A robust optimization solution to bottleneck generalized assignment problem under uncertainty

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

9 Scopus Citations
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Author(s)

  • Yelin Fu
  • Jianshan Sun
  • K. K. Lai
  • John W. K. Leung

Detail(s)

Original languageEnglish
Pages (from-to)123-133
Journal / PublicationAnnals of Operations Research
Volume233
Issue number1
Online published29 May 2014
Publication statusPublished - Oct 2015

Abstract

We consider two versions of bottleneck (or min–max) generalized assignment problem (BGAP) under capacity uncertainty: Task–BGAP and Agent–BGAP. A robust optimization approach is employed to study this issue. The decision maker’s degree of risk aversion and the penalty weighting parameter are incorporated into the objective function. A state-of-the-art linearization method is introduced to deal with the mathematical model and find the solution scheme. Two penalties of weighting parameters that realize the trade-off between solution robustness and model robustness are obtained. Illustrative examples are presented with managerial implications highlighted for decision-making considerations.

Research Area(s)

  • Assignment, Bottleneck, Robust optimization, Stochastic programming

Citation Format(s)

A robust optimization solution to bottleneck generalized assignment problem under uncertainty. / Fu, Yelin; Sun, Jianshan; Lai, K. K.; Leung, John W. K.

In: Annals of Operations Research, Vol. 233, No. 1, 10.2015, p. 123-133.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review