TOPSIS and its Extensions: A Distance-Based MCDM Approach

Hsu-Shih Shih · David L. Olson

Sep 2022 · Studies in Systems, Decision and Control Book 447 · Springer Nature

About this ebook

The objective of the book is to provide materials to demonstrate the development of TOPSIS and to serve as a handbook. It contains the basic process of TOPSIS, numerous variant processes, property explanations, theoretical developments, and illustrative examples with real-world cases. Possible readers would be graduate students, researchers, analysts, and professionals who are interested in TOPSIS, a distance-based algorithm, and who would like to compare TOPSIS with other MCDM methods. The book serves as a research reference as well as a self-learning book with step-by-step illustrations for the MCDM community.

About the author

Hsu-Shih Shih is a Professor in the Department of Management Sciences at Tamkang University, Taiwan, R.O.C. He obtained his Ph.D. degree at the Department of Industrial and Manufacturing Systems Engineering of Kansas State University, Manhattan, Kansas, U.S.A. in 1995; an MS degree at the Department of Mechanical Engineering at Chung Cheng Institute of Technology, Taiwan in 1984; and a BS degree at the Department of Naval Architecture and Marine Engineering of the same institute in 1978. He has also been a visiting scholar at the University of Pittsburgh, U.S.A. (2007), Aalto University, Finland (2010), and the Norwegian School of Economics, Norway (2014). His areas of specialization include decision analysis and support, operations research, soft computing, and environmental management. His publications appear in European Journal of Operational Research, Computers and Operations Research, Journal of Operational Research Society, Central European Journal of Operations Research, Operational Research: An International Journal, Group Decision and Negotiation, Fuzzy Sets and Systems, Journal of Cleaner Production, Information Science, Computers and Mathematics with Applications, Mathematical and Computer Modelling, Applied Mathematics Letters, etc., along with four book chapters, and three books by Springer. He has been Secretary (2012-13), Council Member (2014-15), President (2016-17), and Supervisor (2018-19) of the Operations Research Society of Taiwan, as well as a member of the executive committee of International Society on Multiple Criteria Decision Making (2017-21).

David L. Olson is the James & H.K. Stuart Professor and Chancellor’s Professor at the University of Nebraska. He has published research in over 200 refereed journal articles, primarily on the topic of multiple objective decision-making, information technology, supply chain risk management, and data mining. He teaches in the management information systems, management science, and operations management areas. He has authored over 40 books, to include Decision Aids for Selection Problems, Introduction to Information Systems Project Management, Managerial Issues of Enterprise Resource Planning Systems, Supply Chain Risk Management, and Supply Chain Information Technology. Additionally, he has co-authored the books Introduction to Business Data Mining, Enterprise Risk Management, Advanced Data Mining Techniques, Enterprise Information Systems, Enterprise Risk Management Models, and Financial Enterprise Risk Management. He has served as associate editor of Service Business, Decision Support Systems, and Decision Sciences and co-editor in chief of International Journal of Services Sciences. He has made over 200 presentations at international and national conferences on research topics. He is a member of the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He was a Lowry Mays endowed Professor at Texas A&M University from 1999 to 2001. He was named the Raymond E. Miles Distinguished Scholar award for 2002, and was a James C. and Rhonda Seacrest Fellow from 2005 to 2006. He was named Best Enterprise Information Systems Educator by IFIP in 2006. He is a Fellow of the Decision Sciences Institute.