Addressing the multi-objective Stochastic Closed-loop Supply Chain via two Metaheuristic Approaches: RDA and GA

With the increasing emphasis on supply chain network design (SCND) vulnerabilities, effective mathematical tools for analyzing and understanding appropriate supply chain by risk management are now attracting much attention. The aim of this paper is generated a robust closed-loop supply chain network with the metaheuristic and exact solution planning. Moreover, the downside risk is incorporated into the objective functions of the two-stage stochastic programming model as a risk measure. Hence, the developed stochastic model aims to minimize the expected total cost and the downside risk, simultaneously. In order to address the problem, Red Deer Algorithm (RDA) as one of the powerful recent algorithms and Genetic algorithm (GA) as a well-known metaheuristic are utilized in this study. In addition, the parameters of algorithms are tuned by Response Surface Method (RSM) with an MODM approach to estimate the proper values of the parameters for presented metaheuristic algorithms to improve their performance. To explain the efficiency and effectiveness of methods, four metrics are introduced. At the end, the results show that the ICA achieves the better solution through the most of tests problem.

Keywords:

Two-stage stochastic programming , Closed-loop supply chain , Risk management , Red Deer algorithm , Genetic algorithm