Sensitivity Analysis and Validation of a Multi-Objective Optimization Model for Financial Costs and Delivery Time in Iran’s Logistics Supply Chain Using the Non-Dominated Sorting Genetic Algorithm II
Keywords:
Sensitivity analysis, model validation, one-way ANOVA, Monte Carlo simulation, Pareto front, algorithm comparison, case studies, non-dominated genetic algorithmAbstract
This study examines the sensitivity analysis and validation of a proposed multi-objective optimization model aimed at simultaneously minimizing the total financial costs of the supply chain (transportation, warehousing, production, and delay penalties) and delivery time. The model was tested using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) with real-world data from a network comprising fifteen suppliers, ten production facilities, and twenty end customers along the Tehran–Isfahan–Bandar Abbas corridor. Sensitivity analysis using one-way analysis of variance (ANOVA) indicated that demand (effect size = 0.32), exchange rate (0.28), customs delay (0.25), and fuel price (0.22) exerted the greatest influence on model outputs (p < .001 for all factors). Monte Carlo simulation with 1,000 iterations reduced cost variance to below five percent and decreased the probability of disruption risk (delivery time exceeding 300 hours) from 32 percent to 7 percent. The Pareto front yielded 42 to 48 dominant solutions with a mean hypervolume of 0.73 (SD = 0.02), dispersion of 0.19, and generational distance of 0.04. Comparison of the proposed algorithm with the simple genetic algorithm, ant colony optimization, and particle swarm optimization confirmed its superiority in front quality and convergence speed (30–35 percent). Case studies in Iran Khodro, Digikala, and Petropars demonstrated improvements ranging from 14 to 61 percent in cost, time, and carbon emissions. Paired t-tests and benchmarking with reliability test functions confirmed the robustness of the model.
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Copyright (c) 2025 Fatemeh Ahmari (Author); Seyed Ali Paytakhti Oskoui (Corresponding author); Saeed Anwar Khatibi, Yaghoub Pourkarim (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
