Improvement Of Discrimination Power And Weight Dispersion In Multi-Criteria Data Envelopment Analysis
Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the...
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my.usm.eprints.29018 http://eprints.usm.my/29018/ Improvement Of Discrimination Power And Weight Dispersion In Multi-Criteria Data Envelopment Analysis Ghasemi, Mohammadreza QA1 Mathematics (General) Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. Kekurangan keupayaan mendiskriminasi dan kelemahan pengagihan pemberat kekal sebagai isu utama dalam Analisis Penyampulan Data (DEA). Semenjak model DEA berbilang kriteria (MCDEA) pertama yang dibentuk pada akhir tahun 1990an, hanya pendekatan pengaturcaraangol; yakni, GPDEA-CCR dan GPDEA-BCC telah diperkenalkan bagi menyelesaikan masalah berkenaan dalam konteks berbilang kriteria. 2014 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/29018/1/IMPROVEMENT_OF_DISCRIMINATION_POWER_AND_WEIGHT_DISPERSION_IN_MULTI-CRITERIA_DATA_ENVELOPMENT_ANALSIS.pdf Ghasemi, Mohammadreza (2014) Improvement Of Discrimination Power And Weight Dispersion In Multi-Criteria Data Envelopment Analysis. PhD thesis, Universiti Sains Malaysia. |
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QA1 Mathematics (General) |
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QA1 Mathematics (General) Ghasemi, Mohammadreza Improvement Of Discrimination Power And Weight Dispersion In Multi-Criteria Data Envelopment Analysis |
| description |
Lack of discrimination power and poor weight dispersion remain major issues in
Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA
(MCDEA) model developed in the late 1990s, only goal programming approaches;
that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said
problems in a multi-objective framework.
Kekurangan keupayaan mendiskriminasi dan kelemahan pengagihan pemberat kekal
sebagai isu utama dalam Analisis Penyampulan Data (DEA). Semenjak model DEA
berbilang kriteria (MCDEA) pertama yang dibentuk pada akhir tahun 1990an, hanya
pendekatan pengaturcaraangol; yakni, GPDEA-CCR dan GPDEA-BCC telah
diperkenalkan bagi menyelesaikan masalah berkenaan dalam konteks berbilang
kriteria. |
| format |
Thesis |
| author |
Ghasemi, Mohammadreza |
| author_facet |
Ghasemi, Mohammadreza |
| author_sort |
Ghasemi, Mohammadreza |
| title |
Improvement Of Discrimination Power
And Weight Dispersion In Multi-Criteria
Data Envelopment Analysis
|
| title_short |
Improvement Of Discrimination Power
And Weight Dispersion In Multi-Criteria
Data Envelopment Analysis
|
| title_full |
Improvement Of Discrimination Power
And Weight Dispersion In Multi-Criteria
Data Envelopment Analysis
|
| title_fullStr |
Improvement Of Discrimination Power
And Weight Dispersion In Multi-Criteria
Data Envelopment Analysis
|
| title_full_unstemmed |
Improvement Of Discrimination Power
And Weight Dispersion In Multi-Criteria
Data Envelopment Analysis
|
| title_sort |
improvement of discrimination power
and weight dispersion in multi-criteria
data envelopment analysis |
| publishDate |
2014 |
| url |
http://eprints.usm.my/29018/1/IMPROVEMENT_OF_DISCRIMINATION_POWER_AND_WEIGHT_DISPERSION_IN_MULTI-CRITERIA_DATA_ENVELOPMENT_ANALSIS.pdf http://eprints.usm.my/29018/ |
| _version_ |
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