Title: Optimisation of multi-plant capacitated lot-sizing problems in an integrated supply chain network using calibrated metaheuristic algorithms
Authors: Maryam Mohammadi; Siti Nurmaya Musa; Mohd Bin Omar
Addresses: Process Control and Automation Research Group, School of Chemical Technology, Aalto University, Espoo, Finland ' Centre of Advanced Manufacturing and Material Processing; Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia ' Institute of Mathematical Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
Abstract: In this paper, a mathematical model for a multi-item multi-period capacitated lot-sizing problem in an integrated supply chain network composed of multiple suppliers, plants and distribution centres is developed. The combinations of several functions such as purchasing, production, storage, backordering and transportation are considered. The objective is to simultaneously determine the optimal raw material order quantity, production and inventory levels, and the transportation amount, so that the demand can be satisfied with the lowest possible cost. Transfer decisions between plants are made when demand at a plant can be fulfilled by other production sites to cope with the under-capacity and stock-out problems of that plant. Since the proposed model is NP-hard, a genetic algorithm is used to solve the model. To validate the results, particle swarm optimisation and imperialist competitive algorithm are applied to solve the model as well. The results show that genetic algorithm offers better solution compared to other algorithms.
Keywords: capacitated lot-sizing; multi-plant; production and distribution planning; integrated supply chain; optimisation; metaheuristic algorithms; genetic algorithm; GA; particle swarm optimisation; PSO; imperialist competitive algorithm; ICA.
International Journal of Operational Research, 2020 Vol.39 No.3, pp.325 - 363
Received: 08 May 2017
Accepted: 03 Feb 2018
Published online: 07 May 2020 *