Optimization of multi-plant capacitated lot-sizing problems in an integrated supply chain network using calibrated metaheuristic algorithms

Research output: Contribution to journalArticleScientificpeer-review


Research units

  • University of Malaya


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 centers 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 optimization 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.


Original languageEnglish
Article number39(3)
Pages (from-to)325-363
Number of pages39
JournalInternational Journal of Operational Research
Issue number3
Publication statusPublished - May 2020
MoE publication typeA1 Journal article-refereed

    Research areas

  • capacitated lot-sizing, multi-plant, production and distribution planning, integrated supply chain, optimization, metaheuristic algorithms, genetic algorithm, particle swarm optimization, imperialist competitive algorithm

ID: 27096318