IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i4p2245-d750630.html
   My bibliography  Save this article

Task Allocation in Human–Machine Manufacturing Systems Using Deep Reinforcement Learning

Author

Listed:
  • Taejong Joo

    (Industrial Engineering & Management Sciences, Northwestern University, Evanston, IL 60208, USA)

  • Hyunyoung Jun

    (Industrial and Management Engineering Department, Hanyang University, Ansan 15588, Korea)

  • Dongmin Shin

    (Industrial and Management Engineering Department, Hanyang University, Ansan 15588, Korea)

Abstract

Catering for human operators is a critical aspect in the sustainability of a manufacturing sector. This paper presents a task allocation problem in human–machine manufacturing systems. A key aspect of this problem is to carefully consider the characteristics of human operators having different task preferences and capabilities. However, the characteristics of human operators are usually implicit, which makes the mathematical formulation of the problem difficult. In addition, variability in manufacturing systems such as job completion and machine breakdowns are prevalent. To address these challenges, this paper proposes a deep reinforcement learning-based approach to accommodate the unobservable characteristics of human operators and the stochastic nature of manufacturing systems. Historical data accumulated in the process of job assignment are exploited to allocate tasks to either humans or machines. We demonstrate that the proposed model accommodates task competence and fatigue levels of individual human operators into job assignments, thereby improving scheduling-related performance measures compared to classical dispatching rules.

Suggested Citation

  • Taejong Joo & Hyunyoung Jun & Dongmin Shin, 2022. "Task Allocation in Human–Machine Manufacturing Systems Using Deep Reinforcement Learning," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2245-:d:750630
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/4/2245/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/4/2245/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Digiesi, Salvatore & Kock, Ad A.A. & Mummolo, Giovanni & Rooda, Jacobus E., 2009. "The effect of dynamic worker behavior on flow line performance," International Journal of Production Economics, Elsevier, vol. 120(2), pages 368-377, August.
    2. Xiong, Jian & Xing, Li-ning & Chen, Ying-wu, 2013. "Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns," International Journal of Production Economics, Elsevier, vol. 141(1), pages 112-126.
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    4. Trentesaux, Damien & Moray, Neville & Tahon, Christian, 1998. "Integration of the human operator into responsive discrete production management systems," European Journal of Operational Research, Elsevier, vol. 109(2), pages 342-361, September.
    5. Alan S. Manne, 1960. "On the Job-Shop Scheduling Problem," Operations Research, INFORMS, vol. 8(2), pages 219-223, April.
    6. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    2. Neha Soni & Enakshi Khular Sharma & Narotam Singh & Amita Kapoor, 2019. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models," Papers 1905.02092, arXiv.org.
    3. Oleh Lukianykhin & Tetiana Bogodorova, 2021. "Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-22, April.
    4. Chen, Jiaxin & Shu, Hong & Tang, Xiaolin & Liu, Teng & Wang, Weida, 2022. "Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment," Energy, Elsevier, vol. 239(PC).
    5. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    6. Zhang, Yihao & Chai, Zhaojie & Lykotrafitis, George, 2021. "Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    7. Yifeng Guo & Xingyu Fu & Yuyan Shi & Mingwen Liu, 2018. "Robust Log-Optimal Strategy with Reinforcement Learning," Papers 1805.00205, arXiv.org.
    8. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    9. Chengmin Zhou & Bingding Huang & Pasi Fränti, 2022. "A review of motion planning algorithms for intelligent robots," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 387-424, February.
    10. Yingfei Wang & Inbal Yahav & Balaji Padmanabhan, 2024. "Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19," Information Systems Research, INFORMS, vol. 35(1), pages 120-144, March.
    11. Iwao Maeda & David deGraw & Michiharu Kitano & Hiroyasu Matsushima & Hiroki Sakaji & Kiyoshi Izumi & Atsuo Kato, 2020. "Deep Reinforcement Learning in Agent Based Financial Market Simulation," JRFM, MDPI, vol. 13(4), pages 1-17, April.
    12. Esmaeili Aliabadi, Danial & Chan, Katrina, 2022. "The emerging threat of artificial intelligence on competition in liberalized electricity markets: A deep Q-network approach," Applied Energy, Elsevier, vol. 325(C).
    13. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
    14. Bo Hu & Jiaxi Li & Shuang Li & Jie Yang, 2019. "A Hybrid End-to-End Control Strategy Combining Dueling Deep Q-network and PID for Transient Boost Control of a Diesel Engine with Variable Geometry Turbocharger and Cooled EGR," Energies, MDPI, vol. 12(19), pages 1-15, September.
    15. Hao, Peng & Wei, Zhensong & Bai, Zhengwei & Barth, Matthew J., 2020. "Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions," Institute of Transportation Studies, Working Paper Series qt2fv5063b, Institute of Transportation Studies, UC Davis.
    16. Tambet Matiisen & Aqeel Labash & Daniel Majoral & Jaan Aru & Raul Vicente, 2022. "Do Deep Reinforcement Learning Agents Model Intentions?," Stats, MDPI, vol. 6(1), pages 1-17, December.
    17. Biemann, Marco & Scheller, Fabian & Liu, Xiufeng & Huang, Lizhen, 2021. "Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control," Applied Energy, Elsevier, vol. 298(C).
    18. Brini, Alessio & Tantari, Daniele, 2023. "Deep reinforcement trading with predictable returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    19. Ngo, Vu Minh & Nguyen, Huan Huu & Van Nguyen, Phuc, 2023. "Does reinforcement learning outperform deep learning and traditional portfolio optimization models in frontier and developed financial markets?," Research in International Business and Finance, Elsevier, vol. 65(C).
    20. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2245-:d:750630. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.