Arc Routing Problems with Multiple Trucks and Drones: A Hybrid Genetic Algorithm

2025-08-25

AI Summary

This paper investigates the multi-Truck–multi-Drone Routing Problem with Arc Constraints (RPP-mTD), which jointly optimizes heterogeneous vehicle routes—subject to mandatory traversal of specified directed arcs—to minimize the makespan. Motivated by rural power-line inspection, we propose a collaborative paradigm wherein trucks serve as mobile bases that deploy drones for parallel inspection tasks. Methodologically, we design a two-layer chromosome encoding scheme and a segment-preserving crossover operator, integrated within a hybrid genetic algorithm incorporating directed arc-sequence encoding, dynamic vehicle assignment, and multi-strategy neighborhood search. To the best of our knowledge, this is the first systematic modeling and solution approach for RPP-mTD. The proposed method significantly enhances scalability and coordination efficiency. Extensive experiments on both standard benchmarks and newly generated large-scale instances demonstrate its superiority. The framework provides an efficient decision-support tool for real-world applications such as power infrastructure inspection.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Minimizing makespan for multiple trucks with drones
• Solving generalized Rural Postman Problem with hybrid fleet
• Optimizing coordinated truck-drone routing for field operations

Innovation

• Hybrid Genetic Algorithm combining population and neighborhood searches
• Two-layer chromosome encoding for edge sequence and vehicle assignment
• Segment-preserving crossover operator with multiple local search techniques