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Probabilistic Service Level Guarantees in Make-to-Stock Manufacturing Systems

Author

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  • Dimitris Bertsimas

    (Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Ioannis Ch. Paschalidis

    (Department of Manufacturing Engineering, Boston University, Boston, Massachusetts 02215)

Abstract

We consider a model of a multiclass make-to-stock manufacturing system. External demand for each product class is met from the available finished goods inventory; unsatisfied demand is backlogged. The objective is to devise a production policy that minimizes inventory costs subject to guaranteeing stockout probabilities to stay bounded above by given constants (epsilon) j , for each product class j ( service level guarantees ). Such a policy determines whether the facility should be producing ( idling decisions ), and if it should, which product class ( sequencing decisions ). Approximating the original system, we analyze a corresponding fluid model to make sequencing decisions and employ large deviations techniques to make idling ones. We consider both linear and quadratic inventory cost structures to obtain a priority-based and a generalized longest queue first-based production policy, respectively. An important feature of our model is that it accommodates autocorrelated demand and service processes, both critical features of modern failure-prone manufacturing systems.

Suggested Citation

  • Dimitris Bertsimas & Ioannis Ch. Paschalidis, 2001. "Probabilistic Service Level Guarantees in Make-to-Stock Manufacturing Systems," Operations Research, INFORMS, vol. 49(1), pages 119-133, February.
  • Handle: RePEc:inm:oropre:v:49:y:2001:i:1:p:119-133
    DOI: 10.1287/opre.49.1.119.11183
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    References listed on IDEAS

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    Cited by:

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    2. Moshe Dror & Kenneth R. Smith & Candace Arai Yano, 2009. "Deux Chemicals Inc. Goes Just-in-Time," Interfaces, INFORMS, vol. 39(6), pages 503-515, December.
    3. Seong-Cheol Kang & Theodora Brisimi & Ioannis Paschalidis, 2015. "Distribution-dependent robust linear optimization with applications to inventory control," Annals of Operations Research, Springer, vol. 231(1), pages 229-263, August.
    4. Elodie Adida & Georgia Perakis, 2010. "Dynamic pricing and inventory control: robust vs. stochastic uncertainty models—a computational study," Annals of Operations Research, Springer, vol. 181(1), pages 125-157, December.
    5. 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.
    6. Ioannis Ch. Paschalidis & Yong Liu, 2003. "Large Deviations-Based Asymptotics for Inventory Control in Supply Chains," Operations Research, INFORMS, vol. 51(3), pages 437-460, June.
    7. Bora Kat & Zeynep Avṣar, 2011. "Using aggregate fill rate for dynamic scheduling of multi-class systems," Annals of Operations Research, Springer, vol. 182(1), pages 87-117, January.
    8. Khayyati, Siamak & Tan, Barış, 2020. "Data-driven control of a production system by using marking-dependent threshold policy," International Journal of Production Economics, Elsevier, vol. 226(C).

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