Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency

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  • Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency Book Detail

  • Author : Ajibesin, Adeyemi Abel
  • Release Date : 2024-01-16
  • Publisher : IGI Global
  • Genre : Computers
  • Pages : 413
  • ISBN 13 :
  • File Size : 3,3 MB

Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency by Ajibesin, Adeyemi Abel PDF Summary

Book Description: In today's highly competitive and rapidly evolving global landscape, the quest for efficiency has become a crucial factor in determining the success of organizations across various industries. Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency is a comprehensive guide that delves into the powerful mathematical tool of DEA, is designed to assess the relative efficiency of decision-making units (DMUs), and provides valuable insights for performance improvement. This book presents a systematic overview of DEA models and techniques, from fundamental concepts to advanced methods, showcasing their practical applications through real-world examples and case studies. Catering to a broad audience, this book is designed for students, researchers, consultants, decision-makers, and enthusiasts in the field of efficiency analysis and performance measurement. Consultants and practitioners will gain practical insights for applying DEA in various contexts, and decision-makers will be equipped to make informed decisions for maximizing efficiency. Additionally, individuals with a general interest in data analysis and performance measurement will find this book accessible and informative. This book covers a wide range of topics, including mathematical foundations of DEA, DEA models and variations, DEA efficiency and productivity measures, DEA applications in various industries such as healthcare, finance, supply chain management, environmental management, education management, and public sector management.

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This handbook serves as a complement to the Handbook on Data Envelopment Analysis (eds, W.W. Cooper, L.M. Seiford and J, Zhu, 2011, Springer) in an effort to ex