考虑拆分策略的智能仓库订单拣选优化问题

Optimization for order picking problem with splitting policy in smart warehouses

智能仓库中用智能拣货车和拣货机器人代替了传统的拣货员,从而能实行更加有效的拣货方案。针对以最小化总延误时间为目标的智能仓库的订单拣选问题,考虑订单可以被拆分的情形,提出订单拆分策略,并建立了相应的非线性0-1整数规划模型。为高效求解智能仓库订单拣选优化问题,将问题的模型分解为订单分批阶段和批次分配阶段,并分别设计订单分批算法和智能果蝇优化算法。数值实验结果表明,拆分策略能明显减小智能仓库中订单拣选的总延误时间;通过与同类型算法的比较发现,所提出的智能果蝇优化算法具有更优越的性能。

In smart warehouse,the picker is replaced by the intelligent picking car or shelf picking robot in smart warehouses.Hence a complex but efficient picking policy can be applied in a smart warehouse.Aiming at the order picking problem with splitting policy in smart warehouses with minimum total tardiness,a nonlinear 0-1 integer programming model was proposed to formulate the problem.To solve the order picking problem efficiently,the model was divided into the order batching subproblem and the batch assignment subproblem.A heuristic algorithm and a smart fruit fly optimization algorithm were introduced to handle these two subproblems respectively.According to the numerical experiments,the computational results showed that the order splitting policy could reduce the tardiness significantly,and the proposed smart fruit fly optimization algorithm had better performance than other algorithms.