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A Novel Parallel Adaptive Survivor Selection Framework for Large-Scale Simulation Optimization

Description:

For stochastic simulation optimization in a modern computing era, we introduce a new parallel framework for solving very large-scale problems using a ranking & selection (R&S) approach that simulates all systems or feasible solutions to provide a global statistical guarantee. We propose a parallel adaptive survivor selection (PASS) framework that...

Keyword:

master-worker, simulation, parallel, stochastic, large-scale, and optimization

Subject:

Applied mathematics, Statistics, and Operations research

Creator:

Pei, Linda

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

06/22/2022

Date Created:

2022-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_903888 and http://dissertations.umi.com/northwestern:16108
Optimization under Variable Uncertainty

Description:

In this dissertation, we study models and methods to address uncertainties that can vary in optimization problems. Robust optimization is a popular approach for optimization under uncertainty, especially if limited information is available about the distribution of the uncertainty. It models the uncertainty through sets and finds a robust optimal...

Keyword:

variable uncertainty, optimization, decision dependent uncertainty, and robust optimization

Subject:

Industrial engineering and Operations research

Creator:

Sharma, Kartikey

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

04/29/2020

Date Created:

2020-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_718511 and http://dissertations.umi.com/northwestern:14998

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A Novel Parallel Adaptive Survivor Selection Framework for Large-Scale Simulation Optimization

Optimization under Variable Uncertainty

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