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This paper aims to deal with a real-life strategic conflict in joint operations (JOs) for facility location decision and planning in an oil and gas field that stretches over two countries and tries to develop a basis for mitigating such conflict.

This paper develops a novel approach using integer linear programming (ILP) to determine optimal facility location considering technical, economic and environmental factors. Strategic decision-making in JOs is also influenced by business priorities of individual partner, sociopolitical issues and other covert factors. The cost-related quantitative factors are normalized using inverse normalization function as these are to be minimized, and qualitative factors that are multi-decision-making criteria are maximized, thus transforming both qualitative and quantitative factors as a single objective of maximization in ILP model.

The model identifies the most suitable facility location based on a wide range of factors that would provide maximum benefit in the long term, which will help decision-makers and managers.

The model can be expanded incorporating other quantitative and qualitative factors such as tax incentives by the government, local bodies and government regulations.

The applicability of the model is not limited to JOs or oil/gas field, but is applicable to a wide range of sectors.

The model is transparent and based on rational and scientific basis, which would help in building consensus among the dissenting parties and aid in mitigating strategic conflict. Such type of model for mitigating strategic conflict has not been reported/used before.

The purpose of this paper is to investigate the strategic role of plants, in terms of the type and level of site competence, the relationship with the strategic reason for location, and the impact on operational performance.

The authors use a survey of 103 Swedish manufacturing plants that belong to global production networks and analyze patterns within this context to identify potential archetypes of plants with respect to plant roles, based on factor analysis and cluster analysis.

It is found that the areas of site competence can be grouped into three bundles, characterized thematically as production‐related, supply chain‐related and development‐related. The plants fall into three categories: some plants have only production‐related competences, some have competences concerning both production and supply chain, and the third group of plants possesses all three bundles of competences.

The results provide empirical evidence that site competences come in bundles in three steps according to themes rather than individually. No significant relationship was found between the level of site competence and the strategic reason for site location.

The results provide empirical support for the co‐location of product development and production, since plants with full responsibility for all competence bundles significantly outperform plants having only production‐related competences on cost efficiency, quality, and new product introductions.

The authors research patterns of site competence at a more detailed level than before in the related literature, as well as study the impact on performance, which has not been done before.

Corn ethanol plants consume large amounts of corn and their location has the potential to alter local crop prices and surrounding agricultural land values. The purpose of this paper is to analyze the local economic impact of ethanol plant locations on farmland values.

The relationship between ethanol plant location and agricultural land prices is examined using data obtained from the Agricultural Credit Survey administered by the Federal Reserve Bank of Kansas City.

The findings indicate that ethanol plant location has had an impact on land values. The portion of land price changes attributable to location is consistent with previous estimates of basis changes associated with ethanol plant location.

The paper finds that land markets appear to be rationally adjusting to the location of ethanol plants.

Biomass waste can be used as fuel in biomass power plants to generate electricity. It is a type of renewable energy widely available in Malaysia because 12 million tons of the biomass waste is produced every year. At present, only 5 per cent of the total biomass waste in Sabah, one of the states in Malaysia, is used to generate electricity for on-site consumption. The remaining 95 per cent of the biomass waste has not been utilized because the transportation cost for shifting the waste from the plantations to the power plants is substantial, hence making the cost of the biomass generated electricity to be high. Therefore, a methodology is developed and presented in this paper to determine the optimum geographic distribution and capacities of the biomass power plants around a region so that the cost of biomass generated electricity can be minimized. The paper aims to discuss these issues.

The methodology is able to identify the potential locations of biomass power plants on any locations on a region taking into account the operation and capital costs of the power plants as well as the cost of connecting the power plants to the national grid. The methodology is programmed using Fortran.

This methodology is applied to Sabah using the real data. The results generated from the methodology show the best locations and capacities of biomass power plants in Sabah. There are 20 locations suitable for biomass power plants. The total capacity of these biomass power plants is 4,996 MW with an annual generation of 35,013 GWh. This is sufficient to meet all the electricity demand in Sabah up to 2030.

The methodology is an effective tool to determine the best geographic locations and sizes of the biomass power plants around a region.

This chapter proposes a hybrid heuristic method combining a clustering search (CS) metaheuristic with an exact algorithm to solve a two-stage capacitated facility location problem (TSCFLP). The TSCFLP consists of defining the optimal locations of plants and depots and the product flow from plants to depots (first stage) and from depots to customers (second stage). The problem deals commonly with cargo transportation in which products must be transported from a set of plants to meet customers’ demands passing out by intermediate depots. The main decisions to be made are related to define which plants and depots must be opened from a given set of potential locations, which customer to assign to each one of the opened depots, and the amount of product flow from the plants to the depots and from the depots to the customers. The objective is to minimize costs satisfying demand and capacity constraints. Computational results demonstrate that our method was able to find good solutions when comparing it directly with a commercial solver and a genetic algorithm (GA) reported in a recent chapter found in the literature, requiring less than 1.5% and 41% of the computational time performed by these methods, respectively. Thus, our hybrid method combining CS with an exact algorithm can be considered as a new matheuristic to solve the TSCFLP.

