Fuzzy Sets in Engineering Design and Configuration

As understanding of the engineering design and configuration processes grows, the recognition that these processes intrinsically involve imprecise information is also growing. This book collects some of the most recent work in the area of representation and manipulation of imprecise information during the syn­ thesis of new designs and selection of configurations. These authors all utilize the mathematics of fuzzy sets to represent information that has not-yet been reduced to precise descriptions, and in most cases also use the mathematics of probability to represent more traditional stochastic uncertainties such as un­ controlled manufacturing variations, etc. These advances form the nucleus of new formal methods to solve design, configuration, and concurrent engineering problems. Hans-Jurgen Sebastian Aachen, Germany Erik K. Antonsson Pasadena, California ACKNOWLEDGMENTS We wish to thank H.-J. Zimmermann for inviting us to write this book. We are also grateful to him for many discussions about this new field Fuzzy Engineering Design which have been very stimulating. We wish to thank our collaborators in particular: B. Funke, M. Tharigen, K. Miiller, S. Jarvinen, T. Goudarzi-Pour, and T. Kriese in Aachen who worked in the PROKON project and who elaborated some of the results presented in the book. We also wish to thank Michael J. Scott for providing invaluable editorial assis­ tance. Finally, the book would not have been possible without the many contributions and suggestions of Alex Greene of Kluwer Academic Publishers. 1 MODELING IMPRECISION IN ENGINEERING DESIGN Erik K. Antonsson, Ph.D., P.E.

Table of Contents:
1 Modeling Imprecision in Engineering Design.- 1 Introduction.- 2 Engineering Design with Imprecision.- 3 Combining Imprecision in Design.- 4 Uncontrollable Variables in Design (Noise).- 5 Hybrid Uncertainty.- 6 A Brake Design Application.- 7 Conclusion.- 2 Multiple Objective Design Optimization.- 1 Introduction.- 2 Problem Statement.- 3 Crisp Approaches for MultiObjective Optimization.- 4 Fuzzy Approaches for MultiObjective Optimization.- 5 Cooperative Fuzzy Games.- 6 Numerical Examples.- 7 Summary.- 3 Intelligent Systems for Configuration Problems.- 1 Intelligent Decision Support Systems for Design and Configuration Tasks.- 2 Modelling Design and Configuration Problems Including Fuzziness.- 3 Description of Applications.- 4 Introduction of KONWERK — a Modular Design and Configuration Tool-Kit.- 5 Solving Applications Using the Integrated Approach of Knowledge Based Techniques with Fuzzy Logic and Fuzzy MCDM.- 6 Summary and Conclusions.- 4 Management of Uncertain Information in Simultaneous Engineering.- 1 Introduction.- 2 Aspects of Modeling Uncertain Information.- 3 Model for a Description of Uncertainties on a Contents Level.- 4 Methodology for a Context Dependent Description of Uncertainties.- 5 Information Model to Describe Uncertainties within the Context.- 6 Early Pass on of Information by Structuring Information.- 7 Application of the Methodology.- 8 Conclusion.- 5 Application of the Fuzzy AHP Method for Assessing Alternative Production Cycles.- 1 Introduction.- 2 Selecting which Assessment Method to Apply.- 3 Methods of Weighting and Assessing Stages of Production.- 4 Assessing Production Cycles.- 5 Example of an Assessment Process.- 6 A Method for Personnel Selection in Concurrent Engineering Using Fuzzy Sets.- 1 Introduction.- 2 Personnel Selection inConcurrent Engineering.- 3 Integrated Personnel Planning in CE.- 4 Hierarchical Structure of KSAOs.- 5 Nominal Criteria.- 6 Metric Criteria.- 7 Determination of Overall Suitability.- 8 Conclusion.