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Award ID contains: 2208303

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  1. ; ; (, Naval Research Logistics (NRL))
    ABSTRACT We study the optimal sequencing of a batch of tasks on a machine subject to random disruptions driven by a non‐homogeneous Poisson process (NHPP), such that every disruption requires the interrupted task to be re‐processed from scratch, and partially completed work on a disrupted task is wasted. The NHPP models random disruptions whose frequency varies systematically with time. In general, the time taken to process a given batch of tasks depends on the order in which the tasks are processed. We find conditions under which the simplest possible sequencing rules—shortest processing time first (SPT) and longest processing time first (LPT)—suffice to minimize the completion time of a batch of tasks. 
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    Free, publicly-accessible full text available June 1, 2026
  2. ; ; ; ; (, IISE Transactions)
    Free, publicly-accessible full text available July 3, 2026
  3. ; ; (, Journal of machine learning research)
    Free, publicly-accessible full text available April 1, 2026
  4. ; ; (, Journal of machine learning research)
    Accepted Manuscript Full Text Available
  5. ; ; ; (, Advances in Neural Information Processing Systems)
  6. ; ; ; (, International Conference on Machine Learning)
  7. ; ; ; (, Transactions on machine learning research)
    Accepted Manuscript Full Text Available
  8. ; (, Operations Research Letters)
    Accepted Manuscript Full Text Available
  9. ; ; ; ; (, International Conference on Algorithmic Learning Theory)
    Accepted Manuscript Full Text Available
  10. ; (, The review of economics and statistics)
    Full Text Available