Comparative Efficiency in Data Envelopment Analysis Based on Ratio Analysis
Published on 3. October 2024
Book
Paperback/Softback
XIV, 180 pages
978-3-031-43183-8 (ISBN)
Description
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.
More details
Series
Edition
2023 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
29 farbige Abbildungen, 3 s/w Abbildungen
XIV, 180 p. 32 illus., 29 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 11 mm
Weight
306 gr
ISBN-13
978-3-031-43183-8 (9783031431838)
DOI
10.1007/978-3-031-43181-4
Schweitzer Classification
Other editions
Additional editions
Farhad Hosseinzadeh Lotfi | Tofigh Allahviranloo | Witold Pedrycz
Comparative Efficiency in Data Envelopment Analysis Based on Ratio Analysis
Book
10/2023
Springer
€160.49
Shipment within 15-20 days
Content
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.