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基于PSO-GA算法的后方仓库货位分配优化

Optimization of Location Allocation in Rear Warehouse Based on PSO-GA Algorithm

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针对当前部队后方仓库的货位分配效率不高的问题,将传统的粒子群优化(PSO)算法和遗传算法(GA)相结合,构建一种混合求解模型.结合实例通过仿真分析表明,该混合算法与传统的PSO和GA相比,具有一定的优越性,能够有效提高仓库作业效率和货架稳定性,对后方仓库的货位分配研究具有一定的理论价值和实践意义.
To address the problem of inefficient allocation of cargo space in the rear warehouse of the current army,a hybrid solution model of particle swarm optimization(PSO)algorithm and genetic algo-rithm(GA)is established.Simulation analysis shows that the hybrid algorithm has certain advantages compared with the traditional PSO and GA algorithms,which can effectively improve the efficiency of warehouse operation and shelf stability.It has certain theoretical value and practical significance for the research of location allocation in the rear warehouse.

rear warehouselocation allocationparticle swarm optimization algorithmgenetic algo-rithmmultiobjective optimization

邱雄飞、张桦、赵润泽

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陆军工程大学石家庄校区,河北石家庄 050003

陆军装备部驻石家庄地区第三军事代表室,河北石家庄 050051

后方仓库 货位分配 粒子群算法 遗传算法 多目标优化

2024

信息工程大学学报
中国人民解放军信息工程大学科研部

信息工程大学学报

影响因子:0.276
ISSN:1671-0673
年,卷(期):2024.25(4)
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