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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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | 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/ |
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| Summary: | 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. |
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