基于配送服务模式及路径策略的物流无人机调度模型

Logistics drone scheduling model based on delivery service modes and routing strategies

针对远距离城市空间物流无人机配送问题,提出基于两级多中心场景的无人机(Unmanned Aerial Vehicle, UAV)配送服务模式.综合考虑客户时间、无人机充电和配送路径策略等内容,建立以最小配送总成本为目标的两级多中心物流无人机城市配送调度模型,通过比较标准算例下Cplex优化软件与鲸鱼优化算法(Whale Optimization Algorithm, WOA)、遗传算法(Genetic Algorithm,GA)和灰狼优化算法(Grey Wolf Optimization, GWO)3种启发式算法的实验结果,确定求解算法以设计两阶段算法进行求解.以天津市中心城区为例进行模型求解,并从需求点规模、机型和时间窗3方面进行敏感度分析.研究结果表明:与闭合式路径相比,半开放式路径策略分别节省约11.09%、60.45%的配送总成本和时间窗惩罚成本,成本节省随配送规模增大而增加;提出的固定路径班次模式相比现有按需模式,其可变成本和总成本降幅均高于18%,使用无人机数量减少近28%,生成的班次时刻表和飞行路径时空图可显现不同模式优劣.本文提出的模型对优化远距离城市物流无人机调度方案及配送模式有一定实用价值,可为企业在物流无人机的配送规划方面提供参考.

This study addresses the challenges of long-range urban logistics via Unmanned Aerial Vehicle(UAV)delivery by introducing a two-level multi-hub UAV delivery service model.By comprehensively taking into account factors such as customer time preferences,UAV recharging necessities,and delivery routing strategies,a two-level multi-hub urban UAV delivery scheduling model is established to minimize aggregate delivery expenses.Solution methodologies are meticulously evaluated through benchmark case studies,comparing the efficacy of Cplex optimization software with heuristics including the Whale Optimization Algorithm(WOA),Genetic Algorithm(GA),and Grey Wolf Optimization(GWO).Subsequently,a two-phase algorithmic resolution strategy is devised.The efficacy of the model is exemplified through its application in Tianjin’s central urban area,accompanied by sensitivity analyses meticulously examining three pivotal facts:demand node scale,UAV types,and time window constraints.Results reveal that compared with closed routing strategy,the semi-open routing strategy achieves savings of approximately 11.09%in total delivery costs and 60.45%in time window penalty costs,with cost savings increasing with delivery scale.Compared with on-demand models,the proposed fixed-route schedule mode demonstrates over 18%reductions in both variable costs and overall costs,alongside a nearly 28%decrease in required UAV numbers.Temporal-spatial diagrams illustrating flight schedules and routes highlight the advantages and disadvantages of different approaches.This model offers practical insights for refining long-range urban UAV logistics scheduling schemes and delivery patterns,providing valuable guidance for UAV-based logistics planning for enterprises.