Comparison of static and dynamic order-picking in warehouse systems
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Author (Corporation)
Publication date
2026
Type of student thesis
Course of study
Collections
Type
04B - Conference paper
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Parent work
2026 8th International Symposium on Computational and Business Intelligence (ISCBI)
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Volume
Issue / Number
Pages / Duration
99-104
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Publisher / Publishing institution
IEEE
Place of publication / Event location
Bali
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Abstract
This study examines the optimization of orderpicking routes in warehouse logistics, with a focus on comparing static and dynamic models. The objective is to determine how different methods impact computational costs and efficiency. While dynamic models offer greater flexibility but require higher computational effort, static models, which use fixed order lists, perform well under stable conditions. Despite advancements in dynamic routing, a direct comparison of the performance of Ant Colony Optimization and Nearest Distance Optimization under such conditions has not yet been conducted. This study analyzes their efficiency through a structured comparison of order-picking models. This comparison is based on a simulated warehouse setup with orders that arrive dynamically during the ongoing picking process. The results show that Ant Colony Optimization achieves total travel distances during order picking which are 8% to 13% shorter than those from Nearest Distance Optimization.
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Subject (DDC)
Event
2026 8th International Symposium on Computational and Business Intelligence (ISCBI)
Exhibition start date
Exhibition end date
Conference start date
06.02.2026
Conference end date
08.02.2026
Date of the last check
ISBN
979-8-3315-5080-6
979-8-3315-5079-0
979-8-3315-5081-3
979-8-3315-5079-0
979-8-3315-5081-3
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
peer-reviewed
Open access category
Closed
Citation
Pathmanathan, M., Kica, V., Hanne, T., & Dornberger, R. (2026). Comparison of static and dynamic order-picking in warehouse systems. 2026 8th International Symposium on Computational and Business Intelligence (ISCBI), 99–104. https://doi.org/10.1109/iscbi69404.2026.11496244