A Case Study on Optimization of Warehouses

TL;DR

This paper presents a combined optimization approach for warehouse storage and order picking that considers economic and ergonomic factors, improving overall efficiency by modeling their interdependence.

Contribution

It introduces a customized NSGA-II and an ACO algorithm that incorporate multiple constraints and their interaction, advancing warehouse optimization methods.

Findings

Proposed algorithms outperform traditional techniques in key quality metrics.

Combining storage and picking optimizations yields better overall performance.

Algorithms effectively handle multiple economic and ergonomic constraints.

Abstract

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.