关键词: 交通运输工程；生鲜配送；多车场多车型；速度时空函数；综合模糊时间窗；改进蚁群-遗传算法；两边逐次修正算法

Abstract: 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.

Key words: traffic and transportation engineering; fresh food distribution; multi-yard and multi-vehicle; speed space-time function; comprehensive fuzzy time window; improved ant colony genetic algorithm; two side successive correction algorithm