With the recent increase in e-commerce, automated warehousing industries seek technology solutions providing high transaction rates with economic investment costs. In this context, the application of smart operational policies towards future smart factories’ concepts becomes a critical issue. With the help of recent IT and technological advancements towards Industry 4.0 developments, we study intelligent autonomous vehicle operation policies where vehicles can make decentralized decisions for their safe and flexible travels between multiple aisles in a warehouse. By that, instead of assigning vehicles within a dedicated zone, we allow vehicles to travel freely between multiple aisles. The advantage of such a travel policy might result in a reduced number of vehicle requirements in a warehouse compared to a dedicated path policy. However, the disadvantage of such a flexible travel policy might be the development of smart collision and deadlock control algorithms, and that travel of vehicles might result in increased travel time during their operation due to deadlock and collision cases. First, we develop a smart travel policy approach for the vehicles using an agent-based simulation modeling approach. Then, we apply a statistical method, analysis of variance (ANOVA), to identify which input design factors significantly affect the system performance. As a result, it is observed that the number of bays is the most significant factor affecting the performance of such a system.
Autonomous Vehicle Travel Between Multiple Aisles by Intelligent Agent-Based Modeling
Lect. Notes in Managem., Industrial Engineering
Global Joint Conference on Industrial Engineering and Its Application Areas ; 2021 October 30, 2021 - October 31, 2021
2022-08-25
14 pages
Article/Chapter (Book)
Electronic Resource
English
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