Efficient multi-objective ranking and selection in the presence of uncertainty

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

OPTIMAL COMPUTING BUDGET ALLOCATION FOR SIMULATION BASED OPTIMIZATION AND COMPLEX DECISION MAKING

Ph.DDOCTOR OF PHILOSOPH

Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case

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 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 present a simple myopic budget allocation algorithm for multi-objective problems and propose several variants for different settings. In particular, this myopic method only allocates one simulation sample to one alternative in each iteration. This paper shows how the algorithm works in bi-objective problems under different settings. Empirical tests show that our algorithm can significantly reduce the necessary simulation budget

SIMULATION OPTIMIZATION USING OPTIMAL COMPUTING BUDGET ALLOCATION

Ph.DDOCTOR OF PHILOSOPH

A development of logistics management models for the Space Transportation System

A new analytic queueing approach was described which relates stockage levels, repair level decisions, and the project network schedule of prelaunch operations directly to the probability distribution of the space transportation system launch delay. Finite source population and limited repair capability were additional factors included in this logistics management model developed specifically for STS maintenance requirements. Data presently available to support logistics decisions were based on a comparability study of heavy aircraft components. A two-phase program is recommended by which NASA would implement an integrated data collection system, assemble logistics data from previous STS flights, revise extant logistics planning and resource requirement parameters using Bayes-Lin techniques, and adjust for uncertainty surrounding logistics systems performance parameters. The implementation of these recommendations can be expected to deliver more cost-effective logistics support

A general framework on the computing budget allocation problem

Master'sMASTER OF ENGINEERIN