计算机工程与应用 ›› 2020, Vol. 56 ›› Issue (23): 229-236.DOI: 10.3778/j.issn.1002-8331.1911-0333

• 工程与应用 • 上一篇    下一篇

不同订单分配和算法下的拣货路径优化组合

孟鑫,杨琴,郝婷婷,张洁,曹策俊   

  1. 1.四川师范大学 商学院,成都 610101
    2.长安大学 经济与管理学院,西安 710054
    3.重庆工商大学 商务策划学院,重庆 400067
    4.四川工商学院 经济管理学院,成都 611745
  • 出版日期:2020-12-01 发布日期:2020-11-30

Optimized Combination of Picking Path in Different Distributions and Algorithms

MENG Xin, YANG Qin, HAO Tingting, ZHANG Jie, CAO Cejun   

  1. 1.School of Business, Sichuan Normal University, Chengdu 610101, China
    2.School of Economics and Management, Chang’an University, Xi’an 710054, China
    3.School of Business Planning, Chongqing Technology and Business University, Chongqing 400067, China
    4.School of Economics Management, Sichuan Technology and Business University, Chengdu 611745, China
  • Online:2020-12-01 Published:2020-11-30

摘要:

针对仓库拣货作业效率低与成本高等问题,运用调度理论和启发式算法展开研究。考虑两种拣取方式下的订单分配情况,分析了京东典型的双区型仓库中存在的问题;运用三元组α/β/γ方法对拣货路径规划问题进行描述,建立最小化总路径的拣货路径规划模型,设计与模型相适应的S型启发算法和遗传算法;进行算例仿真,比较与分析两种拣取方式下的仿真结果。结果显示,将订单分批策略与遗传算法下的路径规划相结合能得到较合理的拣货作业规划。

关键词: 拣货作业, 双区型仓库, 路径规划, 启发式算法

Abstract:

Aiming at the problems of low efficiency and high cost of warehouse picking operations, the scheduling theory and heuristic algorithms are adapted. Firstly, it analyzes the problems existing in JD’s typical two-block warehouse, and considers the order distribution under the two picking methods. Then the triple method α/β/γ is used to describe the problem of picking path planning, to establish the picking path planning model to minimize the total path, and to design the S-type heuristic algorithm and genetic algorithm suitable for model matching. Finally, the simulation of the example is carried out, and the simulation results of the two picking methods are compared and analyzed. The results show that the combination of the order batching strategy with the path planning under the genetic algorithm can obtain a more reasonable picking operation plan.

Key words: warehouse picking operations, two-block warehouse, picking path planning, heuristic algorithm