A comprehensive review of batching problems in low-level picker-to-parts systems with order due dates: Main gaps, trade-offs, and prospects for future research

Literature has shown expressive demands for Picking Optimization Methods (POMs) that are better suited to the manual picking systems and Warehouses (WAs). There are still many gaps and trade-offs between the level of customer service and the WA efficiency for the so-called Joint Order Batching, Assignment and Sequencing, and Routing Problem (JOBASRP). Thus, the objective of this manuscript is to provide a literature review on the POMs proposed for the JOBASRP in WAs that deal with low-level picker-to-parts systems together with the order due dates and Stock Keeping Units. In the total of analyzed manuscripts (from 1960 to 2021), metaheuristics together with constructive heuristics obtained the best solutions for the JOBASRP. The most used technique is the Genetic Algorithm (50 %), followed by the Variable Neighborhood Search (40 %) and Iterated Local Search (10 %). The main performance indicators are Total Picking Time, Tardiness in Customer Orders and Computational Processing Time for POMs. In addition, we present a reflection focused on the main gaps, trade-offs, and prospects for improving the level of adaptation and performance demanded for the POMs. This manuscript provides eminent contributions to WAs management theory and practice and to the application of the so-called POMs. The theoretical framework presented summarizes the state of the art, defining the basic guidelines regarding new research opportunities, and supporting managers, students, and researchers in the optimization processes and decision making in WAs. We conclude that broader views on adaptation taxonomies and multi-objective algorithms will guide the inte-gration between different areas of the WAs and the development of better POMs.

Automated summary

This manuscript reviews the literature on Picking Optimization Methods (POMs) for the Joint Order Batching, Assignment and Sequencing, and Routing Problem (JOBASRP) in Warehouses (WAs). The study finds that metaheuristics and constructive heuristics, particularly Genetic Algorithm, are the most effective techniques for solving the JOBASRP. The main performance indicators for POMs are Total Picking Time, Tardiness in Customer Orders, and Computational Processing Time. The manuscript provides valuable insights for improving the adaptation and performance of POMs in WAs.