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Stochastic Scheduling with Incomplete Information

  • Xiaoqiang Cai
  • Xianyi Wu
  • Xian Zhou
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 207)

Abstract

This chapter treats a class of scheduling models subject to machine breakdowns with incomplete information. Under this class of models, the repeated processing times between breakdowns are dependent via a latent random variable. This leads to partially available information on the processing times during the process, and the information is gradually accumulated from previous processing experience and adaptively incorporated into the decision making for processing remaining jobs. Section 8.1 formulates the model and discusses the probabilistic characteristics of the repetition frequency and occupying times, and the impact of incomplete information. The optimal restricted dynamic policies for this model are derived in Section 8.2 based on posterior Gittins indices. Finally, Section 8.3 discusses an interesting case in which the posterior Gittins indices can be represented by the one-step rewards rates of the jobs.

Keywords

Posterior Distribution Optimal Policy Probabilistic Characteristic Reward Rate Decision Epoch 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Xiaoqiang Cai
    • 1
  • Xianyi Wu
    • 2
  • Xian Zhou
    • 3
  1. 1.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongShatin, N.T.Hong Kong SAR
  2. 2.Department of Statistics and Actuarial ScienceEast China Normal UniversityShanghaiPeople’s Republic of China
  3. 3.Department of Applied Finance and Actuarial StudiesMacquarie UniversityNorth RydeAustralia

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