Manufacturing Decision Support Systems de Hamid R. Parsaei

Manufacturing Decision Support Systems: Manufacturing Systems Engineering Series, cartea 1

Hamid R. Parsaei

Thomas R. Hanley

S.S. Kolli

During the last two decades, a tremendous growth in the popularity and applications of computers in manufacturing has occurred. Computer aided design, computer-aided manufacturing, flexible manufacturing systems, group technology and many others are considered by many manufacturing executives as the most promising technologies and philosophies that, if successfully implemented, can reduce costs and enable the US manufacturing companies to become more competitive in the global market. In the computer-integrated manufacturing environ ment, the decision processes are often more involved. The decision makers are frequently required to have access to a vast amount of data to support and analyze their complex decision problems at strategic and tactical levels. Decision support systems are often referred to as computer-based information technologies that allow the decision makers to interactively communicate and solve the decision problems. Manufacturing Decision Support Systems is intended to report the latest developments and address the central issues in this area. This volume consists of 14 refereed chapters, written by leading researchers from academia and industry.

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Cuprins

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.