An approach for the solution to order batching and sequencing in picking systems

This article discusses the use of Genetic Algorithms to solve a specific variation of the Order Batching and Sequencing Problem (OBSP) called Optimized Picking Sequence (OPS). Essentially, OPS is an optimization problem of the order picking in a typical Warehouse (WA) that employs a low-level picker-to-parts system with a pick-and-sort process. The OPS solution premise is to minimize the time and total cost of the picking processes in order to better adjust the trade-off between the level of customer service and the efficiency of this WA. To solve the OPS, we propose a computational tool called GA-OPS which is formulated by the iteration of two Genetic Algorithms (GA(BATCH) and GA(TSP)). GA(BATCH) groups products from different picking orders into picking batches to prevent earliness and tardiness in order fulfilment and to minimize the number of picking travels. GA(TSP) finds the best possible picking routes to reduce distance and picking total time for each batch. The OPS approach is an extension of the Optimal Billing Sequencing (OBS) problem and deals with practical dilemmas not addressed by the OBSP yet. The objective is to evaluate the GA-OPS performance in face of the conditions needed to adapt it to the real conditions of WAs. Experiments with problems of different complexity levels showed that the GA-OPS provides satisfactory quality solutions to any OPS instance. The iteration approach proposed to the OPS fills a gap in the literature and makes innovative contributions to advances towards developing and applying picking optimization methods which are more suitable to the reality of the mentioned WAs.