Abstract: In order to solve the increased costs for agricultural distribution by no-load and secondary distribution problems, an improved hybrid genetic algorithm is put forward in this paper. In this study, the simulated annealing algorithm is incorporated into the genetic algorithm, the advantages of both the algorithm are adopted to jump out the local best, enhance the ability of global optimization. In addition, as the hybrid genetic algorithm is time intensive on computation and converges slowly, a hybrid crossover and heuristic mutation method is proposed, it minimizes unnecessary calculations, and improves the efficiency of the optimization algorithm to overcome these disadvantages. 30 agricultural chain distribution outlets in Daxing are selected for the study of daily distribution route optimization. The results show that the proposed algorithm compared with the traditional algorithm has better convergence, and the optimization result is more approximate optimal global optimal solution, which provides a new method for the real-time path optimization decisions for the agricultural distribution.

Key words: agricultural distribution, hybrid genetic algorithm, hybrid crossover, heuristic mutation

摘要： 针对农资配送过程中普遍存在空载以及二次配送导致配送成本增加问题，提出一种基于改进混合遗传算法的配送优化方法。在遗传算法中融入模拟退火算法，结合二者优势使得算法跳出局部极值，增强全局优化的能力;针对两种算法混合后造成算法运行时间长、收敛慢的缺点，提出一种混合交叉方式以及混合启发式变异的方法，最大限度减少不必要的计算，提高算法的优化效率。选取北京农资大兴配送中心针对大兴地区30个农资连锁经销门店的日常配送路径优化问题开展实验研究，结果表明，提出的算法较传统算法具有更好的收敛性，优化结果更加逼近全局最优解，可为农资配送车辆实时路径优化决策提供一种新的方法。

关键词: 农资配送, 混合遗传, 混合交叉, 启发式变异