Robust possibilistic programming to design a closed-loop blood supply chain network considering service-level maximization and lateral resupply.

Reconfiguring the structure of the supply chain network is one of the most strategic and vital decisions in designing a supply chain network. In this study, a Closed-Loop Blood Supply Chain Network (CLBSCN) considering blood group compatibility, ABO-Rh(D), and blood product shelf life has been studied to determine the best strategic and tactical decisions simultaneously considering lateral resupply/transshipment and service-level maximization. Several vital parameters, including supply and demand, are considered fuzzy numbers to approximate reality due to the nature of the world.

Designing a sustainable closed-loop supply chain network considering lateral resupply and backup suppliers using fuzzy inference system.

Sustainability is key factor for transforming traditional supply chain networks into modern ones. This study, for the first time, considers the impacts of the backup suppliers and lateral transshipment/resupply simultaneously on designing a Sustainable Closed-Loop Supply Chain Network (SCLSCN) to decrease the shortage that may occur during the transmission of produced goods in the network. In this manner, the fuzzy multi-objective mixed-integer linear programming model is proposed to design an efficient SCLSCN resiliently.

Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm.

Demand plays a vital role in designing every closed-loop supply chain network in today's world. The flow of materials and commodities in the opposite direction of the standard supply chain is inevitable. In this way, this study addresses a new multi-echelon multi-period closed-loop supply chain network to minimize the total costs of the network.