group of farmers. Per hectare total cost of cultivation of wheat for the sample as whole was Rs. 37235.99 per hectare, gross return from wheat at overall level was Rs. 63861.60. The input-output relationship at overall size group was 1.76 at Cost 'C For the study resource use efficiency of wheat, Cobb-Douglas production function was used. In the overall group resources seed, plant protection and area were significant. Marginal value of product to factor cost ratio at overall group, in case of Human labour, bullock labour and seed was positive and more than one and in case of machine labour, fertilizer, manure, plant protection and area it was negative and less than one.

The selection of a suitable location for infectious waste disposal is one of the major problems in waste management. Determining the location of infectious waste disposal centers is a difficult and complex process because it requires combining social and environmental factors that are hard to interpret, and cost factors that require the allocation of resources. Additionally, it depends on several regulations. Based on the actual conditions of a case study, forty hospitals and three candidate municipalities in the sub-Northeast region of Thailand, we considered multiple factors such as infrastructure, geological and social & environmental factors, calculating global priority weights using the fuzzy analytical hierarchy process (FAHP). After that, a new multi-objective facility location problem model which combines FAHP and goal programming (GP), namely the FAHP-GP model, was tested. The proposed model can lead to selecting new suitable locations for infectious waste disposal by considering both total cost and final priority weight objectives. The novelty of the proposed model is the simultaneous combination of relevant factors that are difficult to interpret and cost factors, which require the allocation of resources. Keywords: Multi-objective facility location problem, Fuzzy analytic hierarchy process, Infectious waste disposal centers

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

Purpose This column takes a critical look at how technology projects are valued according to various cost factors. Those factors are examined, and a proposal is made regarding the use of agile scrum to overcome the limitations of standard cost analysis. Standard cost analysis based on time and financial burden limit the ability of library leadership to make innovative and visionary decisions concerning library strategy vis-à-vis technology. Design/methodology/approach This is a regular column. The material was written based on experience and familiarity with current issues as well as a literature review. Findings As this is a regular column that is exploratory and speculative in nature, there are no formal findings. The conclusion is based on the ideas presented in the column narrative. Originality/value The average approach to cost analysis, otherwise known as the return on investment, stifles the ability of library leadership to make strategic decisions that are focused on opportunities and relevance. Agile scrum is not simply a method to organize and manage software development projects. It transforms the thinking of an organization and establishes a decision-making model that places concerns at the appropriate level so that libraries can be nimble with regard to technology implementation.

Gelişendünyamızda elektrik enerjisine olan ihtiyaç giderek artmaktadır. Bu talebinkarşılanabilmesi için yeni kaynakların yanı sıra yeni güç iletim sistemlerinede gereksinim vardır. Ancak güç sistemlerinin oluşturulmasındaki maliyet unsurumevcut sistemin en verimli, kararlı ve güvenilir şekilde işletilmesini ortayaçıkarmıştır. Bundan dolayı mevcut güç sistemlerinin olağanüstü durumlarda nasıldavranacağının da incelenmesi ve bilinmesi gereklidir. Yapılan bu çalışmadaIEEE’nin 6 baralı güç sistemi ele alınmıştır. Bu güç sisteminde çeşitlidurumlar oluşturularak sisteminin kararlılık analizleri yapılmıştır. Bubenzetim çalışması, Güç Sistemleri Analizi Programı (PSAT) ilegerçekleştirilmiştir., In our developing world, the need for electricenergy is increasing. In order to meet this demand, new power transmissionsystems as well as new sources are needed. However, the cost factor in creatingpower systems has led to the efficient, stable and reliable operation of theexisting system. Therefore, how the existing power systems behave inextraordinary situations needs to be examined and known. In this study, theIEEE 6-bus power system is considered. Stability analysis of the system wasmade by creating various situations in this power system. This simulation workwas carried out with the Power Systems Analysis Program (PSAT).

Purpose This paper aims to identify the main non-cost factors affecting accurate estimation of life cycle cost (LCC) in building projects. Design/methodology/approach Ten factors affecting LCC in building project cost estimates are identified through literature and interviews. A questionnaire survey is conducted to rank these factors in order of priority and provide the views of cost practitioners about the significance of these factors in the accurate estimation of LCC. The data from 138 construction building projects completed in UK were collected and analysed via multiple regression to discover the relationship between capital and LCCs and between non-cost factors and cost estimation at each stage of the life cycle (capital, operation, maintenance and LCC). Findings The results of analysis of existing LCC data of completing project and survey data from cost professionals are mostly consistent with many literature views and provide a reasonable description of the non-cost factors affecting the accuracy of estimates. Originality/value The value of this study is in the method used, which involves analysis of existing life data and survey data from cost professionals. The results provide a plausible description of the non-cost factors affecting the accuracy of estimates.

