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Inverse scheduling with controllable job parameters

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  • Christos Koulamas

Abstract

Scheduling problems deal with how to sequence a list of jobs with the objective of minimising some measurement of the job completion times. Inverse scheduling problems assume that a job sequence is given and the objective is to determine the minimal perturbation to the job parameters (e.g., processing times) so that the given sequence becomes optimal with respect to a pre-selected objective function. The objective of this paper is to study inverse scheduling problems. It will be shown that these problems can be formulated as linear programming (LP) problems even when, in some cases, the corresponding forward scheduling problems are not solvable in polynomial time. Several applications are discussed, including the generation of benchmark optimal solutions for NP-hard forward scheduling problems.

Suggested Citation

  • Christos Koulamas, 2005. "Inverse scheduling with controllable job parameters," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 1(1), pages 35-43.
    • Handle: RePEc:ids:ijsoma:v:1:y:2005:i:1:p:35-43
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    Citations

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    Cited by:

    1. Rico Walter & Martin Wirth & Alexander Lawrinenko, 2017. "Improved approaches to the exact solution of the machine covering problem," Journal of Scheduling, Springer, vol. 20(2), pages 147-164, April.
    2. Jianhui Mou & Xinyu Li & Liang Gao & Wenchao Yi, 2018. "An effective L-MONG algorithm for solving multi-objective flow-shop inverse scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 789-807, April.
    3. Rico Walter & Alexander Lawrinenko, 2020. "A characterization of optimal multiprocessor schedules and new dominance rules," Journal of Combinatorial Optimization, Springer, vol. 40(4), pages 876-900, November.

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