面向城市商超物流配送的异构车辆调度研究  

Heterogeneous Vehicle Scheduling Oriented to Urban City Supermarket Logistics Distribution

作　　者：张淼寒 安裕强[2,4] 潘楠 孙雨轩[3] 鲍景 高瀚 韩宇航 ZHANG Miaohan;AN Yuqiang;PAN Nan;SUN Yuxuan;BAO Jing;GAO Han;HAN Yuhang(Faculty of Civil Aviation and Aeronautics,Kunming University of Science and Technology,Kunming 650500,China;Hongyun Honghe Tobacco(Group)Co.,Kunming 650000,China;Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Faculty of Management and Economics,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学民航与航空学院,云南昆明650500 [2]红云红河烟草(集团)有限责任公司,云南昆明650000 [3]昆明理工大学信息工程与自动化学院,云南昆明650500 [4]昆明理工大学管理与经济学院,云南昆明650500

出　　处：《昆明理工大学学报（自然科学版）》2022年第6期154-162,181,共10页Journal of Kunming University of Science and Technology(Natural Science)

基　　金：云南省科技计划项目(202104AP080061);红云红河烟草(集团)有限责任公司科技项目(HYHH2021XX04);云南中烟工业有限责任公司科技项目(2018QT05)。

摘　　要：工业5.0时代即将到来,构建面向城市智慧物流调度供应链的核心问题在于如何高效处理货物数量多、种类冗杂、路径实时性等多维约束条件下同时降低物流车辆配送成本.针对此类问题,充分考虑路段拥堵系数、车辆装载率、泡重比、异构车型限制等约束条件,构建以总配送调度成本最小、车辆装载利用率最大为优化目标的城市异构车辆物流调度模型,进一步通过增加指数型温度下降机制、变量交叉扰动和种群分类策略设计一种改进的混合鲸鱼群算法进行模型求解.基于真实数据集的仿真结果表明,相比粒子群算法、模拟退火算法及鲸鱼优化算法,本文所提算法在目标函数上平均降低了34.33%,在配送费用上平均降低了11%,在平均配载率上增加了10.67%.实验结果验证了所提算法在求解此类复杂环境约束条件下的多目标优化问题中展现了较强的全局搜索能力及搜索精度,同时进一步说明了所建立模型在求解城市物流车辆调度问题中的可行性和有效性.The era of Industry 5.0 is approaching.The critical issue in establishing smart urban logistics dispatching supply chain is how to deal with the constraints of large quantities and variety of goods and real-time paths while reducing vehicle logistics distribution costs.To solve this problem, this paper fully considers constraints such as roadway congestion coefficient, vehicle loading rate, bubble weight ratio, and heterogeneous vehicle restrictions to establish an urban heterogeneous vehicle logistics scheduling model with the optimization objectives of minimizing total distribution scheduling cost and maximizing vehicle loading utilization, and designs an improved hybrid whale swarm algorithm by adding exponential temperature drop mechanism, variable cross-turbulence and population classification strategies to model solving.Simulation results based on real data sets show that compared with the particle swarm algorithm, simulated annealing algorithm, and whale optimization algorithm, the proposed algorithm reduces the objective function by 34.33% on average, reduces the distribution cost by 11% on average, and increases the average dispatch rate by 10.67%.The experimental results verify that the proposed algorithms demonstrate global solid search capability and search accuracy in solving such multi-objective optimization problems with complex constraints and further illustrate the feasibility and effectiveness of the established model in solving urban logistics vehicle scheduling problems.

关 键 词：物流配送 城市商超 异构车辆 鲸鱼优化算法 实时路径 

分 类 号：U492.22[交通运输工程—交通运输规划与管理] F252.1[交通运输工程—道路与铁道工程] TP18[经济管理—国民经济]