IDEAS home Printed from https://ideas.repec.org/a/spr/mathme/v83y2016i1d10.1007_s00186-015-0517-x.html
   My bibliography  Save this article

Performance analysis of a reflected fluid production/inventory model

Author

Listed:
  • Yonit Barron

    (Ariel Univertsity)

Abstract

We study the performance of a reflected fluid production/inventory model operating in a stochastic environment that is modulated by a finite state continuous time Markov chain. The process alternates between ON and OFF periods. The ON period is switched to OFF when the content level reaches a predetermined level q and returns to ON when it drops to 0. The ON/OFF periods generate an alternative renewal process. Applying a matrix analytic approach, fluid flow techniques and martingales, we develop methods to obtain explicit formulas for the cost functionals (setup, holding, production and lost demand costs) in the discounted case and under the long-run average criterion. Numerical examples present the trade-off between the holding cost and the loss cost and show that the total cost appears to be a convex function of q.

Suggested Citation

  • Yonit Barron, 2016. "Performance analysis of a reflected fluid production/inventory model," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 83(1), pages 1-31, February.
  • Handle: RePEc:spr:mathme:v:83:y:2016:i:1:d:10.1007_s00186-015-0517-x
    DOI: 10.1007/s00186-015-0517-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00186-015-0517-x
    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/s00186-015-0517-x?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. Onno Boxma & David Perry & Shelley Zacks, 2015. "A Fluid EOQ Model of Perishable Items with Intermittent High and Low Demand Rates," Mathematics of Operations Research, INFORMS, vol. 40(2), pages 390-402, February.
    2. V. Ramaswami, 2006. "Passage Times in Fluid Models with Application to Risk Processes," Methodology and Computing in Applied Probability, Springer, vol. 8(4), pages 497-515, December.
    3. Oded Berman & David Perry & Wolfgang Stadje, 2007. "Performance Analysis of a Fluid Production/Inventory Model with State-dependence," Methodology and Computing in Applied Probability, Springer, vol. 9(4), pages 465-481, December.
    4. Mohebbi, Esmail, 2006. "A production-inventory model with randomly changing environmental conditions," European Journal of Operational Research, Elsevier, vol. 174(1), pages 539-552, October.
    5. Fleischmann, Moritz & Kuik, Roelof & Dekker, Rommert, 2002. "Controlling inventories with stochastic item returns: A basic model," European Journal of Operational Research, Elsevier, vol. 138(1), pages 63-75, April.
    6. Germs, Remco & Foreest, Nicky D. van, 2014. "Optimal Control of Production-Inventory Systems with Constant and Compound Poisson Demand," Research Report 14001-OPERA, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    7. Junmin Shi & Michael Katehakis & Benjamin Melamed, 2013. "Martingale methods for pricing inventory penalties under continuous replenishment and compound renewal demands," Annals of Operations Research, Springer, vol. 208(1), pages 593-612, September.
    8. Pinçe, Çerag & Gürler, Ülkü & Berk, Emre, 2008. "A continuous review replenishment-disposal policy for an inventory system with autonomous supply and fixed disposal costs," European Journal of Operational Research, Elsevier, vol. 190(2), pages 421-442, October.
    9. Shen, Yuelin & Willems, Sean P., 2014. "Modeling sourcing strategies to mitigate part obsolescence," European Journal of Operational Research, Elsevier, vol. 236(2), pages 522-533.
    10. Grunow, M. & Gunther, H.-O. & Westinner, R., 2007. "Supply optimization for the production of raw sugar," International Journal of Production Economics, Elsevier, vol. 110(1-2), pages 224-239, October.
    11. Song, Yuyue & Lau, Hoong Chuin, 2004. "A periodic-review inventory model with application to the continuous-review obsolescence problem," European Journal of Operational Research, Elsevier, vol. 159(1), pages 110-120, November.
    12. Jim (Junmin) Shi & Michael N. Katehakis & Benjamin Melamed & Yusen Xia, 2014. "Production-Inventory Systems with Lost Sales and Compound Poisson Demands," Operations Research, INFORMS, vol. 62(5), pages 1048-1063, October.
    13. R. G. Vickson, 1986. "A Single Product Cycling Problem Under Brownian Motion Demand," Management Science, INFORMS, vol. 32(10), pages 1336-1345, October.
    14. repec:dgr:rugsom:14001-opera is not listed on IDEAS
    15. Jing-Sheng Song & Paul Zipkin, 1993. "Inventory Control in a Fluctuating Demand Environment," Operations Research, INFORMS, vol. 41(2), pages 351-370, April.
    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. (Ai-Chih) Chang, Jasmine & Lu, Haibing & (Junmin) Shi, Jim, 2019. "Stockout risk of production-inventory systems with compound Poisson demands," Omega, Elsevier, vol. 83(C), pages 181-198.
    2. Baek, Jung Woo & Bae, Yun Han, 2022. "A queuing-inventory model for manufacturing systems with fluid-type inventory," Omega, Elsevier, vol. 111(C).
    3. Yonit Barron, 2022. "A probabilistic approach to the stochastic fluid cash management balance problem," Annals of Operations Research, Springer, vol. 312(2), pages 607-645, May.