Optimization of inspection and maintenance strategies based on machinery degradation could improve the operation safety and manufacturing efficiency. In this paper, a novel non-fixed periodic inspection strategy will be proposed and applied in precognitive maintenance to monitor and repair a multistage degradation process. The overall cost function of each degradation stage can be individually constructed based on its existing cost factors, and optimal solutions of the above-applied inspection intervals will be obtained by minimizing the corresponding cost function. Due to the high breakdown risk in the final stage, a warning threshold is tailored to divide it into two parts, where two additional inspection intervals are applied, accordingly. The effectiveness of our cost-optimal non-fixed periodic inspection strategy is testified for the inspection and maintenance of an industrial tool wear experiment in a high-speed milling machine, which can save 37% of the total maintenance cost without accuracy loss of the wear estimation and prediction, compared with the conventional periodic inspection strategy.

The network capacity has to be maximized to support the ever-increasing data traffic demand. One of the potential outcomes to enhance the network capacity is to enhance the spectral efficiency per unit area by expanding the serving node densities. This is unreasonable on account of present Macro evolved node B (eNB) for which site acquisition is expensive. The idea of heterogeneous network (HetNet) and multi-hop relay (MHR) are extremely prominent in long-term evolution (LTE) standard, where small cells are deployed along with Macro cell. Small cells are more suitable solution for the coverage and traffic issues experienced by Macro cell users. Unfortunately, HetNet deployment is not specified in any of the standards. The network operators have been managing tremendous investments for cellular infrastructures. Because of the high cost and lack of radio resources, a precise and productive HetNet deployment seems uttermost important. In this work, a flexible four-stage fuzzy logic-based HetNet deployment scheme is proposed, which deploys mix of Macro-eNB, Micro-eNB and relay station (RS) by considering capacity, coverage and cost factors. The proposed scheme identifies the required number of eNBs, their types and deployment locations to offer the expected coverage in a cost-effective way. The simulation results demonstrate that our proposed scheme is more flexible and offers improved performance in terms of system cost and power ratio than the conventional HetNet deployment schemes.

Recently, the estimation of accident costs within the social costs of nuclear power plants (NPPs) has garnered substantial interest. In particular, the risk aversion behavior of the public toward an NPP accident is considered an important factor when integrating risk aversion into NPP accident cost. In this study, an integrated framework for the external cost assessment of NPP accident that measures the value of statistical life (VSL) and the relative risk aversion (RRA) coefficient for NPP accident based on an individual-level survey is proposed. To derive the willingness to pay and the RRA coefficient for NPP accident risks, a survey was conducted on a sample of 1550 individuals in Korea. The estimation obtained a mean VSL of USD 2.78 million and an RRA coefficient of 1.315. Based on the estimation results in which various cost factors were considered, a multiplication factor of 5.16 and an external cost of NPP accidents of 4.39E−03 USD-cents/kW h were estimated. This study is expected to provide insight to energy policy decision-makers on analyzing the economic validity of NPP compared to other energy sources by reflecting the estimated external cost of NPP accident into the unit electricity generation cost of NPP.

To meet the fast growth of electricity demand, the traditional network solution tends to expand existing substations, build more new substations, and build transmission lines. Distributed Generation (DG) is posed as an alternative method for the network providers not only to accommodate the load increase and relieve network overload, but also to offer other additional technical and economic benefits. This paper addresses the issue of DG planning and has proposed a technique for optimizing the DG size and location to minimize the overall investment and operational cost of the system. The proposed optimization methodology assesses the compatibility of different generation schemes in terms of their cost factors that can be significantly contributed by a DG. The direct and indirect costs of power supply quality, reliability, energy loss, total power operation, and DG investment are used as key cost components of the DG siting and sizing strategy. The Particle Swarm Optimization (PSO) method is applied to obtain the optimal DG planning solutions. Finally, the proposed approach is tested on a distribution feeder of an Australian power network. Simulation results are presented to illustrate the feasibility and effectiveness of the proposed method.

Purpose– The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards improved accuracy.Design/methodology/approach– A data set of 20 building projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression modelling. A connection weight method is applied to determine the importance of cost factors in the performance of ANNs.Findings– The results illustrate that the value of the coefficient of determination=99.75 per cent for ANNs model(s), with a value of 98.1 per cent utilising multiple regression (MR) model(s); second, the mean percentage error (MPE) for ANNs at a testing stage is 0.179, which is less than that of the MPE gained through MR modelling of 1.28; and third, the average accuracy is 99 per cent for ANNs model(s) and 97 per cent for MR model(s). On the basis of these results, it is concluded that an ANNs model is superior to a MR model when predicting running cost of building projects.Research limitations/implications– A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample.Originality/value– This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation.

