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A robust optimization solution to bottleneck generalized assignment problem under uncertainty
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DOI：10.1007/s10479-014-1631-5.png)
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Abstract
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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.
Keywords:
Robust optimization
Bottleneck
Assignment
Stochastic programming
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