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Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem.
In: International Journal of Production Research, Jg. 54 (2016-02-15), Heft 4, S. 1196-1209
Online
academicJournal
Zugriff:
A two-robot flow-shop scheduling problem with n identical jobs and m machines is defined and evaluated for four robot collaboration levels corresponding to different levels of information sharing, learning and assessment:Full – robots work together, performing self and joint learning sharing full information; Pull – one robot decides when and if to learn from the other robot; Push – one robot may force the second to learn from it and None – each robot learns independently with no information sharing. Robots operate on parallel tracks, transporting jobs between successive machines, returning empty to a machine to move another job. The objective is to obtain a robot schedule that minimises makespan (Cmax) for machines with varying processing times. A new reinforcement learning algorithm is developed, using dual Q-learning functions. A novel feature in the collaborative algorithm is the assignment of different reward functions to robots; minimising robot idle time and minimising job waiting time. Such delays increase makespan. Simulation analyses with fast, medium and slow speed robots indicated that Full collaboration with a fast–fast robot pair was best according to minimum average upper bound error. The new collaborative algorithm provides a tool for finding optimal and near-optimal solutions to difficult collaborative multi-robot scheduling problems. [ABSTRACT FROM AUTHOR]
Titel: |
Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem.
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Autor/in / Beteiligte Person: | Arviv, Kfir ; Stern, Helman ; Edan, Yael |
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Zeitschrift: | International Journal of Production Research, Jg. 54 (2016-02-15), Heft 4, S. 1196-1209 |
Veröffentlichung: | 2016 |
Medientyp: | academicJournal |
ISSN: | 0020-7543 (print) |
DOI: | 10.1080/00207543.2015.1057297 |
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