Carbon–polymer-composite bipolar plates (BPP) are suitable for fuel cell and flow battery applications. The advantages of both components are combined in a product with high electrical conductivity and good processability in convenient polymer forming processes. In a comprehensive techno-economic analysis of materials and production processes cost factors are quantified. For the first time a technical cost model for BPP is set up with tight integration of material characterization measurements.

With light rail transit (LRT) and other similar rail-based commuter transit systems, train and associated station platform length provides an added dimension of flexibility not available to buses. Train and platform lengths are important factors in the planning and expansion phases of a network. Existing cost models that determine optimal headway by combining passenger and operational costs provide headways that are small and close to a logistical minimum (2–3 min); this type of standard waiting cost model is not sensitive to train and platform length. In this paper, on-board crowding is used as a cost factor and a cost-of-crowding model is developed from supporting psychological research. Two models are proposed and optimized with respect to train length to determine the optimal train and platform length for a many-to-one peak period commuter LRT system. Data from the C-Train network in Calgary, Alberta is used for numerical analysis of the model. The model demonstrated that crowding has an effect on optimal train length. The model produced feasible results when applied to a real-world scenario.

This study analyzes the economic efficiency of the virtual power plant (VPP) model that aims to integrate a number of emergency generators installed at the consumer end and operate them as a single power plant. Several factors such as the demand response benefits from VPP operation and costs incurred for converting emergency generators into VPP are considered to assess the economic efficiency of the proposed VPP model. Scenarios for yearly VPP conversion are prepared based on the installed capacities of the emergency generators distributed in South Korea, while the costs and benefits are calculated from the viewpoints of participants and power companies in accordance with California Test Methods. Furthermore, a sensitivity analysis is conducted on the cost factors among those affecting the economic efficiency of VPP business because these two factors have a great impact on benefits.

The uptake of solar power globally as an important alternative energy source to fossil fuels, together with a rapid fall in the cost of photovoltaic (PV) systems, has been phenomenal during the past decade. This trend is widely anticipated to continue for the years to come. The decline in PV installation costs, like many other new technologies through history, has been largely driven by the learning curve effect. However, it is suggested that other factors, such as costs of key production inputs and prices of competing technologies, also impact the costs of PV systems. In this study, we construct a hierarchical learning curve model to quantify the effects that various factors have on installation costs of PV systems based on empirical data from Taiwan. The results show that, in addition to the learning curve effect as underpinned by an increase of cumulative PV capacity, reductions to silicon price have significantly contributed to the decline of the final installation costs of PV systems in Taiwan. By quantifying the effects of critical cost factors, the learning curve effects on PV installation costs in Taiwan are defined which enable a more accurate projection of PV installation costs for governments, PV producers, operators and users.

A steel plant flow sheet containing a top gas recycling blast furnace is simulated and subjected to multi-objective optimization through an evolutionary approach. A recently proposed k-optimality criterion is used, which allows optimizing a large number of objectives in an evolutionary way, which is difficult to do by other methods. A number of promising optimum results, showing the optimum tradeoffs between several cost factors are identified and analyzed. The results appear to be very significant in the context of CO2 reduction challenges faced by the steel industries today.

Optical access networks are evolving towards next generation solutions offering much higher bandwidth per end point. Moreover, the uninterrupted access to the network services is becoming crucial and therefore operators are now considering protecting their access networks. However, the cost factor is still very important due to the relatively low cost sharing in access segment. For this purpose, this paper proposes an assessment methodology that can be used to compare different protection schemes and help to identify the suitable solution for a given scenario. The assessment criteria includes some reliability measures such as Failure Impact Factor (FIF) and connection availability, as well as cost parameters such as the investment required in greenfield and brownfield scenarios and the increase in power consumption compared to the unprotected network. The proposed criteria have been used to compare 7 representative protection schemes shown in literature, which differ mainly in the number of protected network elements and the technology used for protection (fiber, wireless, etc.). The considered protection schemes have been applied to a hybrid wavelength division multiplexing/time division multiplexing Passive Optical Network (Hybrid PON) architecture in an urban area. It has been shown that it is difficult to identify the absolute best scheme with respect to all the considered criteria. However, depending on the requirements from the operator regarding the targeted reliability performance in the network, an appropriate protection scheme can be recommended for either a greenfield or a brownfield scenario.

