考虑时空拥堵和时间窗的多配送中心路径优化

Route Optimization of Multi-distribution Centers Considering Time and Space Congestion and Time Window

生鲜配送路径优化问题中,具有产品时效要求高和配送时间不确定的特点,且逐渐大型化和多配送中心化。综合考虑客户时间窗、商品保鲜期、路网拥堵时空特征和多配送中心,构建考虑综合模糊时间窗和速度时空系数的多车场多车型生鲜配送路径优化模型,提出在传统蚁群-遗传算法内嵌入两边逐次修正算法和正交试验设计的改进蚁群-遗传算法(ACO-GA),并利用正交试验设计优化算法参数,以提高算法计算能力。采用不同分布特征的Solomon算例以反映不同聚集形态的客户群体,最后将该模型与其他模型对比,验证模型的合理性,将该算法和传统算法对比,验证算法的有效性。结果表明,该模型可以较好解决配送时效性高和配送时间不确定之间的矛盾,且算法计算能力可接受。研究成果可为生鲜产品精益配送提供新思路。

In the fresh food distribution path optimization problem, it has the characteristics of high demand for product timeliness and uncertain distribution time, and gradually becomes large scale and multiple distribution center. Comprehensively considering the customer time window, the preservation period of goods, the spatial-temporal characteristics of road network congestion and multiple distribution center, a multi-yard and multi-vehicle fresh distribution route optimization model was constructed, which considered the comprehensive fuzzy time window and speed space-time coefficient. An improved ant colony genetic algorithm(ACO-GA) was proposed, in which the successive modification algorithm on both sides and the orthogonal test design were embedded in the traditional ant colony genetic algorithm. And the orthogonal experimental design was used to optimize the algorithm parameters, so as to improve the calculation ability of the algorithm. The Solomon examples with different distribution characteristics were used to reflect the customer groups with different aggregation forms. Finally, the proposed model was compared with other models, and the rationality of the model was verified. And the proposed algorithm was compared with the traditional algorithm to verify the effectiveness of the proposed algorithm. The results show that the proposed model can better solve the contradiction between high timeliness and uncertain delivery time, and the computational ability of the algorithm is acceptable. The research results can provide new ideas for lean distribution of fresh products.