A Meta-Heuristic Algorithm Based on Simulated Annealing for Designing Multi-Objective Supply Chain Systems

This paper proposes a new solution based on tuned-parameter simulated annealing algorithm to obtain near-optimum solutions for solving large multi-objective multi-product supply chain design problem. The selected objective functions are: maximize the total profit, minimize the total supply chain risk, and minimize the supply chain emissions. The characteristics of the algorithm are developed and presented, then coded and tested. Since there is no benchmark available in the existing and state-of-the-art papers, the results acquired by the developed algorithm are compared with the results obtained by an improved augmented $\varepsilon$-constraint algorithm embedded in the General Algebraic Modeling System (GAMS) software for small-scale, medium-scale, and large-scale instances of the multi-objective supply chain problem. The results indicate that the developed simulated annealing algorithm is able to obtain acceptable solutions with reasonable computational time.