目的 降低物流总运营成本,并减少车辆使用数量,在快递包裹物流配送车辆路径优化中引入时间依赖车辆速度。方法 首先,基于车辆容量、配送中心和客户的时间窗及时间依赖等约束,构建以快递包裹物流配送总运营成本最小化和车辆使用数最小化为目标的双目标优化模型。其次,提出一种基于时空聚类的改进多目标粒子群的混合优化算法求解模型,通过引入启发式种群初始化与解码机制来提高初始解质量,应用自适应外部档案机制增强算法的鲁棒性,并集成动态发车策略与多周期车辆共享策略提高算法的寻优性。然后,通过对比CPLEX求解器、多目标模拟退火算法、多目标蚁群算法和多目标遗传算法,验证所提模型和算法的有效性。最后,结合实例比较分析优化前后相关指标,并分别从不同服务周期划分与不同时间依赖分段速度划分2个方面进行敏感性分析。结果 优化后配送网络的总运营成本降低47.6%,车辆使用数减少70.6%。在时间范围[8, 20]内划分4个服务周期与12段时间依赖速度时,所获结果最佳。结论 本研究提出的模型、算法以及相应的服务周期与时间依赖速度设定,能够显著降低快递包裹配送网络的总运营成本与车辆使用数,从而为考虑时间依赖的快递包裹物流配送车辆路径优化问题提供新的研究思路和理论支持。
Abstract
The work aims to incorporate the time-dependent vehicle speed into the vehicle route optimization for express package delivery to minimize total operational costs and the number of vehicles utilized in the distribution network. Firstly, a bi-objective optimization model was constructed subject to constraints including vehicle capacity, distribution centers, customer time windows and time-dependent vehicle speed conditions, with the objectives of minimizing the total operational cost of express parcel logistics distribution and minimizing the number of vehicles deployed. Secondly, an improved multi-objective particle swarm optimization algorithm based on spatiotemporal clustering (SC-IMOPSO) was proposed to solve the model. Heuristic population initialization and decoding mechanisms were introduced to improve the quality of initial solutions, an adaptive external archive mechanism was applied to enhance algorithm robustness, and a dynamic departure strategy and a multi-period vehicle-sharing strategy were integrated to improve algorithm search ability. Thirdly, the effectiveness of the proposed model and algorithm was validated through comparisons with CPLEX solver, multi-objective simulated annealing, multi-objective ant colony optimization, and multi-objective genetic algorithm. Finally, a case study was conducted to analyze key performance indicators. Sensitivity analyses were performed regarding different service period divisions and time-dependent vehicle speed segments. The optimized delivery network reduced total operating costs by 47.6% and the number of vehicles by 70.6%. Specifically, the optimization achieved superior performance when the time horizon[8,20] was divided into four service periods and twelve time-dependent vehicle speed segments. The proposed model, algorithm, and configuration strategies provide effective methods for cost reduction and offer new theoretical insights and research directions for the time-dependent express package distribution vehicle route optimization.
关键词
时间依赖 /
快递包裹配送 /
车辆路径问题 /
SC-IMOPSO算法 /
多周期车辆共享策略
Key words
time-dependent vehicle speed /
express package distribution /
vehicle route optimization /
SC-IMOPSO algorithm /
multi-period vehicle-sharing strategy
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基金
国家自然科学基金项目（72371044）;重庆市教委人文社科研究项目（26SKJD096）;重庆市自然科学基金面上项目（CSTB2025NSCQ-GPX0848）;重庆市教委科学技术重大项目（KJZD-M202300704）;重庆市高等教育教学改革研究重大项目（251030）;重庆交通大学“揭榜挂帅”项目（Z36250002）;重庆交通大学研究生科研创新项目（2025S0079）
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