Overview

In today's computer-integrated manufacturing environment, decision-makers typically require access to a vast amount of data to support and analyze their complex decision problems at the strategic and tactical levels. Increasingly, there is a need for decision support systems that allow decision-makers to communicate and solve their problems interactively. This text provides a forum for research and applications dealing with the design, development and implementation of decision support systems in manufacturing. It should appeal to all those concerned with decision support for manufacturing.

Full Product Details

Author:   Hamid R. Parsaei ,  Thomas R. Hanley ,  S.S. Kolli
Publisher:   Chapman and Hall
Imprint:   Chapman and Hall
Edition:   1997 ed.
Volume:   1
Dimensions:   Width: 15.50cm , Height: 1.90cm , Length: 23.50cm
Weight:   1.400kg
ISBN:  

9780412570407

ISBN 10:   0412570408
Pages:   302
Publication Date:   31 December 1996
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1. A generalized cost analysis system for manufacturing simulation.- 1.1 Introduction.- 1.2 Review of past work.- 1.3 Design of the cost analysis system.- 1.4 System development and implementation.- 1.5 System validation.- 1.6 Conclusions and recommendations.- Acknowledgements.- References.- 2. A decision support system for the justification of computer-integrated manufacturing.- 2.1 Introduction.- 2.2 A DSS for CIM justification.- 2.3 DSS description.- 2.4 Activity-based costing.- 2.5 CIM and the firm’s profit and loss statement.- 2.6 Optimization models in CIM justification.- 2.7 The simulation model.- 2.8 The decision support system.- 2.9 Discussion and further research.- References.- 3. Linking strategies to actions: integrated performance measurement systems for competitive advantage.- 3.1 Introduction.- 3.2 Linking strategies to actions.- 3.3 Integrating performance measurement systems.- 3.4 Manufacturing decision support systems.- 3.5 Case example: the Hill’s® Pet Products Division of the Colgate-Palmolive Company.- 3.6 Conclusions.- References.- 4. Intelligent decision support for quality function deployment.- 4.1 Introduction.- 4.2 Active versus passive quality.- 4.3 Quality function deployment support system.- 4.4 Conclusion.- References.- 5. A knowledge-based decision support system for apparel enterprise evaluation.- 5.1 Introduction.- 5.2 Project objective.- 5.3 Current procurement and source selection procedures.- 5.4 Apparel manufacturing and quality control.- 5.5 Knowledge acquisition.- 5.6 Choice selection methodologies and selection of the inference mechanism for the decision support system.- 5.7 Design of the knowledge framework.- 5.8 The knowledge framework.- 5.9 Software implementation of decision support tool.- 5.10 User interface for BEST.-5.11 BESTForms implementation.- 5.12 BEST results.- 5.13 Conclusions.- Acknowledgements.- References.- 6. Heuristic decision support system database structure for diagnostic expert systems.- 6.1 Introduction.- 6.2 Handling uncertainty in diagnostic systems.- 6.3 Dempster-Shafer theory.- 6.4 Fuzzy logic.- 6.5 Problem-cause relationships.- 6.6 Design of the matrix structure.- 6.7 Example of the matrix.- 6.8 Database requirements for the matrix.- 6.9 Generating the knowledge base.- 6.10 Conclusions and recommendations.- References.- 7. Object-oriented organization of network flow problem-solving knowledge for manufacturing decision support systems.- 71 Introduction.- 7.2 Applications in manufacturing decision support systems.- 7.3 Object-oriented paradigm.- 7.4 Network flow problems.- 7.5 The object-based problem-solving knowledge for network flow problems.- 7.6 Implementation and observations.- 7.7 Conclusions.- Acknowledgements.- References.- 8. Machine learning for process parameter selection in intelligent machining.- 8.1 Introduction.- 8.2 Machine learning technique.- 8.3 Data acquisition.- 8.4 Results.- 8.5 Conclusions.- References.- 9. An interactive program for machine grouping and layout.- 9.1 Introduction.- 9.2 Literature review.- 9.3 Solution method.- 9.4 Interactive decision support system.- 9.5 Conclusions.- Acknowledgements.- References.- 10. Intelligent scheduling systems: an artificial-intelligence-based approach.- 10.1 Introduction.- 10.2 Issues involved in scheduling.- 10.3 An overview of genetic algorithms.- 10.4 Simulation design.- 10.5 Experiments.- 10.6 Conclusions.- Acknowledgements.- References.- 11. Optimizing assembly time for printed circuit board assembly.- 11.1 Introduction.- 11.2 Notations and problem statement.- 11.3 Optimization using integer programming.- 11.4 Simulation results.- 11.5 Conclusion.- Acknowledgements.- References.- 12. On integration of statistical process control and engineering process control: a neural network application.- 12.1 Introduction.- 12.2 Preliminaries.- 12.3 Simulation experiments.- 12.4 Conclusions.- Acknowledgements.- References.- 13. Constraint-based genetic algorithms for concurrent engineering.- 13.1 Introduction.- 13.2 Approaches to concurrent engineering systems.- 13.3 Research areas in concurrent engineering systems.- 13.4 Constraint network modeling and genetic algorithms.- 13.5 Overview of the CBGA concurrent engineering system.- 13.6 A CBGA design session.- 13.7 Summary and conclusion.- Acknowledgements.- References.- 14. Computer-integrated manufacturing: a complex information system.- 14.1 Introduction.- 14.2 Overview of programmable technologies used in computer-integrated manufacturing.- 14.3 A conceptualized information system for computer-integrated manufacturing.- 14.4 Toward the challenge of a computer-integrated economy.- 14.5 Conclusions.- References.

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