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Ensemble multi-objective optimization approach for heterogeneous drone delivery problem
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AI
DOI：10.1016/j.eswa.2024.123472.png)
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Abstract
En 中文
Recently, driven by advancements in the payload capacity and endurance of drones, the logistics industry has shown significant interest in drone Last -Mile logistics. Efficient routing are crucial scientific challenges in drone delivery problems. In this study, we address the routing problem in heterogeneous drone delivery, which involves a large drone transporting multiple small drones to sub -regions for parcel delivery, aiming to both reduce the drones' distance costs and improve customer satisfaction, termed HDDPBO To tackle the HDDPBO problem effectively, we propose a voting -based ensemble multi -objective genetic approach, named VEMOGA, in which an improved clustering algorithm is developed to divide customers into K clusters, enabling each drone to handle multiple parcel deliveries within a sub -region. In this way, it reduces the complexity of HDDPBO by transforming it into multiple sub -problems. Secondly, a multi -objective genetic approach with heuristic operators is proposed to explore high -quality solutions, in which customized crossover and mutation operators are designed in the genetic approach, and a voting -based ensemble algorithm is designed to robustly select the Pareto frontier with high -quality convergence and diversity. Extensive experiments are conducted on synthetic instances to evaluate the proposed algorithm, and the experimental results demonstrate superior performance compared to three other baselines. Additionally, a real -world instance has been scrutinized to ascertain the applicability of Last -Mile logistics, and sensitivity analyses of pivotal factors have been conducted and several managerial insights pertinent are given to the drone -based Last -Mile logistics.
Keywords:
Multiobjective evolutionary algorithm
Ensemble algorithms
Heterogeneous multi-drone
Routing
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2.9W
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10.2W