At an operational level, order picking is the main activity in fulfillment centers. Motivated by and through collaboration with a third party logistic company, this study presents a novel hybrid column generation (CG), genetic algorithm (GA), and artificial neural network (ANN) heuristic for minimizing makespan in manual order picking operations. The results of column generation heuristic is compared against a mixed integer programming model solved by Gurobi, and a parallel simulated annealing and ant colony optimization (PSA-ACO) previously proposed in the literature. Through numerical experiments, the superiority of CG heuristic compared to other methods is shown, and some managerial insights regarding the relationship between makespan optimization, workload balance, picking capacity, and number of pickers in order picking operations is presented.

在运营层面，订单拣选是履行中心的主要活动。在第三方物流公司的推动下，通过与第三方物流公司的合作，本研究提出了一种新颖的混合列生成（CG），遗传算法（GA）和人工神经网络（ANN）启发式方法，以最大程度地减少人工订单拣选操作中的制造时间。将列生成启发式的结果与Gurobi求解的混合整数规划模型以及文献中先前提出的并行模拟退火和蚁群优化（PSA-ACO）进行了比较。通过数值实验，展示了CG启发式方法与其他方法相比的优越性，以及有关管理优化，工作量平衡，拣选能力，