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Global warming and the detrimental impacts of fossil fuels on the environment and human life have led to focus on producing sustainable energy resources for future. One of the renewable and sustainable energy resources is biofuel. Today most of developed countries are focused on producing biofuels to improve economic and environmental opportunities. Because of carbon dioxide and other greenhouse gases emissions during supply chain activities, we present a mathematical model for designing a second generation of sustainable multi stage biofuel supply chain network which contains agricultural zones, bio-refineries and markets with the objective of maximizing the profit. Supply chain costs like growing, harvesting, collecting and agricultural residual storage, transportation of agricultural residual from farms to bio-refineries, producing biofuel in bio-refineries and transportation cost from bio-refineries to markets are also considered. Since considerable amount of Carbon dioxide is emitted during transporting agricultural residual from farms to bio-refineries and transporting biofuel from bio-refineries to markets, four different carbon emission regulations are considered in order to examine the environmental impacts, including: carbon cap, carbon tax, carbon cap-and-trade and carbon offset. Based on the carbon-cap mechanism, the maximum amount of carbon emissions is limited. Under the carbon tax mechanism, for each unit of carbon emission, a tax must be given to the regulations. Regarding a carbon cap-and-trade policy, a carbon cap is imposed on supply chain operations and companies can trade their carbon allowances in the market. Considering a carbon offset mechanism, a company can purchase additional carbon allowances. According to uncertainties in real-world problems, the uncertainty nature of parameters like selling revenue of biofuel, costs of growing, harvesting, collecting agricultural residual, transportation of agricultural residual and biofuel, production biofuel, carbon tax rate and amount of carbon emissions are reflected. A chance constrained fuzzy programming approach is used to deal with uncertain parameters. It is apparent by numerical results that proposed model is so efficient and it can be used in the future to produce and develop second generation biofuel supply chain from agricultural residual by considering the carbon emission regulations.

This study proposes a multi-objective mathematical model to design a natural gas supply chain network with a focus on sustainable development goals. Based on the economic objective of the proposed model, an attempt is made to minimize the whole supply chain costs. The environmental objective minimizes the amount of greenhouse gas emissions, electricity consumption, gas consumption, water consumption, and wastewater generation. The social objective also considers balanced employment, balanced economic development, employee welfare, employee safety, customers cost risk, and operational environment risk. Because parameters in the real world are uncertain and can rarely be estimated precisely, the uncertainty of the model parameters is taken into account. A four-stage solution approach is used to solve the proposed model. In the first step, the best-worst method is used to weight the layers in geographic information system (GIS) software. In the second step, the suggested city gate stations are determined in GIS software. In the third step, a robust possibilistic programming method is used to deal with the uncertainty of the parameters. Finally, in the fourth step, the Torabi–Hassini method is used to transform the multi-objective model into a single-objective one. In order to evaluate the efficiency of the proposed model, a real case study is conducted on Fars Province Gas Company. Numerical results confirm the efficiency of the proposed model, revealing that the uncertainty associated with the model parameters cannot be ignored because system costs are significantly affected under uncertainty. Moreover, the capacity parameter of the suggested city gate station could have a significant effect on system costs while the largest share of supply chain costs is related to transmission fixed costs.

Background Carbon emissions and global warming have increased as a result of population growth and greater usage of fossil fuels. Finding a long-term replacement for fossil fuels, such as biofuels, has become a major problem for energy supply management in recent years. Sustainability must be addressed as a key problem in building biofuel supply chains (BSCs), given the pressing need for societies to limit environmental consequences and promote social responsibility of company activities. Various modeling frameworks have been established so far to design a BSC. At the same time, no research exists that examines both the sustainable development paradigm and the influence of various carbon regulatory policies on the strategic and operational decisions made by BSCs. Methods This study develops a multi-objective, multi-period, multi-echelon BSC from switch grass regarding the economic, environmental and social aspects of sustainability. Four carbon policies are taken into account when assessing the environmental aspect: the carbon cap, the carbon tax, the carbon trade, and the carbon offset. To solve the multi-objective model, the fuzzy interactive programming method is used, and the fuzzy best–worst method is used to weight the social objective components. Results An actual case study in Iran is studied to demonstrate the model’s applicability. Under various carbon policies, different network configurations are obtained based on the location of switch grass resources and installed facilities. Biofuel production and transportation activities account for approximately 28% and 51% of total carbon emissions, respectively, according to numerical results. Furthermore, these activities account for roughly 62% of overall expenses. In the suggested case study, implementing the carbon trade policy reduces carbon emissions by more than 30% while increasing total profit by about 27%. In comparison to other policies, the carbon trade policy has a substantial impact on enhancing social considerations. Overall, the carbon trade policy can greatly improve the economic and environmental components of sustainability without significantly decreasing in the social sustainability. Conclusions The proposed model can assist policymakers and governments in simultaneously optimizing BSC profitability, carbon emission reduction, and social concern.

In this paper, a multiperiod, multiobjective, and multiproduct second-generation biodiesel supply chain network is innovated, taking into account sustainable development and international resources...