This article presents economic models for a new hybrid method where additive manufacturing AM and subtractive methods SMs are integrated through composite process planning. Although AM and SM offer several unique advantages, there are technological limitations such as tolerance and surface finish requirements; tooling and fixturing, etc. that cannot be met by a single type of manufacturing. The intent of this article is not to show a new manufacturing method, but rather to provide economic context to additive and subtractive methods as the best practice provides, and look at the corresponding economics of each of those methods as a function of production batch size, machinability, cost of the material, part geometry and tolerance requirements. Basic models of fixed and variable costs associated with additive, subtractive and hybrid methods to produce parts are also presented. An experimental design is used to study the influence of production volume, material and operating cost, batch size, machinability of the material and impact of reducing AM processing time. A composite response model for the unit cost is computed for the various levels associated with such engineering requirements. The developed models provide insight into how these variables affect the costs associated with engineering a mechanical product that will be produced using AM and SM methods. From the results, it appears that batch size, AM processing time and AM processing cost were the major cost factors. It was shown that the cost of producing ‘near-net’ shape through SM and AM was the decision criteria; which will be critical for tough-to-machine alloys and at multi-batch size.

The materials utilized as a part of aviation applications represent those with high performance, as the aviation industry is particularly materials driven. Continuous development and introduction of new materials in aerospace applications aims at weight reduction, increased fuel efficiency, enhanced performance, and reduced cost. Improvement of engineering materials for aerospace application influences both economic and environmental issues. This chapter on materials selection for aerospace components elucidates the evolution of advanced materials in aerospace industries, as observed through literature available, based on their specific properties and applications. The importance of material selection in aerospace industries, selection of materials based on their mechanical, thermal properties, and cost factors are also discussed. Material selection always involves many criteria; a few such important methods for material selection are also highlighted in this chapter. Recent developments in the field of composites influence aerospace applications, due to high strength-to-weight, thermal resistance, and wear resistance compared to conventional materials. In this chapter, the advantages and disadvantages of newly developed material for aerospace application are also discussed.

BIM is an intelligent model that triggers a different kind of design mindset, conduct and behaviour which emerged as one of the latest moderation of computer-aided technologies being utilized in Architecture, Engineering and Construction (AEC). The demand and global emphasis upon sustainable stressing and eagerness upon efficiency and cost improvements throughout project lifecycle inevitably necessitate BIM to be implemented within AEC industry. This paper therefore elicited an industry wide analysis in BIM adoption in Malaysia. The study aims to review current BIM implementation challenges in Malaysian AEC industry. Quantitative approach is selected for data collection. Questionnaire surveys were distributed to architects, engineers and contractors. The collected data is analyzed through Principal Component Analysis (PCA). The challenges factors are categorized into three (3) categories, culture, business and technology. It is found that cost factor, knowledge factor and stakeholder factor are the major challenges in Malaysia AEC industry. These establish result portrayals will ineluctably serve as a guideline for AEC BIM adopters to develop proper BIM execution planning, with equilibrium established between intended accomplishments and suitable solutions for the tailing predicaments.

Project cost is an important concern in any construction project. Although there has been a lot of studies on factors affecting the cost of construction projects, there seems no consensus as what cost factors have direct influence on the cost of civil engineering projects. This study therefore aims to bridge the current knowledge gap by examining quantity surveyors` perception of the factor structure among nineteen costing attributes identified based on literature review. Questionnaire was used to elicit responses from quantity surveyors working in the Hong Kong construction industry. Principal component analysis is conducted to extract the factor structure of the cost attributes and the attributes are grouped into three factor components, namely the contract management factor, the project management factor and the monetary value factor. Understanding these cost impact factors could be crucial in managing civil engineering projects, since it allows the project stakeholders and quantity surveyors to take precautionary steps to identify the cost management problems and areas for improvement and could even help to avoid cost deviations in engineering projects.

Marginal analysis is the usual approach to environmental economic assessment, for instance, of health-related external costs due to energy-associated air pollutant emissions. However, nonlinearity exists in all steps of their assessment, i.e., atmospheric dispersion, impact assessment, and monetary valuation. Dedicated assessments thus appear necessary when evaluating large systems or their changes such as in green accounting or the implications of economy-wide energy transitions. Corresponding approaches are reviewed. Tools already exist that allow assessing a marginal change (e.g., one power plant's emissions) for different background emission scenarios that merely need to be defined and implemented. When assessing nonmarginal changes, the top-down approach is considered obsolete, and four variants of the bottom-up approach with different application domains were identified. Variants 1 and 2 use precalculated external cost factors with different levels of sophistication, suitable for energy systems modeling, optimizing for social (i.e., private and external) costs. Providing more reliable results due to more detailed modeling, emission sources are assessed individually or jointly in variants 3 and 4, respectively. Aiming at considering nonlinearity more fully and simultaneously following marginal analysis principles, I propose a variant 3-based approach, subdividing an aggregate (i.e., a nonmarginal change) into several smaller changes. Its strengths and drawbacks, notably the associated effort, are discussed.

