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Comparative Efficiency in Data Envelopment Analysis Based on Ratio Analysis 2023 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 180 pages, height x width: 235x155 mm, 29 Illustrations, color; 3 Illustrations, black and white; XIV, 180 p. 32 illus., 29 illus. in color., 1 Paperback / softback
  • Sērija : Studies in Big Data 138
  • Izdošanas datums: 03-Oct-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031431839
  • ISBN-13: 9783031431838
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  • Mīkstie vāki
  • Cena: 136,16 €*
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Comparative Efficiency in Data Envelopment Analysis Based on Ratio Analysis 2023 ed.Palielināt
  • Formāts: Paperback / softback, 180 pages, height x width: 235x155 mm, 29 Illustrations, color; 3 Illustrations, black and white; XIV, 180 p. 32 illus., 29 illus. in color., 1 Paperback / softback
  • Sērija : Studies in Big Data 138
  • Izdošanas datums: 03-Oct-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031431839
  • ISBN-13: 9783031431838
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031431838.html
Keywords:
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.
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.