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基于模拟退火遗传算法的自动化立体仓库货位优化
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国家自然科学基金(51875451)


Optimization of Automatic Storage Location Based on Simulated Annealing Genetic Algorithm
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    摘要:

    自动化立体仓库货位随机分配不仅影响仓库的稳定性,而且降低货物出入库效率。为寻找货位最优分配,提出一种基于模拟退火遗传算法解决货位分配问题的方法,并引入了3种不同的货位分配原则。通过分配原则建立货位分配优化模型,并利用模拟退火遗传算法对其进行解算,最终通过案例分析将所得目标函数的最小值与货位随机分配的初始值以及使用遗传算法求解值进行对比,可见使用此方法优化后货架稳定性以及出入库效率都大大提高。

    Abstract:

    The random distribution of automatic storage not only has a great impact on the stability of the warehouse,but also reduces the efficiency of goods entering and leaving the warehouse.Therefore,finding the optimal allocation of goods plays a decisive role in the use of enterprise warehouses.In view of the above problems,a method was proposed to solve the problem of location allocation through genetic algorithm based on simulated annealing,and three different principles of location allocation were introduced.The distribution allocation optimization model was established by using the distribution principle,and the simulated annealing genetic algorithm was used to solve the problem.Finally,the minimum value of the obtained objective function and the initial value of the random allocation of the cargo space and the solution value using the genetic algorithm were compared through case analysis.The comparison shows that the stability of the shelf and the efficiency of the storage are greatly improved after using the optimization method.

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曹现刚,宫钰蓉,雷一楠.基于模拟退火遗传算法的自动化立体仓库货位优化[J].机床与液压,2020,48(14):67-72.
CAO Xiangang, GONG, LEI Yinan. Optimization of Automatic Storage Location Based on Simulated Annealing Genetic Algorithm[J]. Machine Tool & Hydraulics,2020,48(14):67-72

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  • 在线发布日期: 2020-10-15
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