Abstract
Order Batching is a family of optimization problems related to the process of picking items in a warehouse as part of supply chain management. Problems classified into this category are those whose picking policy consists of grouping the orders received in a warehouse into batches, prior to starting the picking process. Once the batches have been formed, all items within the same batch are picked together on a single picking route. In this survey we review the optimization problems known in this family, focusing on manual picking systems and rectangular-shaped warehouses with only parallel and cross aisles, which is the most common warehouse configuration in the literature. First, we identify the decisions within the strategic, tactical, and operational levels that influence the picking task. Then, we characterize the optimization problems belonging to this family, which objective function might differ. The identified problems are classified into a taxonomy proposed in this paper, which is designed to host future problems within this family. We also review the most outstanding papers by category and the strategies and algorithms proposed for the most relevant activities: batching, routing, sequencing, waiting, and assigning. To conclude, we outline the open issues and future paths of the topic under study.
Abraham Duarte
Full Professor
Abraham Duarte is Full Professor in the Computer Science Department at the Rey Juan Carlos University (Madrid, Spain). He has done extensive research in the interface between computer science, artificial intelligence, and operations research to develop solution methods based on Computational Intelligence (metaheuristics) for practical problems in operations-management areas such as logistics and supply chains, telecommunications, decision-making under uncertainty and optimization of simulated systems.