Linking the Witness Simulation Software to an Expert System to Represent a Decision�Making Process

Robinson, Stewart; Edwards, John S.; Yongfa, Wu

doi:10.2498/cit.2003.02.05

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…There is a wealth of research which explains how manufacturing organisations can improve knowledge management. Applied research on the management of knowledge in manufacturing organisations has explored the link between knowledge management and: organisational performance (Germain et al, 2001); information technology (Beckett et al, 2000;Prasad, 2000;Leseure and Brookes, 2001); competitiveness (Paiva et al, 2002); the transfer of best practice (Collinson, 1999); inter-organisational networking (Robertson et al, 1996;Chaston and Mangles, 2000); organisational learning (Mohanty and Deshmukh, 1999;Chaston et al, 2001;Ordonez de Pablos, 2002;Robinson et al, 2003).…”

Section: Managing Knowledge In Manufacturing Organisationsmentioning

confidence: 99%

Manufacturing knowledge management strategy

Shaw

Edwards

2006

International Journal of Production Research

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Manufacturing knowledge management strategy  Purpose: To understand the components of knowledge management strategy from the perspective of staff in UK manufacturing organisations.  Methodology/Approach: Empirical approach-data collected during workshops conducted in two manufacturing organisations.  Findings: The key components of a knowledge management strategy, and the relationships between it and manufacturing strategy and corporate strategy. Other findings include: the nature of knowledge in manufacturing organisations; the relevance of (in)formal processes; top-down and bottom-up communication; taking ownership for information processes.  Research limitations/implications: Implications for theory development in manufacturing strategy and its alignment with corporate strategy.  Practical implications: Implications for doing knowledge management, and developing strategy, in manufacturing organisations.

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Section: Managing Knowledge In Manufacturing Organisationsmentioning

confidence: 99%

Manufacturing knowledge management strategy

Shaw

Edwards

2006

International Journal of Production Research

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“…In [23], the authors demonstrated how a simulation model developed using a commercial-off-the-shelf (COTS) simulation package can be linked to an external expert system and how decisions generated by the expert system can be fed back into the simulation system subsequently.…”

Section: Related Workmentioning

confidence: 99%

An agent-based approach for managing symbiotic simulation of semiconductor assembly and test operation

Low

Lye

Lendermann

et al. 2005

Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems

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The rapid changing business environment of high-tech asset intensive enterprises such as semiconductor manufacturing constantly drives production managers to look for better solutions to improve the manufacturing process. Simulation, though identified to be the most appropriate technique to generate and test out possible execution plans, suffers from long cycle-time in the process of model update, analysis and verification. It is thus very difficult to carry out prompt "what-if" analysis to respond to abrupt changes in these systems. Symbiotic simulation systems have been proposed as a way of solving this problem by having the simulation and the physical system interact in a mutually beneficial manner. In this paper, we describe our work in developing a prototype proof-of-concept symbiotic simulation system that employs software agents in the monitoring, optimization and control of a semiconductor assembly and test operation.

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“…Several authors have also published work on integrating simulation with an expert system for decisionmaking processes. They include: Robinson et al [29] and Raju [30], whose work covered integrating Xper-tRule (an expert system) with Witness (an off-the-shelf simulator) using a model controller that controlled the actions of both XpertRule and Witness; Flitman and Hurion [31], who proposed the approach of linking a simulation written in FORTRAN with a PROLOGbased expert system; O'Keefe [32], who developed a tool that linked a simulator with PROLOG; and Lyu and Gunasekaran [33], who discussed the integration of an expert system with simulation to evaluate and improve scheduling strategies. The majority of publications under this approach fall short of demonstrating their application in real industry.…”

Section: Introductionmentioning

confidence: 99%

Manufacturing performance optimization: The simulation—expert mechanism approach

Mebrahtu

Walker

Dionysopoulos³

et al. 2009

Proceedings of the Institution of Mechanical Engineers, Part B:

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This paper presents an expert mechanism approach to manufacturing performance optimization using simulation as the base tool. The expert mechanism is integrated to the back end of a manufacturing simulator to interpret manufacturing simulation results, assess performance, and then, consistent with set constraints, to effect changes on controllable variables prior to the next run to improve performance. The expert mechanism has a knowledge base that includes proven operations management performance-enhancing methods. In contrast, existing commercial simulation-optimization methods use meta-heuristics in which a nearoptimum value is searched from a population of alternative solutions, which can be inefficient in terms of time and cost. The findings of a real case study from a world-class manufacturing company are discussed to demonstrate the expert mechanism and compare it with one of the widely used commercial simulation optimizers.

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Linking the Witness Simulation Software to an Expert System to Represent a Decision�Making Process

Cited by 10 publications

References 11 publications

Manufacturing knowledge management strategy

Manufacturing knowledge management strategy

An agent-based approach for managing symbiotic simulation of semiconductor assembly and test operation

Manufacturing performance optimization: The simulation—expert mechanism approach

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