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      Optimization of schedule robustness and stability under random machine breakdowns and processing time variability

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      Author
      Goren, S.
      Sabuncuoglu, I.
      Date
      2010
      Source Title
      IIE Transactions (Institute of Industrial Engineers)
      Print ISSN
      0740-817X
      Electronic ISSN
      1545-8830
      Publisher
      Taylor & Francis
      Volume
      42
      Issue
      3
      Pages
      203 - 220
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      In practice, scheduling systems are subject to considerable uncertainty in highly dynamic operating environments. The ability to cope with uncertainty in the scheduling process is becoming an increasingly important issue. This paper takes a proactive scheduling approach to study scheduling problems with two sources of uncertainty: processing time variability and machine breakdowns. Two robustness (expected total flow time and expected total tardiness) and three stability (the sum of the squared and absolute differences of the job completion times and the sum of the variances of the realized completion times) measures are defined. Special cases for which the measures can be easily optimized are identified. A dominance rule and two lower bounds for one of the robustness measures are developed and subseqently used in a branch-and-bound algorithm to solve the problem exactly. A beam search heuristic is also proposed to solve large problems for all five measures. The computational results show that the beam search heuristic is capable of generating robust schedules with little average deviation from the optimal objective function value (obtained via the branch-and-bound algorithm) and it performs significantly better than a number of heuristics available in the literature for all five measures.
      Keywords
      Proactive scheduling
      Robustness
      Stability
      Permalink
      http://hdl.handle.net/11693/22416
      Published Version (Please cite this version)
      http://dx.doi.org/10.1080/07408170903171035
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      • Department of Industrial Engineering 684
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