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An integrated optimization model for mixed-fleet vehicle routing considering cost and service performance |
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| รหัสดีโอไอ | |
| Title | An integrated optimization model for mixed-fleet vehicle routing considering cost and service performance |
| Creator | Phumiphat Loo-areesuwan |
| Contributor | Sun Olapiriyakul, Advisor |
| Publisher | Thammasat University |
| Publication Year | 2568 |
| Keyword | Mixed-fleet vehicle routing, Electric vehicles, Logistics optimization, MILP, Sustainable logistics, Last-mile delivery, Customer satisfaction |
| Abstract | Efficient transportation planning has become increasingly important in modern logistics systems due to rising operational costs, growing environmental concerns, and increasing customer expectations for reliable delivery services. Logistics companies are increasingly adopting electric vehicles as part of their fleets to reduce carbon emissions and support sustainable transportation initiatives. However, electric vehicles introduce operational constraints such as limited driving range and battery capacity, which increase the complexity of routing and fleet management decisions. At the same time, logistics providers must ensure high levels of service performance, particularly in terms of on-time delivery and customer satisfaction. These challenges highlight the need for optimization models that can simultaneously address cost efficiency and service quality in mixed-fleet logistics operations.This study develops an integrated mixed-fleet vehicle routing optimization model that considers delivery operations using both electric and diesel vehicles. The problem is formulated as a mixed-integer linear programming (MILP) model that incorporates vehicle capacity constraints, electric vehicle range limitations, anddelivery time considerations. The model integrates cost and customer satisfaction into a unified objective function to analyze the trade-off between operational efficiency and service performance. The optimization model is implemented using Python and solved using an optimization solver to evaluate routing strategies under different parameter settings. The results provide insights into how logistics planners can improve routing efficiency while maintaining high service levels. This research contributes to the development of advanced vehicle routing models and provides practical decision-support tools for sustainable and efficient logistics planning. |
