A Particle Swarm Optimization Approach for Solar Cell Industry Multi-stage Closed-Loop Supply Chain Model

Abstract

This chapter presents the study of an integrated forward and reverse closed-loop supply chain (CLSC) network design problem with sustainable concerns in the solar energy industry. The focuses are in the logistics flows, capacity expansion, and technology investments of existing and potential facilities in the multi-stage CLSC. A deterministic multi-objective mixed integer programming model is formulated to capture the tradeoffs between the total cost and the carbon dioxide (CO2) emission and to tackle the multi-stage CLSC design problem from both economic and environmental perspectives. Due to the multi-objective nature and the computational complexity, a multi-objective particle swarm optimization (MOPSO) with novel flow assignment algorithms is designed to search for non-dominated/Pareto CLSC design solutions. Finally, a case study of crystalline solar energy industry is illustrated to verify the proposed multi-objective CLSC design model and demonstrate the efficiency of the developed MOPSO algorithm in terms of computational time and solution quality.