Institut für Lebensmittelwissenschaft und Biotechnologie

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/6

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Optimizing storage assignment, order picking, and their interaction in mezzanine warehouses
(2023) Lesch, Veronika; Müller, Patrick B.M.; Krämer, Moritz; Hadry, Marius; Kounev, Samuel; Krupitzer, Christian; Lesch, Veronika; University of Würzburg, Würzburg, Germany; Müller, Patrick B.M.; University of Applied Sciences Würzburg-Schweinfurt, Würzburg, Germany; Krämer, Moritz; io-consultants GmbH, Co. KG, Heidelberg, Germany; Hadry, Marius; University of Würzburg, Würzburg, Germany; Kounev, Samuel; University of Würzburg, Würzburg, Germany; Krupitzer, Christian; University of Hohenheim, Stuttgart, Germany
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.
Tackling the rich vehicle routing problem with nature-inspired algorithms
(2022) Lesch, Veronika; König, Maximilian; Kounev, Samuel; Stein, Anthony; Krupitzer, Christian; Lesch, Veronika; University of Würzburg, Würzburg, Germany; König, Maximilian; PASS Logistics Solutions AG, Aschaffenburg, Germany; Kounev, Samuel; University of Würzburg, Würzburg, Germany; Stein, Anthony; University of Hohenheim, Hohenheim, Germany; Krupitzer, Christian; University of Hohenheim, Hohenheim, Germany
In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.

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Browsing Institut für Lebensmittelwissenschaft und Biotechnologie by Journal "Applied intelligence"