基于混合算法的汽车零部件物流配送路径优化

Optimization of Auto Parts Logistics Distribution Path Based on Hybrid Algorithm

针对汽车零部件供应物流,建立循环取货配送路径优化模型,将遗传算法与Max-Min蚁群算法融合,采用遗传算法生成初始信息素分布,利用Max-Min蚁群算法求精确解,并通过实例验证。结果表明,混合算法对于解决供应商数量多、带时间窗限制与碳排放限制的配送路径优化问题,可有效降低车辆取货频次和提高车辆装载率。

According to the supply logistics of automobile parts, milk-run distribution path optimization model is established. The Max-Min genetic algorithm and ant colony algorithm are fused. Genetic algorithm is adopted to give information pheromone distribution; the ant algorithm is used to give the precision of the solution, and is verified by an example. The results show that the hybrid algorithm can effectively reduce the distribute frequency and improve vehicle loading rate for solving the distribution path problem of supplier and limiting the time window and carbon emissions.