• DocumentCode
    1090261
  • Title

    IPA Derivatives for Make-to-Stock Production-Inventory Systems With Lost Sales

  • Author

    Zhao, Yao ; Melamed, Benjamin

  • Author_Institution
    Rutgers Bus. Sch., Newark
  • Volume
    52
  • Issue
    8
  • fYear
    2007
  • Firstpage
    1491
  • Lastpage
    1495
  • Abstract
    This note applies the stochastic fluid model (SFM) paradigm to a class of single-stage, single-product make-to-stock (MTS) production-inventory systems with stochastic demand and random production capacity, where the finished-goods inventory is controlled by a continuous-time base-stock policy and unsatisfied demand is lost. This note derives formulas for infinitesimal perturbation analysis (IPA) derivatives of the sample-path time averages of the inventory level and lost sales with respect to the base-stock level and a parameter of the production rate process. These formulas are comprehensive in that they are exhibited for any initial inventory state, and include right and left derivatives (when they differ). The formulas are obtained via sample path analysis under very mild regularity assumptions, and are inherently nonparametric in the sense that no specific probability law need be postulated. It is further shown that all IPA derivatives under study are unbiased and fast to compute, thereby providing the theoretical basis for online adaptive control of MTS production-inventory systems.
  • Keywords
    adaptive control; perturbation theory; probability; stochastic processes; stock control; adaptive control; infinitesimal perturbation analysis; make-to-stock production-inventory system; probability; random production capacity; stochastic fluid model; Adaptive control; Continuous production; Control systems; Information management; Management information systems; Marketing and sales; Production systems; Random variables; Stochastic processes; Stochastic systems; Infinitesimal perturbation analysis (IPA); lost sales; make-to-stock (MTS); production-inventory systems; stochastic fluid models (SFMs);
  • fLanguage
    English
  • Journal_Title
    Automatic Control, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9286
  • Type

    jour

  • DOI
    10.1109/TAC.2007.902760
  • Filename
    4287155