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Production control with backlog-dependent demand

Author

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  • Stanley Gershwin
  • Bariş Tan
  • Michael Veatch

Abstract

A manufacturing firm that builds a product to stock to meet a random demand is studied. Production time is deterministic, so that if there is a backlog, customers are quoted a lead time that is proportional to the backlog. In order to represent the customers' response to waiting, a defection function—the fraction of customers who choose not to order as a function of the quoted lead time—is introduced. Unlike models with backorder costs, the defection function is related to customer behavior. Using a continuous flow control model with linear holding cost and Markov modulated demand, it is shown that the optimal production policy has a hedging point form. The performance of the system under this policy is evaluated, allowing the optimal hedging point to be found.[Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resource: Appendix]

Suggested Citation

  • Stanley Gershwin & Bariş Tan & Michael Veatch, 2009. "Production control with backlog-dependent demand," IISE Transactions, Taylor & Francis Journals, vol. 41(6), pages 511-523.
    • Handle: RePEc:taf:uiiexx:v:41:y:2009:i:6:p:511-523
    DOI: 10.1080/07408170801975040
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    Citations

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    Cited by:

    1. K. Skouri, 2018. "An EOQ model with backlog-dependent demand," Operational Research, Springer, vol. 18(2), pages 561-574, July.
    2. Eugene Khmelnitsky & Gonen Singer, 2015. "An optimal inventory management problem with reputation-dependent demand," Annals of Operations Research, Springer, vol. 231(1), pages 305-316, August.
    3. Barış Tan, 2019. "Production Control with Price, Cost, and Demand Uncertainty," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(4), pages 1057-1085, December.
    4. Manafzadeh Dizbin, Nima & Tan, Barış, 2020. "Optimal control of production-inventory systems with correlated demand inter-arrival and processing times," International Journal of Production Economics, Elsevier, vol. 228(C).
    5. Shine-Der Lee & Chin-Ming Yang & Shu-Chuan Lan, 2016. "Economic lot sizing in a production system with random demand," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(5), pages 1142-1154, April.
    6. Zhang, Dongyang & Zhuge, Liqun & Freeman, Richard B., 2020. "Firm dynamics of hi-tech start-ups: Does innovation matter?," China Economic Review, Elsevier, vol. 59(C).
    7. Omar Besbes & Costis Maglaras, 2009. "Revenue Optimization for a Make-to-Order Queue in an Uncertain Market Environment," Operations Research, INFORMS, vol. 57(6), pages 1438-1450, December.
    8. Amir Ahmadi-Javid & Roland Malhamé, 2015. "Optimal Control of a Multistate Failure-Prone Manufacturing System under a Conditional Value-at-Risk Cost Criterion," Journal of Optimization Theory and Applications, Springer, vol. 167(2), pages 716-732, November.

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