The main purpose of this study is to investigate the most appropriate transportation by using the fuzzy logic when comparing the road, sea, rail, air and pipe transportation in terms of organizing the selection criteria for the type of transportation. In terms of comparison, the speed, security, capacity, appropriateness as well as cost factors and transportation combinations were used. By blurring the transportation combinations, after organizing the mathematical equation, it was decided on which kind of transportation is the most appropriate by considering the speed, security, capacity, appropriateness and cost factors.When handling with the speed, security, capacity combinations in the comparison of the application and when dealing with the land-rail performance, 2,6388, speed, security, cost combination, when considering land-rail performance, 2,6388, capacity, cost and appropriateness combination, seaway transportation performance, 2,7369 and capacity, appropriateness, security combination, it was observed and seen that pipe line performance is the best transportation combination as 2,6389.It was decided that the combined transportation is the most effective when comparing with the speed, reliability and cost criteria of the combined transportation in accordance with the single transportation.

Purpose – This study aims to deliver an approach of how lightweight robot systems can be used to automate manual processes for higher efficiency, increased process capability and enhanced ergonomics. As a use case, a new collaborative testing system for an automated water leak test was designed using an image processing system utilized by the robot. Design/methodology/approach – The “water leak test” in an automotive final assembly line is often a significant cost factor due to its labour-intensive nature. This is particularly the case for premium car manufacturers as each vehicle is watered and manually inspected for leakage. This paper delivers an approach that optimizes the efficiency and capability of the test process by using a new automated in-line inspection system whereby thermographic images are taken by a lightweight robot system and then processed to locate the leak. Such optimization allows the collaboration of robots and manual labour, which in turn enhances the capability of the process station. Findings – This paper examines the development of a new application for lightweight robotic systems and provides a suitable process whereby the system was optimized regarding technical, ergonomic and safety-related aspects. Research limitations/implications – A new automated testing process in combination with a processing algorithm was developed. A modular system suitable for the integration of human–robot collaboration into the assembly line is presented as well. Practical implications – To optimize and validate the system, it was set up in a true to reality model factory and brought to a prototypical status. Several original equipment manufacturers showed great interest in the system. Feasibility studies for a practical implementation are running at the moment. Social implications – The direct human–robot collaboration allows humans and robots to share the same workspace without strict separation measures, which is a great advantage compared with traditional industrial robots. The workers benefit from a more ergonomic workflow and are relieved from unpleasant, repetitive and burdensome tasks. Originality/value – A lightweight robotic system was implemented in a continuous assembly line as a new area of application for these systems. The automated water leak test gives a practical example of how to enrich the assembly and commissioning lines, which are currently dominated by manual labour, with new technologies. This is necessary to reach a higher efficiency and process capability while maintaining a higher flexibility potential than fully automated systems.

As the transhipment of goods is a major cost factor within multimodal transport there is high potential in optimizing transhipment processes at container terminals and thus to reduce transportation costs. One aspect thereby is to improve efficiency of ports ́ personnel, optionally by using port vehicle simulators for training. The paper ́s aim is to identify the demand for port-transcending vehicle simulator training at container terminals in the Danube Region and to examine the feasibility of its practical implementation. Results suggest high basic interest of container terminals in simulator training. Currently, simulators were mainly operated by crane and reach stacker (simulator) manufacturers. However, caused by the high technical and organizational expenses it is yet not possible or at least not profitable for container terminals itself to offer port-transcending simulator training.

Individualized production, which is a major goal of many high-wage countries, describes a production process in which all elements of a production system are designed in such a way that they enable a high level of product variety at mass production costs. This paper demonstrates recent advances in the individualized production with die-based manufacturing processes, namely high-pressure die casting and plastics profile extrusion. Within these application areas, the chosen approach aiming at individualized production is based on the use of numerical die and process design. The design procedure relies on numerical process simulations based on a nonlinear optimization library and a spline-based geometry kernel. All components interact automatically without requiring user interaction; thus, a completely independent optimization cycle can be achieved. The numerical optimization helps to reduce—or even eliminate—the so far very characteristic manual reworking steps of an original die or process design. These reworking steps are a major cost factor when it comes to individual production. Their abolishment through the presented numerical approaches therefore represents a large step towards the concept of individualized production.

Aiming at improving the efficiency of food cold-chain logistics network, shortening the logistic time of food and reducing the logistics cost of food, this study analyzes the optimization strategy and various cost factors of the supply network of food cold chain and establishes and expands a kind of logistics network model adapting to the food cold-chain logistics. We use an improved genetic algorithm to solve the model and design an effective coding scheme, through the modified adaptive crossover probability and mutation probability, we integrate them into the elitism strategy, which has effectively avoided the prematurity of the algorithm and improved the operation efficiency of the algorithm. In the same instance, compared with the simple genetic algorithm, this study puts forward that the average running time and the average iteration number of the improved genetic algorithm have reduced nearly 50%, which has proved the feasibility and the effectiveness of the model and the algorithm.

