【摘要】 为实现在路段通行时间不确定背景下,配送企业对多种车型车辆的组合优化,使车辆资源利用、配送路径最优。通过建立总成本和配送时间最小的多目标模型、并考虑时间窗约束,设计提出多目标进化遗传算法求解该问题。本算法结合链表思想,同时为解决产生不可行解问题,在解编码时采用多染色体;并在算法中针对子染色体和母染色体分别设计交叉算子,运用擂台赛法则和改进精英保留策略构造非支配解集和加快算法的收敛速度。结果表明:相比单车型,多车型组合优化具有更高的经济效益,且随着不确定参数的变化,运输成本上升,多车型配送满载率受影响较小。更多还原

【Abstract】 In order to realize that distribution company has a reasonable combination of multi-type vehicles to make the utilization of vehicle resources and the distribution path optimization under the background of uncertainty of the road passage time, the delivery cost and delivery time are proposed as the multi-objective function of the solution model, by considering the time window constraint. It designs a multi-objective evolutionary genetic algorithm to seek the answer. At the same time, the solution coding adopts multi-chromosome method and linked list idea to avoid infeasible solutions. In the algorithm, crossover operators are designed for the child chromosome and the mother chromosome. The non-dominated solution set by the arena’s principle is constructed and the improved elite retention strategy is used to avoid local optimal solution. The introduction of the two methods can accelerate the convergence of the algorithm. The result shows that, multi-type combination optimization has higher economic benefits than single type, and with the change of uncertain parameters, the transportation cost increases, and the full load rate of the multi-type distribution is less affected.更多还原