Advances in simulation: validity and efficiency

Lee, Judy S.

Title:

Advances in simulation: validity and efficiency

dc.contributor.advisor	Andradóttir, Sigrun
dc.contributor.author	Lee, Judy S.
dc.contributor.committeeMember	Ayhan, Hayriye
dc.contributor.committeeMember	Fujimoto, Richard
dc.contributor.committeeMember	Goldsman, David M.
dc.contributor.department	Industrial and Systems Engineering
dc.date.accessioned	2015-06-08T18:15:06Z
dc.date.available	2015-06-09T05:30:07Z
dc.date.created	2014-05
dc.date.issued	2014-04-08
dc.date.submitted	May 2014
dc.date.updated	2015-06-08T18:15:06Z
dc.description.abstract	In this thesis, we present and analyze three algorithms that are designed to make computer simulation more efficient, valid, and/or applicable. The first algorithm uses simulation cloning to enhance efficiency in transient simulation. Traditional simulation cloning is a technique that shares some parts of the simulation results when simulating different scenarios. We apply this idea to transient simulation, where multiple replications are required to achieve statistical validity. Computational savings are achieved by sharing some parts of the simulation results among several replications. We improve the algorithm by inducing negative correlation to compensate for the (undesirable) positive correlation introduced by sharing some parts of the simulation. Then we identify how many replications should share the same data, and provide numerical results to analyze the performance of our approach. The second algorithm chooses a set of best systems when there are multiple candidate systems and multiple objectives. We provide three different formulations of correct selection of the Pareto optimal set, where a system is Pareto optimal if it is not inferior in all objectives compared to other competing systems. Then we present our Pareto selection algorithm and prove its validity for all three formulations. Finally, we provide numerical results aimed at understanding how well our algorithm performs in various settings. Finally, we discuss the estimation of input distributions when theoretical distributions do not provide a good fit to existing data. Our approach is to use a quasi-empirical distribution, which is a mixture of an empirical distribution and a distribution for the right tail. We describe an existing approach that involves an exponential tail distribution, and adapt the approach to incorporate a Pareto tail distribution and to use a different cutoff point between the empirical and tail distributions. Then, to measure the impact, we simulate a stable M/G/1 queue with a known inter-arrival and unknown service time distributions, and estimate the mean and tail probabilities of the waiting time in queue using the different approaches. The results suggest that if we know that the system is stable, and suspect that the tail of the service time distribution is not exponential, then a quasi-empirical distribution with a Pareto tail works well, but with a lower bound imposed on the tail index.
dc.description.degree	Ph.D.
dc.embargo.terms	2015-05-01
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/53457
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Simulation cloning
dc.subject	Output analysis
dc.subject	Efficiency
dc.subject	Antithetic variables
dc.subject	Splitting
dc.subject	Ranking and selection
dc.subject	Multiple objectives
dc.subject	Probability of correct selection
dc.subject	Simulation optimization
dc.subject	Pareto optimality
dc.subject	Power-law distributions
dc.subject	Quasi-empirical distributions
dc.title	Advances in simulation: validity and efficiency
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.corporatename	H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename	College of Engineering
relation.isOrgUnitOfPublication	29ad75f0-242d-49a7-9b3d-0ac88893323c
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level	Doctoral