Purpose - This paper aims to identify the parameters that influence the cost competitiveness of helicopter services for scheduled passenger transport on a regional area. Design/methodology/approach - The approach used is based on an analysis of demand and on a bi-level model (modal split model - service supply optimization model) optimization. Performance optimization in the supply of helicopter transport services is obtained by a sensitivity analysis on the recognized cost factors. Findings - The identification of the variation margins in the cost factors that might make an helicopter competitive for scheduled passenger services. Research limitations/implications - The inability to consider the totality of the helicopters today on the market due to lack of available data on their cost structure. Practical implications - It is represented by the opportunity of the spread of the helicopter through the strengthening of the competitive position in relation to other transport modes for passenger transport services. Social implications - It is represented by the ability to provide air transport services less expensive on the routes that connect the mainland with small islands, especially during the months when the sea conditions do not allow the naval connection causing the insulation phenomenon. Originality/value - Today, there is a lack of papers dealing with the issue of the performance of the helicopters from the point of view of the transport planner, considering what should be the characteristics of the aircraft to ensure performance to consistent designed services.

From a business perspective, cloud computing has revolutionized the information and communication technology (ICT) industry by offering scalable and on-demand ICT services as well as innovative pricing plans such as pay-per-use and subscription. Considering the economic benefits of cloud computing, cloud-based design and manufacturing (CBDM) has been proposed as a new paradigm in digital manufacturing and design innovation. Although CBDM has the potential to reduce costs associated with high performance computing (HPC) and maintaining ICT infrastructures in the context of engineering design and manufacturing, it is challenging to justify the potential cost savings associated with HPC in the cloud because of the complexity in the cost-benefit analysis of migrating to CBDM. In response, this paper provides important insights into the economics of CBDM by identifying key cost factors and potential pricing models that can influence decision making on whether migrating to the cloud is economically justifiable. Specifically, the cost breakdown of adopting CBDM is presented. The general key benefits are demonstrated using real case studies. In addition, a hypothetical application example is presented to compare costs in CBDM with that of traditional in-house design and manufacturing. Finally, some of the key issues and road blocks are outlined.

Enterprise Resource Planning (ERP) systems are large scale integrated systems covering most of the business processes of an enterprise. ERP projects differ from software projects with customization, modification, integration and data conversion phases. Most of the time effort and time estimations are performed in an ad-hoc fashion in ERP projects and as a result they frequently suffer from time and budget overruns. Although there is no consensus on a methodology to estimate size, effort and cost of ERP projects there are various research studies in the field. The purpose of this paper is to review the literature on effort estimation methods for ERP projects, their validations and limitations. The systematic literature review used online journal indexes between January 2000 and December 2016. Studies focusing on effort estimation for ERP projects were selected. Two reviewers assessed all studies and 41 were shortlisted. In most of the studies, cost factors for ERP projects were investigated and validated. Our findings showed that effort estimation methods have mostly used function points as an input. Validations of these methods were mostly done by using history-based validation approaches.

This paper aims to employ a fuzzy decision-making approach to evaluate risk control options and aid the selection of operational safety strategies in container terminals. The selection of appropriate safety strategies to optimize the Operational Safety Performance (OSP) in container terminals has been a key strategic consideration for many maritime stakeholders including terminal managers, ship-owners, surveyors and safety engineers. The nature of these decisions is complex Multiple Criteria Decision-Making (MCDM) under uncertainty, having ambiguous and incomplete data contained in different quantitative and qualitative forms. To deal with the uncertainty, a hybrid of MCDM models based on fuzzy set theory is proposed in this paper. Analytical Hierarchy Process (AHP) is first used to obtain the weights of all the criteria while the Fuzzy Techniques for Order Preference by Similarity to an Ideal Solution (FTOPSIS) is then introduced to assess the ratings of operational safety strategies to optimize OSP. Finally, a case study is conducted to demonstrate the process of the applied method in practice. Consequently, it proves that the proposed model is capable of modeling safety strategy selection for rational OSP of container terminals through taking into account both risk and cost factors.

Unmanned Aerial System (UAS) technologies have rapidly evolved since the 1990Os. It is estimated that the 2016 market for such systems ($2.6 billion) will quadruple ($10.9 billion) by the year 2025 (Teal Group Corporation 2016). New technological advancements in UAS design, battery duration, GPS navigation capabilities and control reliability, have made possible the development of new low-cost, lightweight aerial systems. These advancements have also generated tremendous interest by academia and industry in the construction domain for different applications such as surveying, safety, and quality, and inspection and documentation. With hundreds of UAS manufacturers available in todayOs market, it is difficult to evaluate the life-cycle cost metrics of the such aerial systems. Wide differences regarding metrics such as aircraft weight, payload capabilities, and battery autonomy, make an objective assessment of expenses a daunting task. This pilot study explores the acquisition cost factors for the comparison of small UASs in the construction domain. As a result, a parametric model is presented to estimate the acquisition costs of small UASs for construction applications.