    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. Yonit Barron, 2016. "Performance analysis of a reflected fluid production/inventory model," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 83(1), pages 1-31, February.
    2. Barron, Yonit, 2016. "Clearing control policies for MAP inventory process with lost sales," European Journal of Operational Research, Elsevier, vol. 251(2), pages 495-508.
    3. Yonit Barron & Dror Hermel, 2017. "Shortage decision policies for a fluid production model with MAP arrivals," International Journal of Production Research, Taylor & Francis Journals, vol. 55(14), pages 3946-3969, July.
    4. Yonit Barron & David Perry & Wolfgang Stadje, 2016. "A make-to-stock production/inventory model with MAP arrivals and phase-type demands," Annals of Operations Research, Springer, vol. 241(1), pages 373-409, June.
    5. Onno Boxma & David Perry & Wolfgang Stadje & Shelley Zacks, 2022. "A compound Poisson EOQ model for perishable items with intermittent high and low demand periods," Annals of Operations Research, Springer, vol. 317(2), pages 439-459, October.
    6. Azoury, Katy S. & Miyaoka, Julia, 2020. "Optimal and simple approximate solutions to a production-inventory system with stochastic and deterministic demand," European Journal of Operational Research, Elsevier, vol. 286(1), pages 178-189.
    7. Mohebbi, E., 2008. "A note on a production control model for a facility with limited storage capacity in a random environment," European Journal of Operational Research, Elsevier, vol. 190(2), pages 562-570, October.
    8. van Jaarsveld, Willem & Dekker, Rommert, 2011. "Estimating obsolescence risk from demand data to enhance inventory control--A case study," International Journal of Production Economics, Elsevier, vol. 133(1), pages 423-431, September.
    9. Klosterhalfen, Steffen T. & Holzhauer, Falk & Fleischmann, Moritz, 2018. "Control of a continuous production inventory system with production quantity restrictions," European Journal of Operational Research, Elsevier, vol. 268(2), pages 569-581.
    10. repec:dgr:rugsom:14001-opera is not listed on IDEAS
    11. Arifoglu, Kenan & Özekici, Süleyman, 2011. "Inventory management with random supply and imperfect information: A hidden Markov model," International Journal of Production Economics, Elsevier, vol. 134(1), pages 123-137, November.
    12. Sandun C. Perera & Suresh P. Sethi, 2023. "A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Continuous‐time case," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 154-169, January.
    13. Walid W. Nasr, 2022. "Inventory systems with stochastic and batch demand: computational approaches," Annals of Operations Research, Springer, vol. 309(1), pages 163-187, February.
    14. Apostolos Burnetas & Odysseas Kanavetas, 2018. "Inventory policies for two products under Poisson demand: Interaction between demand substitution, limited storage capacity and replenishment time uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(8), pages 676-698, December.
    15. Wen Chen & Adam J. Fleischhacker & Michael N. Katehakis, 2015. "Dynamic pricing in a dual‐market environment," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(7), pages 531-549, October.
    16. Benjamin Melamed & Rudolf Leuschner & Weiwei Chen & Dale S. Rogers & Min Cao, 2022. "Inventory turns and finite-horizon Little’s Laws," Annals of Operations Research, Springer, vol. 317(1), pages 129-146, October.
    17. Germs, Remco & Foreest, Nicky D. van, 2014. "Optimal Control of Production-Inventory Systems with Constant and Compound Poisson Demand," Research Report 14001-OPERA, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    18. Arnoud den Boer & Ohad Perry & Bert Zwart, 2018. "Dynamic pricing policies for an inventory model with random windows of opportunities," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(8), pages 660-675, December.
    19. Fernando Alvarez & Francesco Lippi & Roberto Robatto, 2019. "Cost of Inflation in Inventory Theoretical Models," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 32, pages 206-226, April.
    20. Tuğçe Taşkıner & Bilge Bilgen, 2021. "Optimization Models for Harvest and Production Planning in Agri-Food Supply Chain: A Systematic Review," Logistics, MDPI, vol. 5(3), pages 1-27, August.
    21. Axsäter, Sven, 2011. "Batch quantities when forecasts are improving," International Journal of Production Economics, Elsevier, vol. 133(1), pages 212-215, September.

    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:mathme:v:83:y:2016:i:1:d:10.1007_s00186-015-0517-x. 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.