One of the most important tasks faced by decision-makers in business and government is that of selection. Selection problems are challenging in that they require the balancing of multiple, often conflicting, criteria. In recent years, a number of interesting decision aids have become available to assist in such decisions.
The aim of this book is to provide a comparative survey of many of the decision aids currently available. The first chapters present general ideas which underpin the methodologies used to design these aids. Subsequent chapters then focus on specific decision aids and demonstrate some of the software which implement these ideas. A final chapter provides a comparative analysis of their strengths and weaknesses.

Autorentext

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 has authored over 50 books. He has served as an associate editor of Service Business, Decision Support Systems, Journal of Business Analytics, Decision Sciences and of various IEEE journals. 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 received the Herbert Simon Award for Outstanding Contribution in Information Technology and Decision Making in 2021. He is a fellow of the Decision Sciences Institute. Desheng Dash Wu is a distinguished professor at the Economics and Management School, University of Chinese Academy of Sciences, Beijing, China. He has published over 150 ISI-indexed papers in refereed journals and 8 books with Springer. His current research interests include mathematical modeling of systems containing uncertain and risky situations, with a special interest in the finance-economics operations interface, maximizing operational and financial goals using the methodologies of game theory and large-scale optimization. He is an elected member of the Academia Europaea (the Academy of Europe), elected member of the European Academy of Sciences and Arts, and elected member of the International Eurasian Academy of Sciences. Cuicui Luo is an associate professor at the International College of the University of Chinese Academy of Sciences, specializing in decision-making, machine learning, risk management, and financial mathematics. She earned her Ph.D. in financial mathematics from the University of Toronto in 2015. She also holds a Master's degree in Mathematical Finance (2009) and a Bachelor's degree in Actuarial Science (2006), both from the University of Toronto. Dr. Luo has published over 30 articles in esteemed academic journals. Her research focuses on the interplay between decision-making, machine learning, and portfolio optimization. She actively contributes to these fields through her scholarly publications and ongoing research. Majid Nabavi earned his B.S. and M.S. degrees from the University of Tehran and MBA and Ph.D. in business administration with an emphasis in operations management from the University of Nebraska-Lincoln. His teaching areas include operations management, management science, database systems, and business analytics. Dr. Nabavi is a faculty member in the College of Business at the University of Nebraska-Lincoln. He has published in Quality Management Journal and Journal of Brand Strategy and presented research in regional and national conferences. He has been a co-principal investigator in research grant proposals and co-authored a book, Introduction to Business Analytics.

Inhalt
1 Introduction.- Multiple Criteria Decision Making.- Value.- Software Sources.- References.- 2 Developing Criteria Hierarchies.- Hierarchies.- Attributes.- Hierarchy Development Process.- Suggestions for Cases where Preferential Independence is Absent.- Energy Hierarchies.- Conclusions.- References.- 3 Multiattribute Utility Theory.- Case: Nuclear Depository Siting.- Multiattribute Utility Theory.- Tradeoff Analysis.- Nuclear Dump Site Selection.- House Selection.- Conclusions.- References.- 4 Smart.- The SMART Technique.- Independence.- Nuclear Dump Site Example.- House Selection Example.- SMARTS.- Nuclear Site Selection with SMARTS.- SMARTER.- House Selection Example Using SMARTER.- Summary.- References.- 5 The Analytic Hierarchy Process.- Description of AHP.- Hierarchy Development.- Subjective Pairwise Comparisons.- Calculation of Implied Weights.- Consistency Measure.- Synthesis.- Calculations for the Nuclear Waste Disposal Siting Problem.- Calculations for the Housing Selection Decision.- Real-World Applications of AHP.- Computer Support for AHP.- Summary.- Appendix: Calculation of Maximum Eigen Value and Eigenvector.- References.- 6 Geometric Mean Technique.- Geometric Mean Solution.- REMBRANDT.- Nuclear Disposal Site Selection Problem.- House Selection Calculations.- References.- 7 Preference Cones.- Procedure.- Basing Objective on Current Best Choice.- Basing Objective on Worst Choice to Date.- Adjacency Formulation.- Cellular Manufacturing Example.- Basing Objective on Current Best Choice.- Basing Objective on Worst Choice to Date.- Automation Example.- Nuclear Dump Site Selection.- Conclusions.- References.- 8 Outranking Methods.- ELECTRE.- Cellular Manufacturing Example.- Automation Example.- Nuclear Dump Site Selection ELECTRE II.- PROMETHEE.- GAIAOutput.- Nuclear Dump Site SelectionPROMETHEE.- Applications of Outranking Methods.- Conclusions.- References.- 9 Zapros.- Demonstration Model.- Nuclear Dump Site Selection.- House Selection.- Conclusions.- References.- Appendix: Input Files.- 10 Aspiration-Level Interactive Model.- Methodology.- Nuclear Dump Site Selection.- Cellular Manufacturing Example.- Summary.- References.- 11 Visual Interactive Method.- VIMDA Overview.- VIMDA Procedure.- Nuclear Dump Site Selection.- Cellular Manufacturing Example.- Summary.- References.- 12 Models with Uncertain Estimates.- ARIADNE.- Parking Example.- Nuclear Waste Disposal Siting Problem.- Uncertain Value Contributions.- House Buying Example.- HIPRE 3+.- Parking Example.- Nuclear Dump Site Example.- Summary.- References.- 13 Comparisons.- Task Type.- Task Dimensionality.- Task Uniqueness.- Decision Maker Cognitive Effort.- Human Subject Responses.- Aid to Decision Maker Learning.- Synthesis.- Conclusion.- References.- Author Index.- Topic Index.