Cooperative driving route planning based on asymmetric multi-agent path planning problem with limited service area constraints

Mobility and logistics service providers operating a vehicle fleet and serving several targets with each vehicle are faced with the challenge of planning efficient and cooperative driving routes while considering all the vehicles. To address this challenge, we present a novel driving route planning method based on an Asymmetric Multi-Agent Path Planning (AMAPP) problem, a variation of the classical Multiple Traveling Salesmen Problem (MTSP). Given a team of $m$ agents (vehicles) that must visit $n$ targets located in a real road network, a set of $m$ optimized open paths (the Plan) must be found such that each target is visited exactly once. The optimization objective is to minimize the driving distance of the path with the longest driving distance in the Plan (a Min-Max problem) so that this cooperative operation can be completed in the least amount of time. In several cases, the service area for each agent is limited (e.g., certain districts in a city). To simulate this real-world condition, two additional constraints are applied: the maximum geodetic range for each agent, and the maximum spatial range of targets for each agent. An easy-to-apply Genetic Algorithm (GA) with two novel initialization methods is presented to solve this route planning problem. In order to validate the developed route planning method and demonstrate its applicability, the method was tested in a real-world test case where it showed a decent performance. The results show that the applied limited service area constraints not only decrease the average driving distance of the longest route in the route plan, but also reduce the average runtime of the developed solution. Additionally, the performance of the proposed GA was benchmarked using 26 problems based on the TSPLIB instances, where the proposed GA achieved new Best Known Solution (BKS) values for 19 benchmark problems and a result equal to the BKS value for another two problems, demonstrating its superiority and robustness. The developed method can be used for route planning of the mobility and logistics services requiring multiple destinations to be reached by a fleet of vehicles, such as group ride-sharing and last-mile delivery.