Supply Chain Optimization Using Chaotic Differential Evolution Method

This paper describes the application of differential evolution approaches to the optimization of a supply chain. Although simplified, this supply chain included stocks, production, transportation and distribution, in an integrated production-inventory-distribution system. The supply chain problem model is presented as well as a short introduction to each evolutionary algorithm. Differential evolution (DE) is an emergent evolutionary algorithm that offers three major advantages: it finds the global minimum regardless of the initial parameter values, it involves fast convergence, and it uses few control parameters. Inspired by the chaos theory, this work presents a new global optimization algorithm based on different DE approaches combined with chaotic sequences (DEC), called chaotic differential evolution algorithm. The performance of three evolutionary algorithm approaches (genetic algorithm, DE and DEC) and branch and bound method were evaluated with numerical simulations. Results were also compared with other similar approach in the literature. DEC was the algorithm that led to better results, outperforming previously published solutions. The simplicity and robustness of evolutionary algorithms in general, and the efficiency of DEC, in particular, suggest their great utility for the supply chain optimization problem, as well as other logistics-related problems.