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Research Article

Scheduling of autonomous mobile robots with conflict-free routes utilising contextual-bandit-based local search

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Pages 4090-4116 | Received 09 Feb 2021, Accepted 31 Mar 2022, Published online: 30 Apr 2022
 

Abstract

As autonomous robot and sensor technologies have advanced, utilisation of autonomous mobile robots (AMRs) in material handling has grown quickly, owing especially to their scalability and versatility compared with automated guided vehicles (AGVs). In order to take full advantage of AMRs, in this paper, we address an AMR scheduling and routing problem by dividing the entire problem into three sub-problems: path finding, vehicle routing, and conflict resolution. We first discuss the previous literature on characteristics of each sub-problem. We then present a comprehensive framework for minimising total tardiness of transportation requests with consideration of conflicts between routes. First, the shortest paths between all locations are calculated with A*. Based on the shortest paths, for vehicle routing, we propose a new local search algorithm called COntextual-Bandit-based Adaptive Local search with Tree-based regression (COBALT), which utilises the contextual bandit to select the best operator in consideration of contexts. After routing of AMRs, an agent-based model with states and protocols resolves collisions and deadlocks in a decentralised way. The results indicate that the proposed framework can improve the performance of AMR scheduling for conflict-free routes and that, especially for vehicle routing, COBALT outperforms the other algorithms in terms of average total tardiness.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acknowledgement

This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) under Grant NRF-2021R1C1C1003433 funded by the Ministry of Education, and in part by the Dongguk University Research Fund of 2021.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Additional information

Notes on contributors

Sungbum Jun

Sungbum Jun received his B.S. and M.S. degrees in Industrial Engineering from Seoul National University (Seoul, South Korea) in 2012 and 2014, respectively, and his Ph.D. degree in Industrial Engineering from Purdue University in 2020. He currently is an Assistant Professor in the Department of Industrial and Systems Engineering in Dongguk University (Seoul, South Korea). His research interests are optimization algorithms, intelligent manufacturing systems, machine-learning applications, production scheduling, and logistics.

Chul Hun Choi

ChulHun Choi currently serves as Assistant Professor in Business Administration, Sejong University, South Korea. He received his Ph.D. in Industrial Engineering from Purdue University. His research interests cover smart manufacturing/logistics, sustainable business operation, and renewable energy. His works have been presented in Informs and Institute of Industrial & Systems Engineers, and International Symposium on Sustainable Systems and Technology annual conferences.

Seokcheon Lee

Seokcheon Lee received his B.S. and M.S. degrees in Industrial Engineering from Seoul National University (Seoul, South Korea) in 1991 and 1993, respectively, and his Ph.D. degree in Industrial Engineering from Pennsylvania State University (PA, USA) in 2005. He currently is an Associate Professor in the School of Industrial Engineering, Purdue University (West Lafayette, IN, USA). Among his current research interests are optimization techniques from multidisciplinary perspectives and distributed control for logistics.

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