面向取送货路径需求的多仓门越库系统车辆调度研究

项目名称： 面向取送货路径需求的多仓门越库系统车辆调度研究

项目编号： No.71302135

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 管理科学

项目作者： 叶艳

作者单位： 广东工业大学

项目金额： 20万元

中文摘要： 车辆调度是供应链物流领域中越库策略实施的关键问题。在车辆取送货路径的现实需求下，由于货物装卸顺序确定、车辆到达时间分散不确定等特点，车辆调度优化难度加大。针对现有研究未充分重视和解决这一难题，本项目研究多仓门越库系统中面向取送货路径需求的高效车辆调度方法。在装卸顺序要求下，研究基于分散搜索的混合智能优化的确定性车辆调度方法，实现越库运作优化；进一步考虑车辆到达分散不确定的特点及出库时间限制，利用多目标优化、粒子群优化等技术，研究基于多目标混合优化的不确定性车辆调度方法，实现越库运作时间及车辆出库延迟的最小化；针对车辆实时到达、长时间延迟等随机扰动，建立基于本体的调度知识库，研究基于知识与智能优化的越库车辆动态调度策略和方法，实现高效的动态调度。本项目研究对于发展多仓门越库系统的车辆调度技术、车辆调度与路径集成问题研究具有重要的学术理论价值，对实际越库中心的车辆调度和运作具有重要的实践意义。

中文关键词： 车辆调度；多仓门越库系统；取送货路径；装卸顺序；动态调度

英文摘要： Truck scheduling is a key problem for implementing crossdocking strategy in the supply chain and logistics paradigm. Its optimization is more difficult in the practical requirement of vehicle routing for picking up and delivering freight, due to some characteristics including determinate freight unloading/loading sequences and scattered and uncertain truck arrivals. Considering existing researches did not pay full attention to and solve this difficult problem, this project studies efficient truck scheduling approaches oriented to freight pickup and delivery routing requirement for multi-dock-door crossdocking systems. For the demand of given loading and unloading sequence, hybrid intelligent optimization technology based on scatter search is used to develop a deterministic truck scheduling approach in order to optimize crossrocking operations. Considering simultaneously the above demand, scattered and uncertain truck arrivals and outgoing time constraints, a nondeterministic truck scheduling method based on multi-objective hybrid optimization is explored by using some technologies including multi-objective optimization and particle swarm optimization for minimizing crossdocking operations time and outgoing delay. Finally, for real-time truck arrivals and random disturbances such as long delays, an ontology-based

英文关键词： Truck scheduling；Multi-door crossdocking system；pickup and delivery routing；Loading and unloading sequence；Dynamic scheduling