A Case Study on Optimization of Warehouses
This work addresses warehouse efficiency by jointly optimizing storage and picking with multi-objective algorithms, though it is incremental as it builds on existing methods like NSGA-II and ACO.
The authors tackled the joint optimization of storage assignment and order picking in warehouses by proposing a customized NSGA-II algorithm for storage assignment and an Ant Colony Optimization algorithm for order picking, which incorporate multiple economic and ergonomic constraints and their interdependence. Their evaluation showed better performance in storage assignments and order pick routes compared to common techniques across five quality indicators, with further improvement when combining both algorithms.
In warehouses, order picking is known to be the most labor-intensive and costly task in which the employees account for a large part of the warehouse performance. Hence, many approaches exist, that optimize the order picking process based on diverse economic criteria. However, most of these approaches focus on a single economic objective at once and disregard ergonomic criteria in their optimization. Further, the influence of the placement of the items to be picked is underestimated and accordingly, too little attention is paid to the interdependence of these two problems. In this work, we aim at optimizing the storage assignment and the order picking problem within mezzanine warehouse with regards to their reciprocal influence. We propose a customized version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing the storage assignment problem as well as an Ant Colony Optimization (ACO) algorithm for optimizing the order picking problem. Both algorithms incorporate multiple economic and ergonomic constraints simultaneously. Furthermore, the algorithms incorporate knowledge about the interdependence between both problems, aiming to improve the overall warehouse performance. Our evaluation results show that our proposed algorithms return better storage assignments and order pick routes compared to commonly used techniques for the following quality indicators for comparing Pareto fronts: Coverage, Generational Distance, Euclidian Distance, Pareto Front Size, and Inverted Generational Distance. Additionally, the evaluation regarding the interaction of both algorithms shows a better performance when combining both proposed algorithms.