基于分区的多配送中心多车型车辆调度问题与遗传算法设计

 研究大规模的多配送中心多车型车辆调度问题,首先基于配送中心几何重心分区方法将多配送中心多车型车辆调度问题转化为单配送中心多车型车辆调度问题,该分区方法简单、直观,大大降低了运算量.建立了一种基于完整配送费用的多配送中心多车型车辆调度问题的数学模型,配送费用在考虑基本运输费的基础上又引入了司机的工资支出,包括基本工资和加班费.在多配送中心多车型车辆调度模型中,一个配送中心的一辆车可以为多个客户服务,但一个客户只能由某个配送中心的一辆车提供服务.根据模型的这些特点,提出了一种新的多片段染色体混合编码方案,并采用分段交叉、分段变异方法有效继承了父代染色体的部分基因,显著提高了算法的收敛性能.数值仿真结果验证了算法的可行性和有效性.

Multi-delivery Centre Multi-type Vehicle Scheduling Problem Based on the Partition and the Design of Genetic Algorithm

A large scale multi-delivery centre multi-type vehicle scheduling problem is considered. Firstly, based on the method of gravity center partition, the multi-delivery centre multi-type vehicle scheduling problem is transformed into the single-delivery centre multi-type vehicle scheduling problem, the partition method is simple, and reduces the amount of computations. Then based on the minimum delivery cost, a mathematical model of multi-delivery centre multi-type vehicle scheduling problem is established; the model considers not only the basic transportation cost, but also the driver's salary expenses, including the basic wage and overtime pay. In the model of multi-delivery centre multi-type vehicle scheduling, one car is able to provide service for more customers, however a customer must be served only by one car. According these characteristics, a new chromosomal hybrid encoding scheme is put forward, and the techniques of segmented crossover and mutation is used, the method effectively retains the father's generation superiority of chromosome fragments, therefore remarkably improves the convergence speed of the genetic algorithm. The results of numerical simulation verify the feasibility and effectiveness of the algorithm.