Title: A comparative study on the application of evolutionary algorithms to multi-objective, multi-stage supply chain network design
Authors: Ferdous Sarwar; Muhammad Adib Uz Zaman
Addresses: Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh ' Department of Industrial and Systems Engineering, Northern Illinois University, De Kalb, Illinois 60115, USA
Abstract: This paper deals with a multi-objective, multi-stage supply chain network formulation and application of various evolutionary algorithms like particle swarm optimisation (PSO) and genetic algorithm (GA) to solve the formulated problem. As we all know, in recent days, the supply chain network tends to be very complex with lots of suppliers and customers in the value chain. The aim of this paper is to establish a way to optimise a complex multi-stage supply chain network by minimising both costs and lead time under some given constraints while applying both PSO and GA techniques to find out the best solutions available by comparing them. The algorithm techniques were modified to obtain the desired solutions while keeping the entire parameters standard. A numerical real-life example was introduced to check the validity of our assumptions and effectiveness of the techniques used in the paper. Mainly, the Pareto optimal solutions were compared.
Keywords: evolutionary algorithm; particle swarm optimisation; PSO; genetic algorithm; supply chain; multi-objective; optimisation; multi-stage; Pareto-optimal.
International Journal of Supply Chain and Inventory Management, 2017 Vol.2 No.2, pp.143 - 161
Accepted: 01 Feb 2018
Published online: 30 May 2018 *