基于嵌套遗传算法的拣货作业联合优化

针对物流配送中心拣货作业过程中传统订单分批和拣货路径分步优化难以获得整体最优解的问题，为了提高拣货作业效率，提出了一种基于嵌套遗传算法的订单分批和路径优化的联合拣货策略。首先，建立了以拣货总时间最短为目标函数的订单分批与拣货路径联合优化模型；然后，考虑双重优化的复杂性，设计了一种嵌套遗传算法对模型进行求解，外层不断优化订单分批结果，内层根据外层订单分批结果优化拣货路径。算例结果表明，与传统的订单分步优化、分批分步优化策略相比，所提策略的拣货时间分别减少了45.6%、6%，基于嵌套遗传算法的联合优化模型得出的拣货路径更短、拣货时间更少。为验证该算法对不同规模订单均有较优性能，分别对10、20、50张订单规模的算例进行仿真实验，结果表明，随着订单量的增加，整体拣货距离和时间进一步减少，拣货时间的减少从6%增加到7.2%。基于嵌套遗传算法的拣货作业联合优化模型和其求解算法可以有效解决订单分批与拣货路径联合优化问题，为配送中心拣选系统的优化提供依据。

Joint optimization of picking operation based on nested genetic algorithm

It is difficult to obtain the overall optimal solution by the traditional order batching and the picking path step-by-step optimization of picking operation in the logistics distribution center. In order to improve the efficiency of picking operation, a joint picking strategy based on nested genetic algorithm for order batching and path optimization was proposed. Firstly, the joint optimization model of order batching and picking path was established with the shortest total picking time as the objective function. Then, a nested genetic algorithm was designed to solve the model with the consideration of the complexity of double optimizations. The order batching result was continuously optimized in the outer layer, and the picking path was optimized in the inner layer according to the order batching result in the outer layer. Results of the examples show that, compared with the traditional strategies of order step-by-step optimization and step-by-step optimization in batches, the proposed strategy has reduced the picking time by 45.6% and 6% respectively, and the joint optimization model based on nested genetic algorithm results in shorter picking path and less picking time. To verify that the proposed algorithm has better performance on orders with different sizes, the simulation experiments were performed to the examples with 10, 20, 50 orders respectively. The results show that, with the increase of order quantity, the overall picking distance and time are further reduced, the decrease of picking time is risen from 6% to 7.2%.The joint optimization model of picking operation based on nested genetic algorithm and its solution algorithm can effectively solve the joint optimization problem of order batching and picking path, and provide the basis for the optimization of picking system in the distribution center.