Intelligent decision support for flexible manufacturing: Design and implementation of a knowledge-based simulator

Pflughoeft, Kurt; Hutchinson, George K.; Nazareth, Derek L.

doi:10.1016/0305-0483(96)00004-7

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…However, the decision-maker in the end will have the final responsibility for the decisions that have to be made. As Pflughoeft et al (1996) state, many of the decisions faced by the decision-maker must be made timely in order to avoid adverse consequences and therefore the speed of the decisionmaking process is perhaps more important than obtaining an optimal solution.…”

Section: Resultsmentioning

confidence: 99%

“…The simulator interacting with its environment will then design the appropriate what-if experiments, execute a series of simulation runs, record the results, and then perform an analysis of the results. The decisionmaker is then left with the analysis of the results to make the final decision (Pflughoeft et al, 1996).…”

Section: Integration Of Technologiesmentioning

confidence: 99%

“…They concluded that knowledgebased systems can increase the application range between simulation and the user. Pflughoeft et al (1996) describe an architecture of an intelligent knowledge-base simulator (KBSim) that provides a systematic research capability for flexible manufacturing systems (FMS). The KBSim is applied to a FMS scheduling problem to reduce mean flow time and tardiness.…”

Section: Introductionmentioning

confidence: 99%

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Integrating information technologies and knowledge‐based systems: a theoretical approach in action for enhancements in production and inventory control

Reyes

Raisinghani

2002

Knowl Process Manag

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The heightened focus of global competition has increased the necessity of manufacturing in combination with information technologies to continuously improve production flexibility and product quality, delivery and cost. Coupled with uncertainty and delays in information transmission lags, this increased focus has also created confusion and difficulties in production and inventory control management. Many manufacturers have abandoned the simple make-tostock environments in favor of more complex make-to-order, configure-to-order, and engineerto-order. At the heart of this range of options is the information technology (IT) that makes it all possible. Of the most popular new technologies for supporting production and inventory control (PIC) are solver technologies, intelligent decision support systems, and knowledge-based systems. This paper discusses the integration of IT and knowledge management (KM) and the future trends in KM that are likely to impact PIC.

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Section: Resultsmentioning

confidence: 99%

Section: Integration Of Technologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating information technologies and knowledge‐based systems: a theoretical approach in action for enhancements in production and inventory control

Reyes

Raisinghani

2002

Knowl Process Manag

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“…To a large degree, our understanding of FMS is fragmented as there are significant differences in assumptions, constraints, modeling techniques, solution strategies, criteria, objectives, and decision processes. The results that are obtained for one decision situation may not apply in others [19]. Thus, a general and effective method is needed for the analysis of FMS.…”

Section: Introductionmentioning

confidence: 89%

The simulation design and analysis of a Flexible Manufacturing System with Automated Guided Vehicle System

Cheon

Lee

2009

Journal of Manufacturing Systems

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“…Researchers have also combined simulation with artificial intelligence (e.g., Chan et al 2000) and developed knowledge-based simulation models (e.g., Pflughoeft et al 1996). In addition, a variety of decision support tools have been developed for FMaS and FAS design using simulation and/or analytical tools (e.g., Borenstein 1998a, b;Lee and Stecke 1996;Mellichamp et al 1990).…”

Section: Literature Reviewmentioning

confidence: 99%

A framework for capacity planning and machine configuration in flexible assembly systems

Lee

Srinivasan

Yano

2006

Int J Flex Manuf Syst

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We consider the problem of simultaneously determining the number of machines (and/or workers), the assignment of tasks (and related tools and components) to these machines, and the number of jobs circulating in a flexible assembly system (FAS), to satisfy steady-state throughput requirements for a family of similar products at minimum cost. We focus on situations where there are precedence relations among the various tasks, as is common in assembly systems. We present a framework for solving this problem based on a heuristic decomposition approach which involves the solution of only a few types of sub-problems. We demonstrate the efficiency and effectiveness of the overall procedure using a number of example problems.

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Intelligent decision support for flexible manufacturing: Design and implementation of a knowledge-based simulator

Cited by 12 publications

References 14 publications

Integrating information technologies and knowledge‐based systems: a theoretical approach in action for enhancements in production and inventory control

Integrating information technologies and knowledge‐based systems: a theoretical approach in action for enhancements in production and inventory control

The simulation design and analysis of a Flexible Manufacturing System with Automated Guided Vehicle System

A framework for capacity planning and machine configuration in flexible assembly systems

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