A novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical uncertainty: a real case study.

Organ transplantation is one of the most complicated and challenging treatments in healthcare systems. Despite the significant medical advancements, many patients die while waiting for organ transplants because of the noticeable differences between organ supply and demand. In the organ transplantation supply chain, organ allocation is the most significant decision during the organ transplantation procedure, and kidney is the most widely transplanted organ. This research presents a novel method for assessing the efficiency and ranking of qualified organ-patient pairs as decision-making units (DMUs) for kidney allocation problem in the existence of COVID-19 pandemic and uncertain medical and logistical data. To achieve this goal, two-stage network data envelopment analysis (DEA) and credibility-based chance constraint programming (CCP) are utilized to develop a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method. The main benefits of the developed method can be summarized as follows: considering internal structures in kidney allocation system, investigating both medical and logistical aspects of the problem, the capability of expanding to other network structures, and unique efficiency decomposition under uncertainty. Moreover, in order to evaluate the validity and applicability of the proposed approach, a validation algorithm utilizing a real case study and different confidence levels is used. Finally, the numerical results indicate that the developed approach outperforms the existing kidney allocation system.

Designing a sustainable supply chain network for producing high-value products from waste glass.

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.