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

Atiya Al-Zuheri and Ilias Vlachos

International Journal of Manufacturing Technology and Management, 2023, vol. 37, issue 1, 1-23

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. (search for similar items in EconPapers)
Date: 2023
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