Comparative Efficiency in Data Envelopment Analysis Based on Ratio Analysis

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The combination of DEA and ratio analysis is introduced as a suitable field for evaluating the performance of DMUs. In this regard, DEA-R is also proposed as a hybrid technique for calculating efficiency, ranking DMUs, and finding efficient faces. Therefore, the relationship between DEA and DEA-R provides a suitable field for researchers in the field of evaluating the performance of DMUs. The audience of this book is not limited to researchers in mathematics fields, but experts and students in industrial engineering and management fields also benefit from the topics of this book.

Presents an introduction of comparative efficiency models in ratio analysis and DEA-R Demonstrates the relationship between ratio analysis models and DEA-R with classical data envelopment analysis models Focuses on comparative efficiency models in ratio analysis and DEA-R

Inhalt
Relationship between DEA models without explicit inputs and DEA-R models.- Finding efficient surfaces in DEA-R models.- Cost and revenue efficiency in DEA-R models.- A Novel Slack-Based Model for Efficiency and Super-efficiency in DEA-R.- A Multi-Criteria Ratio-Based Approach for Two-Stage Data Envelopment Analysis.- A novel network DEA-R model for evaluating hospital services supply chain performance.- A novel inverse DEA-R model for inputs/output estimation.- Evaluation of Two-Stage Networks based on Average Efficiency Using DEA and DEA-R with Fuzzy Data.- Stochastic network DEA-R models for two-stage systems.

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783031431838
    • Genre Technology Encyclopedias
    • Lesemotiv Verstehen
    • Anzahl Seiten 196
    • Herausgeber Springer
    • Größe H235mm x B155mm x T11mm
    • Jahr 2024
    • EAN 9783031431838
    • Format Kartonierter Einband
    • ISBN 3031431839
    • Veröffentlichung 03.10.2024
    • Titel Comparative Efficiency in Data Envelopment Analysis Based on Ratio Analysis
    • Autor Farhad Hosseinzadeh Lotfi , Tofigh Allahviranloo , Witold Pedrycz , Mohammad Reza Mozaffari , Javad Gerami
    • Untertitel Studies in Big Data 138
    • Gewicht 306g
    • Sprache Englisch

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