Design and Development of a Multi-Objective Optimization Model for Financial Costs and Delivery Time in the Iranian Supply Chain Using NSGA-II
Keywords:
supply chain management, multi - objective optimization, delivery time, supply chain costs, iran logisticsAbstract
This study aims to design and develop a multi-objective optimization model to simultaneously minimize total financial supply chain costs and delivery time under economic uncertainty conditions in Iran. The research adopts an applied, simulation-based approach, and the model is formulated as a mixed-integer linear programming problem. The supply chain network consists of 15 suppliers, 10 production units, and 20 end customers within a major domestic logistics corridor. The NSGA-II algorithm was implemented in MATLAB, and its parameters were tuned using the Taguchi method. Four scenarios were analyzed: baseline, exchange-rate fluctuation, customs disruption, and sustainability scenario. Model performance was evaluated using Pareto frontier quality indicators such as Hypervolume, Spread, and Generational Distance, alongside paired t-tests and one-way ANOVA. Results indicate that in the baseline scenario, total cost and delivery time were significantly reduced by 28% and 22%, respectively, compared to the traditional approach (p < 0.001). In the sustainability scenario, CO₂ emissions decreased by 18–22%. ANOVA results revealed that demand fluctuation had the largest effect size on model performance. Moreover, NSGA-II significantly outperformed simple GA, ACO, and PSO in terms of Hypervolume value and convergence speed. The proposed model effectively generates a high-quality Pareto frontier, achieving a robust balance between cost reduction and delivery time minimization while demonstrating resilience against economic volatility and operational disruptions. It can therefore serve as a strategic decision-support tool in supply chain management and logistics policy planning.
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Copyright (c) 2025 Fatemeh Ahmari (Author); Seyed Ali Paytakhti Oskouei (Corresponding author); Saeed Anvar Khatibi, Yaghoub Pourkarim (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
