Optimizing electric vehicle routing for cross-period distribution of multitype scarce resources under hybrid time windows and dynamic priorities

Minghua Zeng; Yiwei Cui; Rong Zhang; Yanbing Wang; Ni Dong; Jin Zhang; Minghua Zeng; Yiwei Cui; Rong Zhang; Yanbing Wang; Ni Dong; Jin Zhang

doi:10.3934/jimo.2026129

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 7: 3533-3561. doi: 10.3934/jimo.2026129

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Research article

Optimizing electric vehicle routing for cross-period distribution of multitype scarce resources under hybrid time windows and dynamic priorities

1.
School of Automotive and Traffic Engineering, Guangdong Polytechnic Normal University, Guangzhou 510665, China
2.
School of Traffic and Transportation Engineering, East China Jiao Tong University, Nanchang 330013, China
3.
Xinjiang Vocational & Technical College of Communications, ürümqi, Xinjiang, China, 831400
4.
School of Transportation and Logistics, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China

Received: 29 January 2026 Revised: 13 June 2026 Accepted: 16 June 2026 Published: 29 June 2026
90B30, 90B50, 90B06

This study investigates the problem of multitype scarce resource distribution using electric vehicles, incorporating practical factors including hybrid time windows, cross-period delivery, and priority-based allocation. We develop a mixed integer programming model to minimize the total distribution costs subject to limits on electric vehicle routing, hybrid time windows, priority, cross-period assignment, load capacity, and battery swapping. A tailored genetic algorithm is designed to solve the proposed model. Numerical experiments demonstrate the model's effectiveness and superiority over benchmark scenarios – including models without priority, demand splitting, battery swapping stations, and region partitioning, as well as intraperiod and soft time windows – in terms of both the distribution cost and demand satisfaction rate. To further balance service fairness across all customers, we introduce an adaptive dynamic priority mechanism based on the cumulative demand satisfaction rate and service start time, and propose a ε-adaptive dynamic priority-based multitype scarce resource distribution model with cross-period hybrid time windows. The results show that the enhanced model significantly improves equity performance while maintaining operational efficiency.
- multitype scarce resources,
- electric vehicle routing,
- hybrid time window,
- cross-period,
- adaptive dynamic priority
Citation: Minghua Zeng, Yiwei Cui, Rong Zhang, Yanbing Wang, Ni Dong, Jin Zhang. Optimizing electric vehicle routing for cross-period distribution of multitype scarce resources under hybrid time windows and dynamic priorities[J]. Journal of Industrial and Management Optimization, 2026, 22(7): 3533-3561. doi: 10.3934/jimo.2026129

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Abstract

This study investigates the problem of multitype scarce resource distribution using electric vehicles, incorporating practical factors including hybrid time windows, cross-period delivery, and priority-based allocation. We develop a mixed integer programming model to minimize the total distribution costs subject to limits on electric vehicle routing, hybrid time windows, priority, cross-period assignment, load capacity, and battery swapping. A tailored genetic algorithm is designed to solve the proposed model. Numerical experiments demonstrate the model's effectiveness and superiority over benchmark scenarios – including models without priority, demand splitting, battery swapping stations, and region partitioning, as well as intraperiod and soft time windows – in terms of both the distribution cost and demand satisfaction rate. To further balance service fairness across all customers, we introduce an adaptive dynamic priority mechanism based on the cumulative demand satisfaction rate and service start time, and propose a ε-adaptive dynamic priority-based multitype scarce resource distribution model with cross-period hybrid time windows. The results show that the enhanced model significantly improves equity performance while maintaining operational efficiency.

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