In this study, the challenges faced by logistics companies in daily route planning were investigated under complex and dynamic operational conditions. Using a Taiwanese logistics company as a real-world case, the problem involved planning dozens of delivery routes each day while complying with fixed primary routes, time windows, vehicle capacity limits, and heterogeneous store demands. To address these challenges, Rule-Based Reasoning and optimization algorithms were utilized to develop an intelligent route planning system. The rule-based component formalized domain-specific operational constraints to ensure feasibility, while Ant Colony Optimization was applied to refine route sequencing within the constrained solution space. Rather than fully replacing manual planning, the proposed hybrid framework was designed as a decision-support tool to provide structured, reproducible reference routes and assist planners in efficiently handling complex real-world logistics settings. The results demonstrated that the proposed framework could substantially reduce planning time while offering consistent and explainable routing baselines to support informed decision-making in practical logistics operations.
Citation: JONG-YIH KUO, TI-FENG HSIEH, HUI-CHI LIN. Optimal route selection for supporting lines in logistics companies[J]. Journal of Industrial and Management Optimization, 2026, 22(5): 2479-2502. doi: 10.3934/jimo.2026091
In this study, the challenges faced by logistics companies in daily route planning were investigated under complex and dynamic operational conditions. Using a Taiwanese logistics company as a real-world case, the problem involved planning dozens of delivery routes each day while complying with fixed primary routes, time windows, vehicle capacity limits, and heterogeneous store demands. To address these challenges, Rule-Based Reasoning and optimization algorithms were utilized to develop an intelligent route planning system. The rule-based component formalized domain-specific operational constraints to ensure feasibility, while Ant Colony Optimization was applied to refine route sequencing within the constrained solution space. Rather than fully replacing manual planning, the proposed hybrid framework was designed as a decision-support tool to provide structured, reproducible reference routes and assist planners in efficiently handling complex real-world logistics settings. The results demonstrated that the proposed framework could substantially reduce planning time while offering consistent and explainable routing baselines to support informed decision-making in practical logistics operations.
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JONG-YIH KUO, TI-FENG HSIEH, HUI-CHI LIN. Optimal route selection for supporting lines in logistics companies[J]. Journal of Industrial and Management Optimization, 2026, 22(5): 2479-2502. doi: 10.3934/jimo.2026091
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