Robust Optimization Approach In Data Envelopment Analysis Models: Extension To The Cases With Uncertain Production Trade-offs, Integer Data And Negative Data.

Data envelopment analysis (DEA) is a popular performance measurement technique and since it was first introduced, DEA models have been extensively applied in real-world managerial problems. One of the challenges in applying DEA models in real-world problems is uncertainty and inaccuracy in data whic...

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Main Author: Rokhsaneh, Yousef Zehi
Format: Thesis
Language:English
Published: 2023
Subjects:
Online Access:http://eprints.usm.my/60885/
http://eprints.usm.my/60885/1/YOUSEF%20ZEHI%20ROKHSANEH%20-%20TESIS24.pdf
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author Rokhsaneh, Yousef Zehi
author_facet Rokhsaneh, Yousef Zehi
author_sort Rokhsaneh, Yousef Zehi
building USM Institutional Repository
collection Online Access
description Data envelopment analysis (DEA) is a popular performance measurement technique and since it was first introduced, DEA models have been extensively applied in real-world managerial problems. One of the challenges in applying DEA models in real-world problems is uncertainty and inaccuracy in data which can be due to error in measurement, calculation, prediction etc. As uncertainty is an inevitable factor in many optimization problems, therefore the uncertainty in data should be taken into consideration to ensure reliable optimal solutions and benchmarking. Robust optimization is one of the most recent approaches for handling uncertainty in DEA models which immunize the uncertain parameters over a pre-specified uncertainty set to determine an optimal solution which is guaranteed to be the best for all or most of the possible realizations of the uncertain parameters. Applying robust optimization approach in DEA models resulted to Robust DEA field which is a relatively young yet growing field in DEA, introduced in 2008. The goal of this thesis is to fulfil some of the theoretical and practical gaps in robust DEA field. The previous works on robust DEA models only considered inputs and outputs data to be uncertain, thus one of the objectives of this thesis is to assess the effect of uncertainty in the other involved parameters in the optimization such as weights assigned to inputs and outputs and production trade-offs. Moreover, a comparative analysis between the proposed robust DEA model and other approaches of handling uncertainty in data such as interval DEA will be provided.
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institution Universiti Sains Malaysia
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language English
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spelling usm-608852024-07-31T04:27:53Z http://eprints.usm.my/60885/ Robust Optimization Approach In Data Envelopment Analysis Models: Extension To The Cases With Uncertain Production Trade-offs, Integer Data And Negative Data. Rokhsaneh, Yousef Zehi QA1 Mathematics (General) Data envelopment analysis (DEA) is a popular performance measurement technique and since it was first introduced, DEA models have been extensively applied in real-world managerial problems. One of the challenges in applying DEA models in real-world problems is uncertainty and inaccuracy in data which can be due to error in measurement, calculation, prediction etc. As uncertainty is an inevitable factor in many optimization problems, therefore the uncertainty in data should be taken into consideration to ensure reliable optimal solutions and benchmarking. Robust optimization is one of the most recent approaches for handling uncertainty in DEA models which immunize the uncertain parameters over a pre-specified uncertainty set to determine an optimal solution which is guaranteed to be the best for all or most of the possible realizations of the uncertain parameters. Applying robust optimization approach in DEA models resulted to Robust DEA field which is a relatively young yet growing field in DEA, introduced in 2008. The goal of this thesis is to fulfil some of the theoretical and practical gaps in robust DEA field. The previous works on robust DEA models only considered inputs and outputs data to be uncertain, thus one of the objectives of this thesis is to assess the effect of uncertainty in the other involved parameters in the optimization such as weights assigned to inputs and outputs and production trade-offs. Moreover, a comparative analysis between the proposed robust DEA model and other approaches of handling uncertainty in data such as interval DEA will be provided. 2023-08 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/60885/1/YOUSEF%20ZEHI%20ROKHSANEH%20-%20TESIS24.pdf Rokhsaneh, Yousef Zehi (2023) Robust Optimization Approach In Data Envelopment Analysis Models: Extension To The Cases With Uncertain Production Trade-offs, Integer Data And Negative Data. PhD thesis, Universiti Sains Malaysia.
spellingShingle QA1 Mathematics (General)
Rokhsaneh, Yousef Zehi
Robust Optimization Approach In Data Envelopment Analysis Models: Extension To The Cases With Uncertain Production Trade-offs, Integer Data And Negative Data.
title Robust Optimization Approach In Data Envelopment Analysis Models: Extension To The Cases With Uncertain Production Trade-offs, Integer Data And Negative Data.
title_full Robust Optimization Approach In Data Envelopment Analysis Models: Extension To The Cases With Uncertain Production Trade-offs, Integer Data And Negative Data.
title_fullStr Robust Optimization Approach In Data Envelopment Analysis Models: Extension To The Cases With Uncertain Production Trade-offs, Integer Data And Negative Data.
title_full_unstemmed Robust Optimization Approach In Data Envelopment Analysis Models: Extension To The Cases With Uncertain Production Trade-offs, Integer Data And Negative Data.
title_short Robust Optimization Approach In Data Envelopment Analysis Models: Extension To The Cases With Uncertain Production Trade-offs, Integer Data And Negative Data.
title_sort robust optimization approach in data envelopment analysis models: extension to the cases with uncertain production trade-offs, integer data and negative data.
topic QA1 Mathematics (General)
url http://eprints.usm.my/60885/
http://eprints.usm.my/60885/1/YOUSEF%20ZEHI%20ROKHSANEH%20-%20TESIS24.pdf