A multi-period multi-product green supply network design problem with price and greenness dependent demands under uncertainty

Abstract

Governmental limitations and customer expectations have increased the focus on sustainability in supply chain network design (SCND). To address this issue, a bi-objective mixed-integer mathematical model is introduced with the aim of simultaneously maximizing supply chain profit and minimizing overall carbon emissions. Additionally, customer demands are considered price- and greenness-sensitive for multiple products, and uncertainty in the production process is estimated by a finite number of scenarios. The Monte Carlo sampling approach is used to produce the initial scenarios, and a heuristic scenario reduction approach is subsequently utilized. Due to the complexity of the model, we develop a hybrid metaheuristic algorithm that embeds variable neighborhood search (VNS) in two genetic algorithms to accelerate the convergence of the algorithm to high-quality solutions. These algorithms are compared to multi-objective particle swarm optimization (MOPSO) with two leader selection procedures. To improve the performance of these algorithms, the response surface method is applied to modulate the algorithm parameters. Finally, several analyses are performed to investigate the efficiency and effectiveness of the proposed approach.