基于灰狼优化算法的货物包装容器尺寸优化设计

王曦; 李立顺; 李红勋

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基于灰狼优化算法的货物包装容器尺寸优化设计
王曦,李立顺,李红勋
陆军军事交通学院军事交通运输研究所，天津 300171

摘要:

目的针对存放固定类型货物的仓库，设计货物包装容器尺寸系列，使得每种货物在设计的尺寸系列中都能找到最合适的容器，以提高仓库储运效率。方法根据问题描述，以单容器同构装箱问题为基础，以所有货物在容器尺寸系列中的最佳空间利用率平均值为目标函数，采用遗传装箱算法求解目标函数，通过灰狼优化算法进行容器尺寸系列的优化设计(GA-GWO)，并在此基础上引入贪心装箱算法改进原有算法(GR-GA-GWO)，同时以A公司仓库15种货物为样本，设计4个容器尺寸的优化设计问题，验证算法的实用性。结果 GA-GWO求解的结果可以完成货物包装容器的尺寸设计，但计算时间较长;GR-GA-GWO的求解结果较GA-GWO目标函数降低2.85%~5.34%，时间节省40.98%~60.49%，是有效的算法改进。结论通过提出的基于灰狼优化算法的货物包装容器尺寸优化设计方法，为A公司仓库解决货物包装容器尺寸的设计问题，也为同类货物包装容器尺寸优化设计提供参考。

关键词: 容器优化装箱算法灰狼优化

DOI：10.19554/j.cnki.1001-3563.2025.07.031

分类号:

基金项目:

Optimal Design of Cargo Packaging Container Dimensions Based on Grey Wolf Optimization Algorithm

WANG Xi, LI Lishun, LI Hongxun

(Institute of Military Transportation, Army Military Transportation University, Tianjin 300171, China)

Abstract:

For warehouses storing fixed types of goods, the work aims to design a series of cargo packaging container dimensions so that each type of cargo can be contained in the most suitable container of the designed dimensions to improve the transportation and storage efficiency of warehouses. According to the problem description, with the isomorphic packing of single container as the basis and the average value of the best space utilization rate of all cargoes in the containers of different dimensions as the objective function, the genetic container packing algorithm was employed to solve the objective function, and the grey wolf optimization algorithm (GA-GWO) was used to optimize the design of the container dimensions. Based on this, the greedy container packing algorithm was introduced to improve the original algorithm (GR-GA-GWO). To verify the practicality of the algorithm, 15 types of cargoes from Company A's warehouse were taken as samples, and four container dimension optimization design problems were designed. The results solved by GA-GWO could complete the cargo packaging container dimension design, but the calculation time was relatively long. The solution results of GR-GA-GWO reduced the objective function by 2.85%-5.34% compared to GA-GWO, but saved 40.98%-60.49% of the time, which was an effective algorithm improvement. The proposed method for optimizing the design of cargo packaging container dimensions based on the grey wolf optimization algorithm can provide a reference for the cargo packaging container dimension design of various types of warehouses.

Key words: container optimization container packing algorithm grey wolf optimization