IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v243y2015i3p839-851.html
   My bibliography  Save this article

Modeling and optimization control of a demand-driven, conveyor-serviced production station

Author

Listed:
  • Tang, Hao
  • Xu, Lingling
  • Sun, Jing
  • Chen, Yingjun
  • Zhou, Lei

Abstract

This study investigates the look-ahead control of a conveyor-serviced production station (CSPS), viewed as a production center, which is connected to a sales center. The production station is equipped with a buffer to temporarily store the parts that will flow into the product bank of the sales center after processing. The whole two-center system is characterized by random parts arrival, random customer demand, random processing time and limited buffer or bank capacities. Thus, the decision-making on the look-ahead range of such demand-driven CSPS is subject to the constraints of production and sales levels. In this paper, we will focus on modeling the stochastic control problem and providing solutions for finding the optimal look-ahead control policy under either average- or discounted-cost criteria. We first establish a detailed semi-Markov decision process for the look-ahead control of the demand-driven CSPS by combining the vacancies of both the buffer and the bank into one state, which can be solved by policy iteration or value iteration if the system parameters are known precisely. Then, to avoid the curse of dimensionality and modeling in the numerical optimization methods, we also propose a Q-learning algorithm combined with a simulated annealing technique to derive the approximate solutions. Simulation results are finally presented to show that by our established model and proposed optimization methods the system can achieve an optimal or suboptimal look-ahead control policy once the capacities of both the buffer and the bank are designed appropriately.

Suggested Citation

  • Tang, Hao & Xu, Lingling & Sun, Jing & Chen, Yingjun & Zhou, Lei, 2015. "Modeling and optimization control of a demand-driven, conveyor-serviced production station," European Journal of Operational Research, Elsevier, vol. 243(3), pages 839-851.
  • Handle: RePEc:eee:ejores:v:243:y:2015:i:3:p:839-851
    DOI: 10.1016/j.ejor.2015.01.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221715000296
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2015.01.009?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bertrand, J. Will M. & van Ooijen, Henny P.G., 2012. "The capacity investment decision for make-to-order production systems with demand rate control," International Journal of Production Economics, Elsevier, vol. 137(2), pages 272-283.
    2. W. M. Nawijn, 1985. "The Optimal Look-Ahead Policy for Admission to a Single Server System," Operations Research, INFORMS, vol. 33(3), pages 625-643, June.
    3. Ventura, José A. & Valdebenito, Victor A. & Golany, Boaz, 2013. "A dynamic inventory model with supplier selection in a serial supply chain structure," European Journal of Operational Research, Elsevier, vol. 230(2), pages 258-271.
    4. Li, Yanjie & Cao, Fang, 2013. "A basic formula for performance gradient estimation of semi-Markov decision processes," European Journal of Operational Research, Elsevier, vol. 224(2), pages 333-339.
    5. Stratos Ioannidis, 2013. "Joint production and quality control in production systems with two customer classes and lost sales," IISE Transactions, Taylor & Francis Journals, vol. 45(6), pages 605-616.
    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. Asadi, Amin & Nurre Pinkley, Sarah, 2021. "A stochastic scheduling, allocation, and inventory replenishment problem for battery swap stations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(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. Qiu, Ruozhen & Sun, Minghe & Lim, Yun Fong, 2017. "Optimizing (s, S) policies for multi-period inventory models with demand distribution uncertainty: Robust dynamic programing approaches," European Journal of Operational Research, Elsevier, vol. 261(3), pages 880-892.
    2. Duan, Lisha & Ventura, José A., 2019. "A Dynamic Supplier Selection and Inventory Management Model for a Serial Supply Chain with a Novel Supplier Price Break Scheme and Flexible Time Periods," European Journal of Operational Research, Elsevier, vol. 272(3), pages 979-998.
    3. Giri, B.C. & Bardhan, Sudarshan, 2017. "Sub-supply chain coordination in a three-layer chain under demand uncertainty and random yield in production," International Journal of Production Economics, Elsevier, vol. 191(C), pages 66-73.
    4. Feyzbakhsh, S. A. & Matsui, M. & Itai, K., 1998. "Optimal design of a generalized conveyor-serviced production station: Fixed and removal item cases," International Journal of Production Economics, Elsevier, vol. 55(2), pages 177-189, July.
    5. Wu, Kan & Yuan, Xue-Ming, 2016. "Optimal production-inventory policy for an integrated multi-stage supply chain with time-varying demandAuthor-Name: Zhao, Shi Tao," European Journal of Operational Research, Elsevier, vol. 255(2), pages 364-379.
    6. M. J. Hermoso-Orzáez & J. Garzón-Moreno, 2022. "Risk management methodology in the supply chain: a case study applied," Annals of Operations Research, Springer, vol. 313(2), pages 1051-1075, June.
    7. Haji Hosseinloo, Ashkan & Ryzhov, Alexander & Bischi, Aldo & Ouerdane, Henni & Turitsyn, Konstantin & Dahleh, Munther A., 2020. "Data-driven control of micro-climate in buildings: An event-triggered reinforcement learning approach," Applied Energy, Elsevier, vol. 277(C).
    8. Baller, Annelieke C. & Dabia, Said & Dullaert, Wout E.H. & Vigo, Daniele, 2019. "The Dynamic-Demand Joint Replenishment Problem with Approximated Transportation Costs," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1013-1033.
    9. Xia, Li & Shihada, Basem, 2015. "A Jackson network model and threshold policy for joint optimization of energy and delay in multi-hop wireless networks," European Journal of Operational Research, Elsevier, vol. 242(3), pages 778-787.
    10. Hlioui, Rached & Gharbi, Ali & Hajji, Adnène, 2017. "Joint supplier selection, production and replenishment of an unreliable manufacturing-oriented supply chain," International Journal of Production Economics, Elsevier, vol. 187(C), pages 53-67.
    11. José A. Ventura & Boaz Golany & Abraham Mendoza & Chenxi Li, 2022. "A multi-product dynamic supply chain inventory model with supplier selection, joint replenishment, and transportation cost," Annals of Operations Research, Springer, vol. 316(2), pages 729-762, September.
    12. Engebrethsen, Erna & Dauzère-Pérès, Stéphane, 2019. "Transportation mode selection in inventory models: A literature review," European Journal of Operational Research, Elsevier, vol. 279(1), pages 1-25.
    13. Jing, Fuying & Chao, Xiangrui, 2022. "Forecast horizons for a two-echelon dynamic lot-sizing problem," Omega, Elsevier, vol. 110(C).
    14. Firouz, Mohammad & Keskin, Burcu B. & Melouk, Sharif H., 2017. "An integrated supplier selection and inventory problem with multi-sourcing and lateral transshipments," Omega, Elsevier, vol. 70(C), pages 77-93.
    15. Xiaonong Lu & Baoqun Yin & Haipeng Zhang, 2016. "A reinforcement-learning approach for admission control in distributed network service systems," Journal of Combinatorial Optimization, Springer, vol. 31(3), pages 1241-1268, April.
    16. Dong, Chuanwen & Transchel, Sandra & Hoberg, Kai, 2018. "An inventory control model for modal split transport: A tailored base-surge approach," European Journal of Operational Research, Elsevier, vol. 264(1), pages 89-105.

    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:eee:ejores:v:243:y:2015:i:3:p:839-851. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.