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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.

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.

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