Title: A Pareto-based optimisation algorithm for a multi-objective integrated production-distribution planning problem of a multi-echelon supply chain network design
Authors: Keyvan Sarrafha; Abolfazl Kazemi; Alireza Alinezhad; Seyed Taghi Akhavan Niaki
Addresses: Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran ' Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran ' Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran ' Department of Industrial Engineering, Sharif University of Technology, P.O. Box 11155-9414 Azadi Ave., Tehran 1458889694, Iran
Abstract: This paper addresses a multi-periodic supply chain network design (SCND) problem involving suppliers, manufacturers, distribution centres (DCs), and customer zones (CZs). Logistic decisions made in each time period have a tactical nature. Location/strategic decisions are made at the beginning of the time horizon and remain unchanged until the last period. While both backorders and lost sales are considered, the aim is to design the supply chain network (SCN) under three minimisation objectives including total costs, the transfer time of products to CZs, and backorder level and lost sale of products. A multi-objective mixed-integer linear programming (MILP) model is developed and a meta-heuristic algorithm named multi-objective vibration damping optimisation (MOVDO) with tuned parameters is proposed to find non-dominated solutions. The performance of this method is compared with two popular existing algorithms called NSGA-II and NRGA when they solve some randomly generated problems.
Keywords: supply chain network design; SCND; integrated production-distribution planning; production-distribution planning; PDP; multi-objective vibration damping optimisation; MOVDO; NSGA-II; non-dominated ranking genetic algorithm; NRGA; Taguchi method.
International Journal of Services and Operations Management, 2021 Vol.38 No.1, pp.40 - 72
Received: 16 Sep 2018
Accepted: 19 Nov 2018
Published online: 20 Jan 2021 *