Value of modeling uncertainty in multi-objective programming

Download

index.pdf

Date

2019

Author

Yiğit, Ece

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

210
views

95
downloads

In this thesis, the value of modeling uncertainty in multi-objective problems is inves-tigated. First, a mathematical model for a general two-stage multi-objective stochas-tic problems is introduced. Then, a new approach is presented for calculating thevalue of the stochastic solution and the expected value of perfect information for suchproblems. Computational experiments are provided to test the validity and the perfor-mance of the proposed methodology considering a bi-objective problems that involve uncertainty.

Subject Keywords

Stochastic programming., Keywords: Stochastic programming, multi-objective programming, value of stochas-tic solution, expected value of perfect information.

URI

http://etd.lib.metu.edu.tr/upload/12624621/index.pdf
https://hdl.handle.net/11511/44552

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Expected Value of Partial Perfect Information in Hybrid Models Using Dynamic Discretization Yet, Barbaros; Fenton, Norman; Neil, Martin (2018-01-01) In decision theory models, expected value of partial perfect information (EVPPI) is an important analysis technique that is used to identify the value of acquiring further information on individual variables. EVPPI can be used to prioritize the parts of a model that should be improved or identify the parts where acquiring additional data or expert knowledge is most beneficial. Calculating EVPPI of continuous variables is challenging, and several sampling and approximation techniques have been proposed. This...
A stochastic programming approach to multicriteria portfolio optimization Sakar, Ceren Tuncer; Köksalan, Mustafa Murat (2013-10-01) We study a stochastic programming approach to multicriteria multi-period portfolio optimization problem. We use a Single Index Model to estimate the returns of stocks from a market-representative index and a random walk model to generate scenarios on the possible values of the index return. We consider expected return, Conditional Value at Risk and liquidity as our criteria. With stocks from Istanbul Stock Exchange, we make computational studies for the two and three-criteria cases. We demonstrate the trade...
Estimation and hypothesis testing in multivariate linear regression models under non normality İslam, Muhammed Qamarul (Informa UK Limited, 2017-01-01) This paper discusses the problem of statistical inference in multivariate linear regression models when the errors involved are non normally distributed. We consider multivariate t-distribution, a fat-tailed distribution, for the errors as alternative to normal distribution. Such non normality is commonly observed in working with many data sets, e.g., financial data that are usually having excess kurtosis. This distribution has a number of applications in many other areas of research as well. We use modifie...
Comparative analysis of variability modelling approaches in component models Suloglu, Selma; Kaya, Muhammed Çağrı; Karamanlıoğlu, Alper; Entekhabi, Sina; Nikoo, Mahdi Saeedi; Tekinerdogan, Bedir; Doğru, Ali Hikmet (2018-12-01) The results of a systematic literature review conducted for variability modelling in software component models are analysed and presented here. A well-planned protocol guided the screening of 3230 papers that resulted in the identification of 55 papers. Reviewing these papers, 23 of them were considered as primary studies related to our research questions. A comparison framework is introduced to further understand, assess, and compare those selected papers. Observations about the important aspects of compon...
Inference in multivariate linear regression models with elliptically distributed errors İslam, Muhammed Qamarul; Yazici, Mehmet (2014-08-01) In this study we investigate the problem of estimation and testing of hypotheses in multivariate linear regression models when the errors involved are assumed to be non-normally distributed. We consider the class of heavy-tailed distributions for this purpose. Although our method is applicable for any distribution in this class, we take the multivariate t-distribution for illustration. This distribution has applications in many fields of applied research such as Economics, Business, and Finance. For estimat...

Citation Formats

E. Yiğit, “Value of modeling uncertainty in multi-objective programming,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Industrial Engineering., Middle East Technical University, 2019.