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An exact algorithm to minimize mean squared deviation of job completion times about a common due date

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  • Srirangacharyulu, B.
  • Srinivasan, G.

Abstract

We consider a deterministic n-job, single machine scheduling problem with the objective of minimizing the Mean Squared Deviation (MSD) of job completion times about a common due date (d). The MSD measure is non-regular and its value can decrease when one or more completion times increases. MSD problem is connected with the Completion Time Variance (CTV) problem and has been proved to be NP-hard. This problem finds application in situations where uniformity of service is important. We present an exact algorithm of pseudo-polynomial complexity, using ideas from branch and bound and dynamic programming. We propose a dominance rule and also develop a lower bound on MSD. The dominance rule and lower bound are effectively combined and used in the development of the proposed algorithm. The search space is explored using the breadth first branching strategy. The asymptotic space complexity of the algorithm is O(nd). Irrespective of the version of the problem – tightly constrained, constrained or unconstrained – the proposed algorithm provides optimal solutions for problem instances up to 1000 jobs size under different due date settings.

Suggested Citation

  • Srirangacharyulu, B. & Srinivasan, G., 2013. "An exact algorithm to minimize mean squared deviation of job completion times about a common due date," European Journal of Operational Research, Elsevier, vol. 231(3), pages 547-556.
    • Handle: RePEc:eee:ejores:v:231:y:2013:i:3:p:547-556
    DOI: 10.1016/j.ejor.2013.06.017
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    References listed on IDEAS

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    1. Prabuddha De & Jay B. Ghosh & Charles E. Wells, 1989. "Note---A Note on the Minimization of Mean Squared Deviation of Completion Times About a Common Due Date," Management Science, INFORMS, vol. 35(9), pages 1143-1147, September.
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    Cited by:

    1. Pereira, Jordi & Vásquez, Óscar C., 2017. "The single machine weighted mean squared deviation problem," European Journal of Operational Research, Elsevier, vol. 261(2), pages 515-529.

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