Title: A genetic algorithm-based optimisation model for designing an efficient, sustainable supply chain network under disruption risks

Authors: Atiya Al-Zuheri; Ilias Vlachos

Addresses: Department of Production Engineering and Metallurgy, University of Technology, Baghdad 10066, Iraq ' Excelia Group, Excelia Business School, 102 Rue de Coureilles, 17000 La Rochelle, France

Abstract: Existing supply chain designs focus on efficiency and cost minimisation, particularly in just-in-time (JIT) systems. At the same time, sustainability requires designs that preserve resources and minimise environmental impact; thus, companies should design their supply chains to be simultaneously flexible, sustainable, and efficient. This study proposes a genetic algorithm-based optimisation model to address the trade-off between the total supply cost and the carbon emission cost during supply network disruption. The model is tested using a case study to validate its applicability using the particle swarm optimisation (PSO) approach. A number of factors are analysed: lead time, order quantity variance, and transportation mode selection. Performance variables include the total supply chain cost which comprises production, transportation, and CO₂ costs. The model has many opportunities for application where the supply chain is disrupted, such as in the recent pandemic, especially when companies do not want to compromise efficiency and sustainability.

Keywords: genetic algorithm; optimisation model; supply chain design; resilience; sustainability; efficiency; disruptions; carbon tax; just-in-time; JIT; particle swarm optimisation; PSO.

DOI: 10.1504/IJMTM.2023.131021

International Journal of Manufacturing Technology and Management, 2023 Vol.37 No.1, pp.1 - 23

Accepted: 04 Nov 2020
Published online: 19 May 2023 *