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Genetic programming-assisted automatic heuristics design for dynamic vehicle routing with drones considering time window
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DOI：10.1007/s12293-026-00523-4.png)
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Abstract
En 中文
Integrating trucks and drones into package delivery offers a promising path toward a future logistics system that is more efficient and sustainable than current methods. However, coordinating trucks and drones under uncertain traffic conditions, particularly variable travel times, remains a critical challenge, with limited research addressing this dynamic variant. The most challenging part of the problem is to make real-time decisions (i.e., whether to accept the newly arrived service requests or not) during the execution of the routes. To address this gap, this study proposes a novel genetic programming algorithm that is Hybrid Crossover Genetic Programming featuring two distinct crossover operators to tackle the dynamic vehicle routing problem with drone and time window constraints. The algorithm dynamically adjusts schedules and routes by incorporating real-time data, enabling it to adapt to fluctuating traffic conditions and delivery demands. Whenever a new request arrives, it tries to re-generate new routes to include the new request by the heuristic. It accepts the new request if successful and rejects otherwise. Extensive experiments were conducted to evaluate the algorithm’s performance against the existing methods, demonstrating its ability to find high-quality solutions efficiently. Experiments demonstrate that the proposed algorithm is highly effective, achieving fast convergence and consistently outperforming the existing methods.
Keywords:
Dynamic vehicle routing problem
Drone
Genetic programming
Time window
Heuristics
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2.3
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447
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718
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