A Hybrid Artificial Neural Network, Genetic Algorithm and Column Generation Heuristic for Minimizing Makespan in Manual Order Picking Operations

Abstract 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. To evaluate the performance of the algorithm and its gap to optimality, a Benders decomposition method is proposed that can obtain tight lower bounds compared to Gurobi and for small size problems. Additionally, the results of column generation heuristic is compared against a parallel simulated annealing and ant colony optimization (PSA-ACO) previously proposed in the literature. Through numerical experiments, 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.