关键词: JIT采购, 自适应网格的人工蜂群算法, 多目标车辆调度, 运输经济

Abstract:

Under the JIT procurement mode, the influence of transportation cycle and purchase quantity on path optimization is studied, the scheduling model is established, and a grid-based adaptive Adaptive Artificial Bee Colony Algorithm （GAMOABC） is designed to minimize the total transportation distance and the number of vehicles used. In the algorithm, the pareto optimal solution saved in the grid is used to update and maintain the optimal solution in the grid, so as to ensure the diversity of solution set and update the leading bee position in the grid through location sharing information, so as to improve the accuracy of solution set. The priority value corresponding to the vehicle and raw materials is represented by the encoding method of two-dimensional matrix. In the decoding process, in order to meet the production constraints, the scheduling set is determined according to the current raw material consumption completion time, and the heuristic information is designed. The test examples and experiments show that compared with NSGA-II and MOEAS, the Pareto solution set obtained by GAMOABC algorithm is more diverse and accurate.

Key words: JIT purchasing, artificial bee colony algorithm based on adaptive grid, multi-objective vehicle scheduling, transportation economy