Handbook of Operations Analytics Using Data Envelopment Analysis

  • Shiuh-Nan Hwang
  • Hsuan-Shih Lee
  • Joe Zhu

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 239)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. José L. Ruiz, Inmaculada Sirvent
    Pages 1-29
  3. Peng Zhou, Kim Leng Poh, Beng Wah Ang
    Pages 31-49
  4. C. Serrano Cinca, C. Mar Molinero, Y. Fuertes Callén
    Pages 51-87
  5. Necmi Kemal Avkiran, Yushu (Elizabeth) Zhu
    Pages 113-143
  6. Yueh-Cheng Wu, Qian Long Kweh, Wen-Min Lu, Shiu-Wan Hung, Chia-Fa Chang
    Pages 167-186
  7. Sebastián Lozano, Miguel Ángel Hinojosa, Amparo María Mármol, Diego Vicente Borrero
    Pages 215-239
  8. Hirofumi Fukuyama, William L. Weber
    Pages 241-266
  9. Dong-Joon Lim, Shabnam R. Jahromi, Timothy R. Anderson, Anca-Alexandra Tudorie
    Pages 331-349
  10. Julie Harrison, Paul Rouse
    Pages 385-412
  11. Toshiyuki Sueyoshi, Yan Yuan
    Pages 445-481
  12. Back Matter
    Pages 499-506

About this book


This handbook focuses on Data Envelopment Analysis (DEA) applications in operations analytics which are fundamental tools and techniques for improving operation functions and attaining long-term competitiveness. In fact, the handbook demonstrates that DEA can be viewed as Data Envelopment Analytics.

Chapters include a review of cross-efficiency evaluation; a case study on measuring the environmental performance of OECS countries; how to select a set of performance metrics in DEA with an application to American banks; a relational network model to take the operations of individual periods into account in measuring efficiencies; how the efficient frontier methods DEA and stochastic frontier analysis (SFA) can be used synergistically; and how to integrate DEA and multidimensional scaling.

In other chapters, authors construct a dynamic three-stage network DEA model; a bootstrapping based methodology to evaluate returns to scale and convexity assumptions in DEA; hybridizing DEA and cooperative games; using DEA to represent the production technology and directional distance functions to measure band performance; an input-specific Luenberger energy and environmental productivity indicator; and the issue of reference set by differentiating between the uniquely found reference set and the unary and maximal types of the reference set.

Finally, additional chapters evaluate and compare the technological advancement observed in different hybrid electric vehicles (HEV) market segments over the past 15 years; radial measurement of efficiency for the production process possessing multi-components under different production technologies; issues around the use of accounting information in DEA; how to use DEA environmental assessment to establish corporate sustainability; a summary of research efforts on DEA environmental assessment applied to energy in the last 30 years; and an overview of DEA and how it can be utilized alone and with other techniques to investigate corporate environmental sustainability questions.


Centralized DEA DEA DEA Models Data Envelopment Analysis Production Tradeoffs Scale Elasticity

Editors and affiliations

  • Shiuh-Nan Hwang
    • 1
  • Hsuan-Shih Lee
    • 2
  • Joe Zhu
    • 3
  1. 1.Dept of Business AdministrationMing Chuan UniversityTaipeiTaiwan
  2. 2.College of Maritime Science & MgmtNational Taiwan Ocean UniversityKeelungTaiwan
  3. 3.School of BusinessWorcester Polytechnic InstituteWorcesterUSA

Bibliographic information