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Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem

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  • Kfir Arviv
  • Helman Stern
  • Yael Edan

Abstract

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 ( C max ) 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.

Suggested Citation

  • Kfir Arviv & Helman Stern & Yael Edan, 2016. "Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1196-1209, February.
  • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:4:p:1196-1209
    DOI: 10.1080/00207543.2015.1057297
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    Cited by:

    1. Behice Meltem Kayhan & Gokalp Yildiz, 2023. "Reinforcement learning applications to machine scheduling problems: a comprehensive literature review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 905-929, March.
    2. Constantin Waubert de Puiseau & Richard Meyes & Tobias Meisen, 2022. "On reliability of reinforcement learning based production scheduling systems: a comparative survey," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 911-927, April.
    3. Shun Jia & Yang Yang & Shuyu Li & Shang Wang & Anbang Li & Wei Cai & Yang Liu & Jian Hao & Luoke Hu, 2024. "The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    4. Andreas Kuhnle & Jan-Philipp Kaiser & Felix Theiß & Nicole Stricker & Gisela Lanza, 2021. "Designing an adaptive production control system using reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 855-876, March.

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