Arc Routing Problems with Multiple Trucks and Drones: A Hybrid Genetic Algorithm
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915462112083968 |
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| author | Sobhanan, Abhay Charkhgard, Hadi Kwon, Changhyun |
| author_facet | Sobhanan, Abhay Charkhgard, Hadi Kwon, Changhyun |
| contents | Arc-routing problems underpin numerous critical field operations, including power-line inspection, urban police patrolling, and traffic monitoring. In this domain, the Rural Postman Problem (RPP) is a fundamental variant in which a prescribed subset of edges or arcs in a network must be traversed. This paper investigates a generalized form of the RPP, called RPP-mTD, which involves a fleet of multiple trucks, each carrying multiple drones. The trucks act as mobile depots traversing a road network, from which drones are launched to execute simultaneous service, with the objective of minimizing the overall makespan. Given the combinatorial complexity of RPP-mTD, we propose a Hybrid Genetic Algorithm (HGA) that combines population-based exploration with targeted neighborhood searches. Solutions are encoded using a two-layer chromosome that represents: (i) an ordered, directed sequence of required edges, and (ii) their assignment to vehicles. A tailored segment-preserving crossover operator is introduced, along with multiple local search techniques to intensify the optimization. We benchmark the proposed HGA against established single truck-and-drone instances, demonstrating competitive performance. Additionally, we conduct extensive evaluations on new, larger-scale instances to demonstrate scalability. Our findings highlight the operational benefits of closely integrated truck-drone fleets, affirming the HGA's practical effectiveness as a decision-support tool in advanced mixed-fleet logistics. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_18105 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Arc Routing Problems with Multiple Trucks and Drones: A Hybrid Genetic Algorithm Sobhanan, Abhay Charkhgard, Hadi Kwon, Changhyun Neural and Evolutionary Computing Optimization and Control Arc-routing problems underpin numerous critical field operations, including power-line inspection, urban police patrolling, and traffic monitoring. In this domain, the Rural Postman Problem (RPP) is a fundamental variant in which a prescribed subset of edges or arcs in a network must be traversed. This paper investigates a generalized form of the RPP, called RPP-mTD, which involves a fleet of multiple trucks, each carrying multiple drones. The trucks act as mobile depots traversing a road network, from which drones are launched to execute simultaneous service, with the objective of minimizing the overall makespan. Given the combinatorial complexity of RPP-mTD, we propose a Hybrid Genetic Algorithm (HGA) that combines population-based exploration with targeted neighborhood searches. Solutions are encoded using a two-layer chromosome that represents: (i) an ordered, directed sequence of required edges, and (ii) their assignment to vehicles. A tailored segment-preserving crossover operator is introduced, along with multiple local search techniques to intensify the optimization. We benchmark the proposed HGA against established single truck-and-drone instances, demonstrating competitive performance. Additionally, we conduct extensive evaluations on new, larger-scale instances to demonstrate scalability. Our findings highlight the operational benefits of closely integrated truck-drone fleets, affirming the HGA's practical effectiveness as a decision-support tool in advanced mixed-fleet logistics. |
| title | Arc Routing Problems with Multiple Trucks and Drones: A Hybrid Genetic Algorithm |
| topic | Neural and Evolutionary Computing Optimization and Control |
| url | https://arxiv.org/abs/2508.18105 |