Publication
Title
Solving the storage location assignment problem using reinforcement learning
Author
Abstract
In this work, we deal with the Storage Location Assignment Problem, often referred to as the SLAP, in an E-commerce Distribution Center (EDC). With E-commerce steadily increasing in popularity over the past decades, it has become a key part of the logistics industry. Due to the direct link with the customer, EDC's are forced into a significantly more complex and dynamic order picking process compared to conventional Bulk Distribution Centers. As a result of these challenges, many traditional approaches such as genetic algorithms and rule-based methods reach only suboptimal solutions. We propose the use of Reinforcement Learning (RL) to solve the SLAP, leading to a solution that adapts to dynamically changing environment parameters during runtime. For this purpose, we define a model that transforms the SLAP into a sequential decision making problem. We validate this novel approach by training a state-of-the-art RL algorithm within this model and comparing its results with a benchmark genetic algorithm approach. We conclude that the RL algorithm achieves promising results, surpassing benchmark performance and nearing optimal performance in a small-scale warehouse environment.
Language
English
Source (book)
ICMAI 2023 : 8th International Conference on Mathematics and Artificial Intelligence, 7-9 April, 2023, Chongqing, China
Publication
New York, N.Y. : Association for Computing Machinery , 2023
ISBN
978-1-4503-9998-2
978-1-4503-9998-2
DOI
10.1145/3594300.3594314
Volume/pages
p. 89-95
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identifier
Creation 21.03.2024
Last edited 22.03.2024
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