This textbook presents methodologies and applications associated with multiple criteria decision analysis (MCDA), especially for those students with an interest in industrial engineering. With respect to methodology, the book covers (1) problem structuring methods; (2) methods for ranking multi-dimensional deterministic outcomes including multiattribute value theory, the analytic hierarchy process, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and outranking techniques; (3) goal programming; (4) methods for describing preference structures over single and multi-dimensional probabilistic outcomes (e.g., utility functions); (5) decision trees and influence diagrams; (6) methods for determining input probability distributions for decision trees, influence diagrams, and general simulation models; and (7) the use of simulation modeling for decision analysis.
This textbook also offers:
- Easy to follow descriptions of how to apply a wide variety of MCDA techniques
- Specific examples involving multiple objectives and/or uncertainty/risk of interest to industrial engineers
- A section on outranking techniques; this group of techniques, which is popular in Europe, is very rarely mentioned as a methodology for MCDA in the United States
- A chapter on simulation as a useful tool for MCDA, including ranking & selection procedures. Such material is rarely covered in courses in decision analysis
- Both material review questions and problems at the end of each chapter . Solutions to the exercises are found in the Solutions Manual which will be provided along with PowerPoint slides for each chapter.
The methodologies are demonstrated through the use of applications of interest to industrial engineers, including those involving product mix optimization, supplier selection, distribution center location and transportation planning, resource allocation and scheduling of a medical clinic, staffing of a call center, quality control, project management, production and inventory control, and so on. Specifically, industrial engineering problems are structured as classical problems in multiple criteria decision analysis, and the relevant methodologies are demonstrated.
About the Author: Gerald W. Evans is Professor Emeritus in the Department of Industrial Engineering at the University of Louisville (UL). His research and teaching interests lie in the areas of multiciteria decision analysis, simulation modeling and analysis, optimization, logistics and project management.
Dr. Evans received his BS in Mathematics in 1972, his MS in Industrial Engineering in 1974, and his PhD in Industrial Engineering in 1979, all from Purdue University.
Dr. Evans has served as Principal Investigator or Co-Principal Investigator on over $3 million of funded research from organizations such as the National Science Foundation, the Defense Logistics Agency, NASA, the National Institute for Hometown Security, Louisville Metro Government, General Electric, and United Parcel Services among other organizations. In addition, he has consulted in the areas of simulation modeling and analysis, project management, and economic analysis for a variety of organizations.
He has published approximately 100 papers in various journals and conference proceedings.
Dr. Evans has received the Fellow Award and the Operations Research Division Award from IIE, the Moving Spirit Award from INFORMS for his work with the UL INFORMS Student Chapter, the Dean's Award for Outstanding Graduate Teaching; and he was a University of Louisville nominee for Outstanding Faculty of Adult Learners for Kentuckiana Metroversity Inc. He is listed in American Men and Women of Science, Who's Who in Engineering, and Who's Who in America.