Increased demand for new batteries and strict government protocols have stressed the battery industries to collect and recycle used batteries for economic and environmental benefits. This scenario has forced the battery industries to collect used batteries and establish the formal battery recycling plant (BRP) for effective recycling. The starting of BRP includes several strategic decisions, one of the most critical decisions encountered is to find the best sustainable location for BRP. Hence, this paper aims to address the complexity of the issues faced during the BRP location selection through a hybrid framework.

In this study, the criteria are identified under socio-cultural, technical, environmental, economic and policy and legal (STEEP) dimensions through literature review and experts' opinions. Then, the hybrid methodology integrating fuzzy decision making trial and evaluation laboratory (DEMATEL), best worst method (BWM) and technique for order preference by similarity to an ideal solution (TOPSIS) has been proposed to find the inter-relationship between criteria, the weights of criteria and the best alternative.

The identified five main criteria and 26 sub-criteria have been analyzed through fuzzy DEMATEL, and found that the policy and legal criteria have more inter-relationship with other criteria. Then from BWM results, it is found that the support from government bodies has attained the maximum weightage. Finally, the second alternative has been identified as a more suitable location for establishing BRP using TOPSIS. Further, it is found from the results that the support from government bodies, the impact of emissions, availability of basic facilities and community health are the essential criteria under STEEP dimensions for establishing BRP.

In addition to the various existing sustainable criteria, this study has also considered a set of policy and legal criteria for the evaluation of locations for BRP. Further, the hybrid MCDM method has been proposed in this study for selecting the best alternative. Thus, this study has yielded more insights to the decision-makers in choosing a sustainable location for BRP.

Facility location and re-location decisions are critical managerial decisions in modern supply chains. Such decisions are difficult in this environment as managers encounter uncertainty and risks. The study investigates establishing or moving distribution facilities in the global supply chain by considering costs, fulfilment, trade uncertainties, risks under environmental trade-offs and disruptive technologies.

This paper combines the possibilities and probabilistic scenarios for a supply chain network by proposing the novel Robust Optimisation and Mixed Integer Linear Programming (ROMILP) method developed under the potential uncertainty of demand while considering the costs associated with a four-tier supply chain network. ROMILP has been solved in a real-time logistics environment by applying a case study approach.

The solution is obtained using an exact solution approach and provides optimality in all tested market scenarios along the proposed global logistics corridor. A sensitivity analysis examines potential facility location scenarios in a global supply chain context.

Logistics managers can apply the ROMILP model to test the cost-benefit trade-offs against their facility location and relocation decisions while operating under uncertainty. Future research is proposed to extend the literature by applying data from the OBOR logistics corridor.

This study is the first to examine sustainable dimensions along the global logistics corridor and investigate the global container traffic perspective. The study also adds value to the Middle East logistics corridor regarding facility location decisions.

The paper aims at the bi‐objective optimization of a two‐echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the distribution centers (DCs) to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met.

To optimize the two objectives simultaneously, the location and distribution two‐echelon network model is mathematically represented in this paper considering the associated constraints, capacity, production and shipment costs and solved using hybrid multi‐objective particle swarm optimization (MOPSO) algorithm.

This paper shows that the heuristic based hybrid MOPSO algorithm can be used as an optimizer for characterizing the Pareto optimal front by computing well‐distributed non‐dominated solutions. These aolutions represent trade‐off solutions out of which an appropriate solution can be chosen according to industrial requirement.

Very few applications of hybrid MOPSO are mentioned in literature in the area of supply chain management. This paper addresses one of such applications.

The purpose of this paper is to allow environmental policy makers to identify the sort of problems and obstacles and the kind of influences that firms face from different economic actors, when trying to improve their environmental performance. It aims to identify the actions that are taken by firms to cope with more difficult environmental regulations. These will help the regulator in the design of strategies to foster environmental improvements by firms.

This paper analyses some of the specific questions of an environmental management survey that was carried out in the manufacturing industry in Chile in 2001. A large number of variables from the survey were involved and they are hard to handle in such disaggregated terms. The factor analysis (FA) methodology is thus applied to reduce the information to a manageable number of variables.

The results of the FA methodology provide the regulator with fewer dimensions to concentrate on when designing environmental strategies, while they also provide an insight into each general area of concern. The indices of environmental performance that are developed in the paper contribute to the targeting of policy recommendations, as they allow comparisons of the levels of environmental performance between different sorts of manufacturing plants.

This paper helps to determine patterns of environmental performance in manufacturing firms, which are very helpful for environmental policy makers when designing strategies to foster environmental improvements by firms, particularly when countries are facing budget constraints. This case study could also be illustrative for other countries with similar characteristics.

The approach used in this paper allows environmental policy makers to count on certain indicators to efficiently target their environmental policies.

The location‐allocation problem involves multiple shipping destinations, with known demands for a given product and known transportation costs from sources to destinations. The problem is to determine the number of facilities and their locations in order to best service the shipping destinations. This paper presents an approach to facility location which allows the analysis of multiple conflicting goals as an extension of previous solution approaches. Specifically, the paper applies the branch and bound integer goal programming approach to the location‐allocation problem.