Risk-averse two-stage stochastic programming-based closed-loop supply chain network design under uncertain demand

Abstract

In recent years, there has been a growing awareness and acceptance of the concept of green environmental protection among the public, primarily due to the worsening environmental issues and increased emphasis by governments on corporate social responsibility regulations. As a result, the management of closed-loop supply chains has gained significant attention from both businesses and researchers. Most stochastic programming models addressing closed-loop supply chain (CLSC) under uncertainty are typically designed to be risk-neutral. In this paper, a risk-averse two-stage stochastic programming (RATSSP) model for the closed-loop supply chain network design (CLSCND) problem considering facility capacity level decisions under uncertain demand. It is aiming to trade off the expected total profit and the profit risk, based on a given degree of responsive risk aversion. Due to the NP-hard nature of the CLSCND problem, this paper applies simplified swarm optimization (SSO), genetic algorithm (GA), and particle swarm optimization (PSO) algorithms to solve the problem. To improve the efficiency and quality of the obtained solutions, we propose a parallel computation meta-heuristic algorithm, parallel simplified swarm optimization (PSSO), along with parallel versions of PSO and GA. Computational results show the effectiveness of our proposed model, with PSSO demonstrating the best robustness and solution quality among the considered algorithms.