IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v60y2023i2d10.1007_s12597-023-00632-5.html
   My bibliography  Save this article

Markov interval chain (MIC) for solving a decision problem

Author

Listed:
  • Salah eddine Semati

    (University of Msila)

  • Abdelkader Gasmi

    (University of Msila)

Abstract

One of the main missions of a certain company is to predict its future for reasons of continuity, which reflect the balance of its long term, in various aspects. In this work, we propose the use of Markov Interval Chain models to help business leaders to make better decisions. The proposed model consists in considering the numbers of customers declared by each company, which are discrete values as centers of symmetric intervals. By this, we have avoided the problem of increase and decrease in the number of customers for each company. As an example, we applied this model to predict the distribution of market shares in the later period as a probability distribution intervals, which provides information’s for companies to make decisions, and it gave satisfactory results.

Suggested Citation

  • Salah eddine Semati & Abdelkader Gasmi, 2023. "Markov interval chain (MIC) for solving a decision problem," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 802-811, June.
    • Handle: RePEc:spr:opsear:v:60:y:2023:i:2:d:10.1007_s12597-023-00632-5
    DOI: 10.1007/s12597-023-00632-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-023-00632-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12597-023-00632-5?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. J. K. Satia & R. E. Lave, 1973. "Markovian Decision Processes with Probabilistic Observation of States," Management Science, INFORMS, vol. 20(1), pages 1-13, September.
    2. Jay K. Satia & Roy E. Lave, 1973. "Markovian Decision Processes with Uncertain Transition Probabilities," Operations Research, INFORMS, vol. 21(3), pages 728-740, June.
    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. Blanc, J.P.C. & den Hertog, D., 2008. "On Markov Chains with Uncertain Data," Other publications TiSEM b44dfb0a-1676-4ce3-8d16-f, Tilburg University, School of Economics and Management.
    2. Erick Delage & Shie Mannor, 2010. "Percentile Optimization for Markov Decision Processes with Parameter Uncertainty," Operations Research, INFORMS, vol. 58(1), pages 203-213, February.
    3. Wolfram Wiesemann & Daniel Kuhn & Berç Rustem, 2013. "Robust Markov Decision Processes," Mathematics of Operations Research, INFORMS, vol. 38(1), pages 153-183, February.
    4. V Varagapriya & Vikas Vikram Singh & Abdel Lisser, 2023. "Joint chance-constrained Markov decision processes," Annals of Operations Research, Springer, vol. 322(2), pages 1013-1035, March.
    5. Zeynep Turgay & Fikri Karaesmen & Egemen Lerzan Örmeci, 2018. "Structural properties of a class of robust inventory and queueing control problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(8), pages 699-716, December.
    6. Andrew J. Keith & Darryl K. Ahner, 2021. "A survey of decision making and optimization under uncertainty," Annals of Operations Research, Springer, vol. 300(2), pages 319-353, May.
    7. Hyeong Chang, 2006. "Perfect information two-person zero-sum markov games with imprecise transition probabilities," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 64(2), pages 335-351, October.
    8. Peter Buchholz & Dimitri Scheftelowitsch, 2019. "Computation of weighted sums of rewards for concurrent MDPs," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 89(1), pages 1-42, February.
    9. Arnab Nilim & Laurent El Ghaoui, 2005. "Robust Control of Markov Decision Processes with Uncertain Transition Matrices," Operations Research, INFORMS, vol. 53(5), pages 780-798, October.
    10. Zeynep Turgay & Fikri Karaesmen & E. Örmeci, 2015. "A dynamic inventory rationing problem with uncertain demand and production rates," Annals of Operations Research, Springer, vol. 231(1), pages 207-228, August.
    11. Schapaugh, Adam W. & Tyre, Andrew J., 2013. "Accounting for parametric uncertainty in Markov decision processes," Ecological Modelling, Elsevier, vol. 254(C), pages 15-21.
    12. Wolfram Wiesemann & Daniel Kuhn & Berç Rustem, 2010. "Robust Markov Decision Processes," Working Papers 034, COMISEF.
    13. Hao Zhang, 2010. "Partially Observable Markov Decision Processes: A Geometric Technique and Analysis," Operations Research, INFORMS, vol. 58(1), pages 214-228, February.
    14. Nicholas J. J. Smith, 2023. "Acting on belief functions," Theory and Decision, Springer, vol. 95(4), pages 575-621, November.
    15. David L. Kaufman & Andrew J. Schaefer, 2013. "Robust Modified Policy Iteration," INFORMS Journal on Computing, INFORMS, vol. 25(3), pages 396-410, August.
    16. Garud N. Iyengar, 2005. "Robust Dynamic Programming," Mathematics of Operations Research, INFORMS, vol. 30(2), pages 257-280, May.
    17. D. Škulj & R. Hable, 2013. "Coefficients of ergodicity for Markov chains with uncertain parameters," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(1), pages 107-133, January.
    18. Susana Díaz-Vázquez & Emilio Torres-Manzanera & Irene Díaz & Susana Montes, 2021. "On the Search for a Measure to Compare Interval-Valued Fuzzy Sets," Mathematics, MDPI, vol. 9(24), pages 1-30, December.
    19. Zhu, Zhicheng & Xiang, Yisha & Zhao, Ming & Shi, Yue, 2023. "Data-driven remanufacturing planning with parameter uncertainty," European Journal of Operational Research, Elsevier, vol. 309(1), pages 102-116.
    20. Xiaoting Ji & Yifeng Niu & Lincheng Shen, 2016. "Robust Satisficing Decision Making for Unmanned Aerial Vehicle Complex Missions under Severe Uncertainty," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-35, 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:spr:opsear:v:60:y:2023:i:2:d:10.1007_s12597-023-00632-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.