越库配送车辆调度优化仿真研究

Research on Vehicle Scheduling Optimization and Simulation in Cross Docking

越库配送被称为"物流领域的JIT"。越库配送的车辆调度是影响配送效率的关键环节,合理的车辆调度能够使客户和供应商得到及时服务,减少等待时间,加快物品的流转速度。现有研究未考虑越库配送中搬运设备对车辆调度的影响,使调度结果的可操作性不强。针对上述问题,以最小化操作时间为目标,建立了有搬运约束的越库车辆调度的数学模型。依据问题特征结合果蝇优化算法特点,提出基于整数的编码方案;采用贪婪搜索和随机方法初始化种群;设计了基于插入算子和交换算子的嗅觉搜索策略;为了提高算法的优化质量,设计了一种基于优势解集的协作引导策略。通过仿真,结果显示改进果蝇优化算法具有较好的全局搜索能力及较快的收敛速度,是求解越库配送车辆调度问题的有效方法。

Cross docking is called as ＂JIT for logistics ＂. The vehicle scheduling is the key factor that affects the distribution efficiency. Reasonable vehicle scheduling can speed up the flow of goods, and reduce the waiting time for the customers and suppliers. But, the existing research does not consider the impact of the handling equipment on the vehicle dispatching, so that the dispatching result is not operational. For the problem, we established a mathematic model of the vehicle scheduling in cross docking with handling equipment constraintswas. The objective of the model is to minimize the operation time. According to the characteristics of the problem and the fruit fly optimization algorithm （FOA）, an integer-based coding scheme was proposed. And greedy search and random method were applied to initialize the population. The exchange and insertion operation was used to produce neighbors in the smell-based search stage. A collaboration guiding mechanism using better solution set was designed to improve the algorithm＇s global search ability and convergence speed. Through the simulation experiments, the result shows that the improved FOA has better global search capability and faster convergence, and is an effective method to solve the cross-docking scheduling problem.