基于块合并策略的三维装箱多目标优化算法  

Three-dimensional Packing Multi-objective Optimization Algorithm Based on Block Merging Strategy

作　　者：杨欣 李明[1] YANG Xin;LI Ming(College of Intelligent Manufacturing and Modern Industry,Xinjiang University,Urumqi 830017,China)

机构地区：[1]新疆大学智能制造现代产业学院,乌鲁木齐830017

出　　处：《包装工程》2025年第1期193-202,共10页Packaging Engineering

基　　金：自治区重点研发计划项目(2022B01057-2)。

摘　　要：目的针对货车三维装箱的多目标优化问题,旨在提升货车货物的装载效率,降低装载成本,并确保货物在运输过程中的安全性。方法结合货物在运输转弯过程中的力学分析,求解货物重心的安全区域,构建货车三维装箱多目标优化模型;提出一种基于块合并的策略,以减少决策空间;设计一种基于强化学习、Q-Learning算法的双种群约束多目标优化框架,以提高算法的收敛性和解的多样性;利用公共数据集和实例数据进行验证。结果在满足转弯重心约束及其他约束的基础上,所提算法的平均空间利用率为92.07%,显著高于其他算法。结论本文所提的多目标优化算法能有效提高三维装箱问题的空间利用率和载重利用率,为工程实践中的三维装箱问题提供了有效的解决方案和参考。此外,该算法在保障货物运输安全性的前提下,显著提升了装箱规划的效率。The work aims to address the multi-objective optimization problem of three-dimensional truck loading,so as to enhance loading efficiency,reduce costs,and ensure cargo transportation safety.By integrating mechanical analysis of cargo during transportation turns,the safety region for the cargo's center of gravity was determined,and a multi-objective optimization model for truck loading was constructed.A block-merging strategy was proposed to reduce the decision space.A dual-population constraint-based multi-objective optimization framework based on reinforcement learning and Q-Learning algorithm was designed to improve convergence and solution diversity.Validation with public datasets and case studies showed that,under constraints including the turning center of gravity,the average space utilization rate achieved 92.07%,significantly surpassing other algorithms.In conclusion,the proposed multi-objective optimization algorithm effectively improves the space and load utilization rates for three-dimensional loading problems,offering effective solutions and references for practical applications.Furthermore,the algorithm significantly enhances loading planning efficiency while ensuring the safety of cargo transportation.

关 键 词：三维装箱 多目标优化 块组合 强化学习 转弯重心约束 

分 类 号：TB458.3[一般工业技术]