This paper aims to design a supply chain network for producing double glazed glass from the recycling of waste glass. All three pillars of sustainability are taken into consideration. The economic objective tries to maximize total profits. The environmental objective considers the energy consumption, the generated waste, the greenhouse gas emission, the water consumption, and the fuel consumption of vehicles. The social objective addresses created job opportunities, the worker safety, the regional development, the worker benefit, and training hours. To solve the model, a two-stage framework based on the group best-worst method and an interactive fuzzy programming approach is developed. The proposed model is validated through a real case study based on waste glass management in the city of Shiraz. It is revealed that when sustainable development goals are approached, a great degree of improvement will be attained in environmental and social aspects without a significant decrease in the economic sustainability. The results also demonstrate that the locations of glass recycling centres are different under economic, environmental, and social pillars, and the proposed model yields an optimal system configuration with a proper satisfaction degree of all objectives. Moreover, applying the proposed solution procedure enables system designers to obtain the most desirable trade-off between different aspects of sustainability.

Designing an efficient supply chain for organ transplant networks which is intimately related to humans’ life plays a primary role in improving the network’s performance. This research is focused on proposing a new multi-period location-allocation modeling approach to make appropriate strategic decisions for designing organ transplant networks under supply and budget uncertainties. To serve this purpose, a bi-objective possibilistic programming model is formulated the aim of which is to maximize network responsiveness and minimize the total cost. A fuzzy goal programming approach is adopted to solve multiple objective function models and control their deviations from the corresponding aspiration levels. As an important contribution of this study, the chance of success of transplantation processes is taken into consideration by proposing appropriate utility functions according to transportation criteria. Moreover, for the purpose of coping with the inherent uncertainty of the input parameters, a possibilistic programming model based on Me measure converted to three optimistic, realistic and pessimistic models is developed. Three new formulations have also been developed to tackle equality chance constraints. Finally, the optimal solutions of the developed models are analyzed through conducting a real case study in Iran. According to the results, for the considered organ transplant network, the possibilistic programming model based on the realistic measure is better than the optimistic and pessimistic measure in most confidence levels.

Competitive location deals with the problem of locating new facilities to provide a service (or goods) to the customers of a given geographical area where other facilities (competitors) offering the same service are already present. The new facilities will have to compete with the existing facilities for capturing the market share. This paper proposes a new model in which a new entering firm desires to obtain a specific percentage of the market share in such a way as to minimize the total costs. The total costs consist of costs earmarked for installing the new located facilities and traveling as well as the waiting costs of the customers who receive service from the facilities. Two heuristics based on genetic algorithm and tabu search are proposed to solve the model. A number of numerical examples are designed and solved in order to assess the performance of the proposed heuristics.

This paper proposes a novel reliable hierarchical location-allocation model addressing a real-world health service network design problem. The model considers several key issues regarding health service network designs such as hierarchical structure of networks characterised by a two-level multi-flow hierarchy with service referral, uncertainty associated with demand, service and geographical accessibility, prioritizing patients based on their urgency and adopting different service strategies to serve them, service quality reflected in the patients’ expected waiting time by considering the priority queuing system, and risk of unexpected disruptive events. To deal with different sources of uncertainty in the concerned problem, a robust scenario-based stochastic programming approach is employed. To solve the proposed model, a Benders decomposition algorithm enhanced by several accelerating methods is developed. A practical case study is presented to illustrate the applicability of the proposed model as well as the effectiveness of the designed solution procedure.

This study addresses a novel two-stage possibilistic-stochastic programming model for designing a reliable municipal solid waste management system (MSWMS) in the presence of random disruptions arising from unexpected natural and man-made hazards and imprecision arising from the lack of knowledge about some input parameters. To design a profit-maximizing and environment-friendly MSWMS, an integrated system is worked out to provide revenues through producing renewable energy, fertilizers, and recycled products from processing municipal solid wastes. To solve the proposed model in large-scale instances, an L-shaped method is designed in which several enhancement strategies such as valid inequalities, local branching, and primal heuristics are developed to expedite the convergence rate. The proposed model is validated through a real case study of the MSWMS in the city of Shiraz, Iran. Compared with traditional models with the consideration of only normal disruption-free conditions, the expected profit of the proposed model drops down slightly but the system will hedge against the risk of different disruptions. The optimal network configuration is strongly affected by disruptive conditions such that the number of constructed facilities is increased and they are moved to more reliable locations. Moreover, when designing reliable MSWMSs from economic, environmental, and risk perspectives is approached, the system will incur more costs. The proposed solution procedure also proves to be effective in finding good quality solutions within a short running time for large-scale instances that are far beyond the reach of CPLEX.

Flexible manufacturing system (FMS) consists of some multi-functional machines that are linked together through a material-handling system and the whole of the system is controlled by a central computer. One of the most important problems in production planning of FMS is machine–tool selection and operation allocation problem that directly influences the production costs and time. This paper presents a 0-1 integer linear programming model for the multi-objective machine–tool selection and operation allocation problem in FMS. The proposed model attempts to simultaneously minimize machining cost, material-handling cost, setup cost, and maximum machine work load time and tool life. We also consider the constraints of tool magazine capacity, tool life, and machine capacity in the problem. Due to the nondeterministic polynomial (NP)-hard nature of the derived problem, a genetic algorithm is developed to solve the model. Computational results indicate the effectiveness of the proposed algorithm for solving the problem.

Competitive location deals with the problem of locating new facilities to provide a service (or goods) to the customers of a given geographical area where other facilities (competitors) offering the same service are already present. The new facilities will have to compete with the existing facilities for capturing the market share. This paper proposes a new model in which a new entering firm desires to obtain a specific percentage of the market share in such a way as to minimize the total costs. The total costs consist of costs earmarked for installing the new located facilities and traveling as well as the waiting costs of the customers who receive service from the facilities. Two heuristics based on genetic algorithm and tabu search are proposed to solve the model. A number of numerical examples are designed and solved in order to assess the performance of the proposed heuristics.