Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (24): 147-153.DOI: 10.3778/j.issn.1002-8331.1606-0301

Previous Articles     Next Articles

Routing optimization and algorithm analysis of equipment joint distribution

KANG Wenfeng, TANG Guangming, SUN Yifeng   

  1. College of Cryptography Engineering, PLA Information Engineering University, Zhengzhou 450001, China
  • Online:2017-12-15 Published:2018-01-09



  1. 解放军信息工程大学 密码工程学院,郑州 450001

Abstract: Aiming at problems that exist in the traditional equipment distribution such as independent distribution, sharing nothing between depots, unreasonable routing optimization, and so on, equipment joint distribution is raised, through synthesizing the time of distribution, serve satisfaction and the cost of distribution, a joint distribution routing optimization model with time windows is built. Then a new self-adaptive improved genetic algorithm is put forward. The algorithm first utilizes PFIH to construct the initial solution, employs a novel mutation operator and self-adaptive crossover or mutation probability. To speed up the convergence of the algorithm, relocate and 2-opt algorithms are adopted to optimize the intermediate solutions in the end of every iteration. RCA is adopted to handle the weight of multi-targets. Finally, experimental results show that the performance of the algorithm is excellent and efficient, and it can be applied to the actual scene of military equipment joint distribution.

Key words: joint distribution, time windows, self-adaptive improved genetic algorithm, self-adaptive crossover or mutation probability, neighborhood searching algorithm

摘要: 针对传统的装备配送模式存在着分区复杂、物资无法共享以及配送路径优化不合理等问题,提出了装备联合配送的方式,综合考虑配送时间、部队服务满意度和配送成本的目标,构建带时间窗的联合配送路径优化模型。并针对模型,提出了一种自适应改进遗传算法。该算法利用PFIH算法构建初始解,采用新颖的变异算子和自适应的交叉变异概率,利用relocate和2-opt进行中间解的优化,加快算法收敛。多目标权重处理采用RCA算法进行量化。最后实验证明该算法性能优良,求解高效,能够应用于军用装备联合配送的实际场景。

关键词: 联合配送, 时间窗, 自适应改进遗传算法, 自适应交叉变异概率, 邻域搜索算法