Context: The research literature on software development projects usually assumes that effort is a good proxy for cost. Practice, however, suggests that there are circumstances in which costs and effort should be distinguished. Objectives: We determine similar-ities and differences between size, effort, cost, duration, and num-ber of defects of software projects. Method: We compare two es-tablished repositories (ISBSG and EBSPM) comprising almost 700 projects from industry. Results: We demonstrate a (log)-linear relation between cost on the one hand, and size, duration and number of defects on the other. This justifies conducting linear regression for cost. We establish that ISBSG is substantially differ-ent from EBSPM, in terms of cost (cheaper) and duration (faster), and the relation between cost and effort. We show that while in ISBSG effort is the most important cost factor, this is not the case in other repositories, such as EBSPM in which size is the dominant factor. Conclusion: Practitioners and researchers alike should be cautious when drawing conclusions from a single repository.

Renewable energy generation has gained more and more importance not only in large power grids, but as well in small islanded microgrids. As fuel transport might be a major cost factor in islanded grids in remote locations, optimal use of the existing generation assets is of major importance. In this paper we want to present an optimal unit commitment approach that is adapted for islanded grids by considering frequency reserve constraints. A special focus in the paper is lying on the ease of configuration of commercial real time optimization solutions.

Testing and debugging automotive cyber physical systems are challenging. Developing and integrating cyber and physical components require extensive testing to ensure reliable and safe releases. One important cost factor in the debugging process is the time required to analyze failures. Since large number of failures usually happen due to a few underlying faults, clustering failures based on the responsible faults helps reduce analysis time. We focus on the software-in-the-loop and hardware-in-the-loop levels of testing where test execution times are high. We devise a methodology for adapting existing clustering techniques to a real context. We augment an existing clustering approach by a method for selecting representative tests. To analyze failures, rather than investigating all failing tests one by one, testers inspect only these representatives. We report on the results of a large scale industrial case study. We ran experiments on ca. 850 KLOC. Results show that utilizing our clustering tool, testers can reduce failure analysis time by more than 80%.

ObjectivesCost-burden analyses usually lack a wider societal perspective. Through the creation of a robust resource use measure (RUM), data gaps on societal costs will be addressed, allowing more accurate estimates of the cost burden of asthma in the UK. ApproachA systematic review, applying an established checklist and data extraction tool, will identify and evaluate those existing methods and measures of generating resource use data. These data, incorporated with focus group data, will inform the design of a new RUM. To be distributed to 2000 participants, randomly selected from GP practices (Swansea & Edinburgh), the Swansea data will be linked and validated with their record held in SAIL (Secure Anonymised Information Linkage). A nested cost burden analysis will be applied, generating an estimate of relevant societal costs. Preliminary resultsA preliminary search of the ‘PubMED’ database generated 181 papers that met the inclusion criteria. Initial analysis shows that a societal perspective is usually lacking from RUMs. A critical appraisal of these measures has identified significant variance in both the quality and content of the questionnaires. Focus groups highlighted a number of cost elements that are not, as yet, discussed in the literature. ConclusionSocietal cost factors are not adequately captured within existing tools, our findings will inform the development of a new RUM which will be piloted and validated according to best practice guidelines. Capturing the societal costs of asthma will allow more accurate estimates of the total costs of asthma in the UK.

The supplier selection problem represents one of the most important components of the supply chain management. This article presents a multiple criteria decision making analysis contributing to the selection of the most convenient supplier in the flexible packaging industry. Due to the fact that in today's supply chain management, the performance of potential suppliers is evaluated against multiple criteria rather than taking into account only the cost factor, the appropriate criteria in the supplier selection have been identified, and the considered variants are assessed, considering the decision maker’s preferences and existing constraints. The variants are ranked in terms of their suitability for selecting a supplier with the use of Electre III method. The results obtained from the simulation experiment suggest that this methodology is a feasible decision support model.

Due to customized products, shorter product life cycles, and unpredictable patterns of demand, the manufacturing industries are faced with stochastic production requirements. The production requirements (product mix and demand) may not be known exactly at the time of designing the manufacturing cell. It is likely that a set of possible production requirements (scenarios) with certain probabilities may be given at the design stage. Though a large number of research works on manufacturing cell have been reported, very few of them have considered random product mix constraint at the design stage. This paper presents a nonlinear mixed-integer mathematical model for the cell formation problem with the uncertainty of the product mix for a single period. The model incorporates real-life parameters such as alternate routing, operation sequence, duplicate machines, uncertain product mix, uncertain product demand, batch size, processing time, machine capacity, and various cost factors. The cost factors considered are machine amortization costs, operating costs, inter-cellular material handling costs, and intra-cellular material handling costs. A consultancy work is carried out for the proposed auto-components manufacturing industry to be located in the suburb of Chennai. In this paper, a solution methodology for best possible cell formation using simulated annealing algorithm is presented and the computational procedure has been illustrated for the case study undertaken.

