具有送货机器人和无人机补给的移动包裹寄存系统的数学方法

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-10-09 DOI:10.1016/j.swevo.2025.102182

Cheng Chen , Emrah Demir , Wenke Li , Xisheng Hu , Hainan Huang , Jian Li

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引用次数: 0

摘要

受城市物流中自主技术快速发展的推动，本研究引入了一种具有自主资源的车辆路径问题的新形式，包括移动包裹储物柜（MPLs）、送货机器人和无人机。在这个问题中，顾客可以选择送货上门，也可以选择从指定停车场的储物柜里取货。机器人从MPLs部署，根据需要由无人机补充。我们将此问题定义为带有送货机器人和无人机补给的移动包裹寄存柜问题（MPLPDR-DR）。为了解决这个问题，我们建立了一个混合整数线性规划（MILP）模型，并开发了一种数学方法。该方法集成了一种混合元启发式算法，用于优化MPLs和交付机器人的路由，而MILP模型确定了最佳的无人机补给决策。混合元启发式算法建立在人工蜂群框架上，集成了大邻域搜索过程、可变邻域下降过程和突变机制。所建议的方法还解决了与并行和顺序交付计时相关的同步挑战。大量的实验证明了该算法在大型MPLPDR-DR实例上的有效性，结果提供了有价值的管理见解。

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A matheuristic approach for the mobile parcel locker delivery system with delivery robots and drone resupply

Motivated by the rapid advancement of autonomous technologies in urban logistics, this research introduces a novel variant of vehicle routing problem with autonomous resources, including mobile parcel lockers (MPLs), delivery robots and drones. In this problem, customers choose between home delivery and self-pickup from lockers at designated parking areas. Robots are deployed from MPLs which are resupplied by drones as needed. We define this problem as the Mobile Parcel Locker Problem with Delivery Robot and Drone Resupply (MPLPDR-DR). To solve it, we formulate a mixed-integer linear programming (MILP) model and develop a matheuristic approach. This approach integrates a hybrid metaheuristic algorithm for optimizing the routing of MPLs and delivery robots, while a MILP model determines the optimal drone resupply decisions. The hybrid metaheuristic is built on the artificial bee colony framework and integrates a large neighborhood search procedure, a variable neighborhood descent procedure, and a mutation mechanism. The proposed approach also addresses synchronization challenges related to timing in parallel and sequential deliveries. Extensive experiments highlight the algorithm’s effectiveness on large set MPLPDR-DR instances, and the results offer valuable managerial insights.

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.