Modelling a Dual-Objective Optimization Model for Cost Reduction and Disruption Risk Minimization in Automotive Supply Chains

Khalid almadani; Nur Arina Bazilah Aziz

doi:10.11113/mjfas.v20n6.3545

Authors

Khalid almadani Department of Mathematical Sciences, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
Nur Arina Bazilah Aziz ᵃDepartment of Mathematical Sciences, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia ᵇUTM-Centre for Industrial and Applied Mathematics, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/mjfas.v20n6.3545

Keywords:

Dual-objective optimization, automotive supply chains, (NSGA-II), multi-modal transportation, disruption risks.

Abstract

Dual-Objective Optimization model is vital in automotive supply chains (ASC) to emphasize multi-modal transportation under disruption scenarios at minimizing costs and disruption risks. In this context, the study evaluated the hypothetical and real-world data based on the deployment of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to understand the efficacy of incorporating multi-modal transportation to balance cost reduction and risk mitigation. The findings of Dual-Objective Optimization model revealed the model's superiority in identifying cost-effective transportation modes, offering a significant improvement over previous model. This research contributes to the mathematical modelling by providing a comprehensive framework for automotive supply chains, addressing operational efficiency and resilience against disruptions.

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