货到人拣选系统订单分配问题研究

Research on Order Assignment of Goods-to-person Order Picking System

文中主要针对多层穿梭车的"货到人"拣选系统中的订单分配问题进行研究,为了规避传统订单分配策略带来的订单耦合问题,对"货到人"拣选系统的订单分配问题进行了分析,建立了订单分配问题的数学模型,以最小化订单拣选完成时间为目标,求得较优的货箱到达顺序。为了避免求解大规模订单量时,传统算法容易陷入局部最优解的问题,文中提出基于邻域搜索的自适应遗传算法。相比于标准遗传算法,改进遗传算法做出了两点优化:①采用自适应交叉和变异概率,避免破坏优秀子代;②在变异操作之后加入邻域搜索,以提高算法的局部搜索能力,避免算法过早收敛。采用MATLAB作为仿真平台来对比传统订单分配策略、标准遗传算法和自适应邻域搜索遗传算法的求解性能。仿真结果表明,在解的优劣性方面,自适应邻域搜索遗传算法优于标准遗传算法。

This paper mainly studies the order assignment problem in the goods-to-person order picking system based on multi-layer shuttle. In order to avoid the order coupling problem brought by the traditional order assignment strategy,the order assignment problem of the goods-to-person order picking system is analyzed,the mathematical model of the order assignment problem is established. In order to minimize the order picking completion time,the optimal container arrival order is obtained. An adaptive genetic algorithm based on neighborhood search is proposed in order to solve the problem that the standard genetic algorithm is easy to get trapped into the local optimal solution when solving the large-scale order quantity. Compared with the standard genetic algorithm,the optimization is carried out from two aspects: ①Adopt adaptive crossover and mutation probability to avoid destroying the excellent offspring;②Add the neighborhood search after the mutation operation to improve the local search ability of algorithm in order to avoid premature convergence of the algorithm. MATLAB is used as the simulation platform to compare the performance of the traditional order assignment strategy,the standard genetic algorithm and the adaptive neighborhood search genetic algorithm. The simulation results show that the adaptive neighborhood search genetic algorithm is superior to the standard genetic algorithm in the aspect of the average deviation distance and the standard deviation of the solution.