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Title: Efficient multi-objective ranking and selection in the presence of uncertainty
Author: Zhang, Wen
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2018
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We consider the problem of ranking and selection with multiple-objectives in the presence of uncertainty. Simulation optimisation offers great opportunities in the design and optimisation of complex systems. In the presence of multiple objectives there is usually no single solution that performs best on all the objectives. Instead, there are several Pareto-optimal (efficient) solutions with different trade-offs which cannot be improved in any objective without sacrificing performance in another objective. For the case where alternatives are evaluated on multiple stochastic criteria, and the performance of an alternative can only be estimated via simulation, we consider the problem of efficiently identifying the Pareto optimal designs out of a (small) given set of alternatives. We develop a simple myopic budget allocation algorithm and propose several variants for different settings. In particular, this myopic method only allocates one simulation sample to one alternative in each iteration. Empirical tests show that the proposed algorithm can significantly reduce the necessary simulation budget and perform better than some existing well known algorithms in certain settings.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HB Economic Theory ; QA76 Electronic computers. Computer science. Computer software