IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v52y2022i1p56-68.html
   My bibliography  Save this article

Lenovo Schedules Laptop Manufacturing Using Deep Reinforcement Learning

Author

Listed:
  • Yi Liang

    (AI Laboratory, Lenovo Research, Beijing 100193, China)

  • Zan Sun

    (AI Laboratory, Lenovo Research, Beijing 100193, China)

  • Tianheng Song

    (AI Laboratory, Lenovo Research, Beijing 100193, China)

  • Qiang Chou

    (AI Laboratory, Lenovo Research, Beijing 100193, China)

  • Wei Fan

    (AI Laboratory, Lenovo Research, Beijing 100193, China)

  • Jianping Fan

    (AI Laboratory, Lenovo Research, Beijing 100193, China)

  • Yong Rui

    (AI Laboratory, Lenovo Research, Beijing 100193, China)

  • Qiping Zhou

    (LCFC, Lenovo, Hefei 230601, China)

  • Jessie Bai

    (LCFC, Lenovo, Hefei 230601, China)

  • Chun Yang

    (LCFC, Lenovo, Hefei 230601, China)

  • Peng Bai

    (LCFC, Lenovo, Hefei 230601, China)

Abstract

Lenovo Research teamed with members of the factory operations group at Lenovo’s largest laptop manufacturing facility, LCFC, to replace a manual production scheduling system with a decision-making platform built on a deep reinforcement learning architecture. The system schedules production orders at all LCFC’s 43 assembly manufacturing lines, balancing the relative priorities of production volume, changeover cost, and order fulfillment. The multiobjective optimization scheduling problem is solved using a deep reinforcement learning model. The approach combines high computing efficiency with a novel masking mechanism that enforces operational constraints to ensure that the machine-learning model does not waste time exploring infeasible solutions. The use of the new model transformed the production management process enabling a 20% reduction in the backlog of production orders and a 23% improvement in the fulfillment rate. It also reduced the entire scheduling process from six hours to 30 minutes while it retained multiobjective flexibility to allow LCFC to adjust quickly to changing objectives. The work led to increased revenue of US $1.91 billion in 2019 and US $2.69 billion in 2020 for LCFC. The methodology can be applied to other scenarios in the industry.

Suggested Citation

  • Yi Liang & Zan Sun & Tianheng Song & Qiang Chou & Wei Fan & Jianping Fan & Yong Rui & Qiping Zhou & Jessie Bai & Chun Yang & Peng Bai, 2022. "Lenovo Schedules Laptop Manufacturing Using Deep Reinforcement Learning," Interfaces, INFORMS, vol. 52(1), pages 56-68, January.
  • Handle: RePEc:inm:orinte:v:52:y:2022:i:1:p:56-68
    DOI: 10.1287/inte.2021.1109
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2021.1109
    Download Restriction: no

    File URL: https://libkey.io/10.1287/inte.2021.1109?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. He, Zhiliang & Thürer, Matthias & Zhou, Wanling, 2024. "The use of reinforcement learning for material flow control: An assessment by simulation," International Journal of Production Economics, Elsevier, vol. 274(C).

    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. Gong, Manlin & Hu, Yucong & Chen, Zhiwei & Li, Xiaopeng, 2021. "Transfer-based customized modular bus system design with passenger-route assignment optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    2. Smith, John Paul, 1974. "A Lockset analysis of farm to plant milk assembly," ISU General Staff Papers 1974010108000018144, Iowa State University, Department of Economics.
    3. Jumbo, Olga & Moghaddass, Ramin, 2022. "Resource optimization and image processing for vegetation management programs in power distribution networks," Applied Energy, Elsevier, vol. 319(C).
    4. Martins, Sara & Ostermeier, Manuel & Amorim, Pedro & Hübner, Alexander & Almada-Lobo, Bernardo, 2019. "Product-oriented time window assignment for a multi-compartment vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 276(3), pages 893-909.
    5. Zi-bin Jiang & Qiong Yang, 2016. "A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-15, November.
    6. A. Scholz & G. Wäscher, 2017. "Order Batching and Picker Routing in manual order picking systems: the benefits of integrated routing," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 491-520, June.
    7. Qi, Mingyao & Lin, Wei-Hua & Li, Nan & Miao, Lixin, 2012. "A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 248-257.
    8. Srinivas, Sharan & Ramachandiran, Surya & Rajendran, Suchithra, 2022. "Autonomous robot-driven deliveries: A review of recent developments and future directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    9. Derigs, U. & Kaiser, R., 2007. "Applying the attribute based hill climber heuristic to the vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 177(2), pages 719-732, March.
    10. Almoustafa, Samira & Hanafi, Said & Mladenović, Nenad, 2013. "New exact method for large asymmetric distance-constrained vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 226(3), pages 386-394.
    11. César Rego, 1998. "A Subpath Ejection Method for the Vehicle Routing Problem," Management Science, INFORMS, vol. 44(10), pages 1447-1459, October.
    12. van Gils, Teun & Ramaekers, Katrien & Braekers, Kris & Depaire, Benoît & Caris, An, 2018. "Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions," International Journal of Production Economics, Elsevier, vol. 197(C), pages 243-261.
    13. Fleming, Christopher L. & Griffis, Stanley E. & Bell, John E., 2013. "The effects of triangle inequality on the vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 224(1), pages 1-7.
    14. Kafle, Nabin & Zou, Bo & Lin, Jane, 2017. "Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 62-82.
    15. Imai, Akio & Nishimura, Etsuko & Current, John, 2007. "A Lagrangian relaxation-based heuristic for the vehicle routing with full container load," European Journal of Operational Research, Elsevier, vol. 176(1), pages 87-105, January.
    16. Andreas Stenger & Daniele Vigo & Steffen Enz & Michael Schwind, 2013. "An Adaptive Variable Neighborhood Search Algorithm for a Vehicle Routing Problem Arising in Small Package Shipping," Transportation Science, INFORMS, vol. 47(1), pages 64-80, February.
    17. Müller, Juliane, 2010. "Approximative solutions to the bicriterion Vehicle Routing Problem with Time Windows," European Journal of Operational Research, Elsevier, vol. 202(1), pages 223-231, April.
    18. Patrik Eveborn & Mikael Rönnqvist & Helga Einarsdóttir & Mats Eklund & Karin Lidén & Marie Almroth, 2009. "Operations Research Improves Quality and Efficiency in Home Care," Interfaces, INFORMS, vol. 39(1), pages 18-34, February.
    19. Martinhon, Carlos & Lucena, Abilio & Maculan, Nelson, 2004. "Stronger K-tree relaxations for the vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 158(1), pages 56-71, October.
    20. Hernan Caceres & Rajan Batta & Qing He, 2017. "School Bus Routing with Stochastic Demand and Duration Constraints," Transportation Science, INFORMS, vol. 51(4), pages 1349-1364, November.

    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:inm:orinte:v:52:y:2022:i:1:p:56-68. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.