A Social Engineering Optimizer Algorithm for a Closed-Loop Supply Chain System with Uncertain Demand

This paper presents a new model for a closed-loop supply chain problem under uncertainty. This model considers production, distribution, collection, recycling and disposal of items simultaneously. Because of the increased importance of the environmental factors, this model focuses on the revers flow of the supply chain and considers different types of technology for recycling centers. The model aims to minimize the establishment cost of centers, shipment cost, holding cost, collection cost and recycling cost. To face with uncertain parameters, a credibility-based possibilistic programming method is applied. Then, a social engineering optimizer algorithm is proposed to solve the problem efficiency. To validate the model and proposed algorithm, the results are compared with the results of GAMS. In addition, they prove the superiority of the proposed algorithm over a genetic algorithm to deal with problems and find better results in less running time. Finally, the behavior of the model is assessed by changing the values of parameters and the results are reported.