Managing the products at the end of its intended use and recovery of such used products from the market is gaining significant importance these days due to global environmental concerns, resource reduction, government regulations and economic factors. Reverse logistics networks serve the same. The handling of products, components and materials during the recovery process can be called reverse logistics. Due to the increasing number of heavy vehicles in the country like India, large quantities of used tires are generated every year, and proper disposal of these used tires creates problem in the day-to-day life. Hence recover the value from these used tires in the form of material recovery is gaining importance. The implementation of such recycling system usually requires an appropriate reverse logistics network for choosing the physical locations, facilities, and transportation links to convey the used products from the end customers to the recycling facility. A case study was conducted and traditional reverse logistics networks involved in the used tires focusing quality characteristics has been analyzed in the paper and a new network has been suggested focusing the quality characteristics of material recovered out of used tires through laboratory work and at the same time without compromising the cost factor involved in it. Finally a mathematical model has been developed and optimization of cost subjected to quantity, and time has been done and validated for the decision makers involved in the reverse logistics activities of used tires recycling.

Value stream mapping (VSM) is a widely adopted method for transformation of production environments into a lean operational state. The straightforwardness as well as the completeness, with which processes are analysed and optimised, are key to the success of the method originating from the automotive industry. However, these attributes of the ‘paper and pencil’ approach result in limitations when applying VSM in complex production environments. This research paper targets to overcome these limitations, specifically in production environments involving significant demand variability, complex routing and cost factors which potentially increase with lean implementation. For this purpose, an enhanced VSM method is developed, which utilises discrete event simulation (DES). The method features a feasibility and trade-off analysis which is incorporated into the VSM procedure. A case study covering a process of exhaust gas purification catalyst production is then conducted to test the newly developed method. The ...

This paper analyzes the impact factor, combined with practical engineering experience to extract the main influencing factors, and based on research data on the major beneficial factor for statistical analysis to understand trends from the road cost; selection of multiple linear regression model, the unit cost per lane kilometer as the dependent variable, the cost factor as independent variables, the cost estimate to build the macro-factor model and the cost of each grade highway microeconomic factors estimation model.

>> Dimethyl ether (DME) is a new clean fuel as an environmentally-being energy resources. DME hassimilar characteristics to those of LPG and can be substituted Diesel fuel. KOGAS has investigated and developednew innovative DME synthesis process from synthesis gas with KOGAS's own technologies. KOGAS had finished the construction of 10ton/day DME demonstration plant in 2008, we have established the basic design of commercial plant which can produce 3,000ton/day DME. Specifically, an economic model for a commercial DME project willbe presented. It accounts for all the major cost factors that are considered in a commercial scale project as themodel input for performing cash flow analysis, after which key economic indicators are produced including the internal rate of return (IRR), net present value (NPV). Sensitivity analysis is performed to identify dominant costfactors to the project economics and quantify their impact. The inputs to the economic analysis will be basedon representative cost factors from the commercial-scale design of KOGAS’ direct DME process supplementedby literature data. Case study results will be presented based on recent commercialization projects.

Seagoing vessels have to undergo regular inspections, which are currently performed manually by ship surveyors. The main cost factor in a ship inspection is to provide access to the different areas of the ship, since the surveyor has to be close to the inspected parts, usually within arm's reach, either to perform a visual analysis or to take thickness measurements. The access to the structural elements in cargo holds, e.g., bulkheads, is normally provided by staging or by "cherry-picking" cranes. To make ship inspections safer and more cost-efficient, we have introduced new inspection methods, tools, and systems, which have been evaluated in field trials, particularly focusing on cargo holds. More precisely, two magnetic climbing robots and a micro-aerial vehicle, which are able to assist the surveyor during the inspection, are introduced. Since localization of inspection data is mandatory for the surveyor, we also introduce an external localization system that has been verified in field trials, using a climbing inspection robot. Furthermore, the inspection data collected by the robotic systems are organized and handled by a spatial content management system that enables us to compare the inspection data of one survey with those from another, as well as to document the ship inspection when the robot team is used. Image-based defect detection is addressed by proposing an integrated solution for detecting corrosion and cracks. The systems' performance is reported, as well as conclusions on their usability, all in accordance with the output of field trials performed onboard two different vessels under real inspection conditions.