445 pages | 74 B/W Illus.
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.
The Process of Multicriteria Decision Analysis. Problem Structuring. Making Decisions under Conditions of Certainty with a Small Number of Alternatives. Multi-Objective Mathematical Programming. Probability Review. Modeling Preferences over Risky/Uncertain Outcomes. Modeling Methodologies for Generating Probabilistic Outcomes: Decision Trees and Influence Diagrams. Determining Probabilistic Inputs for Decision Models. The Use of Simulation for Decision Models