An Artificial Intelligence Approach to the Scheduling of Flexible Manufacturing Systems

Shaw, Michael J.; Whinston, Andrew B.

doi:10.1080/07408178908966221

Cited by 48 publications

(20 citation statements)

References 19 publications

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“…Consequently, the criterion for selecting an appropriate system attribute is the chief concern. Shaw and Whinston (1989) suggest that a proper system attribute is the one which is more informed than others in indicating system status. Besides, a global system attribute is better than a local one; a domain-specific system attribute is preferred to a general one.…”

Section: Mru Algorithmmentioning

confidence: 97%

Intelligent scheduling of FMSs with inductive learning capability using neural networks

Wang¹,

Chen²,

Liu³

1995

Int J Flex Manuf Syst

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Abstract. With the growing uncertainty and complexity in the manul~acturing environment, most scheduling problems have been proven to be NP-complete and this can degrade the performance of conventional operations research (OR) techniques~ This article presents a system-attribute-oriented knowledge-based scheduling system (SAOSS) with inductive learning capability. With the rich heritage from artificial intelligence (AI), SAOSS takes a multialgorithm paradigm which makes it more intelligent, flexible, and suitable than others for tackling complicated, dynamic scheduling problems.SAOSS employs an efficient and effective inductive learning method, a continuous iterative dichotomister 3 (CID3) algorithm, to induce decision rules for scheduling by converting corresponding decision frees into hidden layers of a self-ger~rated neural network. Connection weights between hidden units imply the schethling heuristics, which are then formulated into scheduling rules. An FMS scheduling problem is also given for illustration. The scheduling results show that the system-attribute-oriented knowledge-based approach is capable of addressing dynamic scheduling problems.

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Section: Mru Algorithmmentioning

confidence: 97%

Intelligent scheduling of FMSs with inductive learning capability using neural networks

Wang¹,

Chen²,

Liu³

1995

Int J Flex Manuf Syst

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“…Cho and Wysk (1995) viewed the problem from a CIM viewpoint, and discussed the requirements, problems and models for an intelligent workstation controller. Davis and Jones (1989) ¢ McConnell and Medeiros (1992) ,/ Rogers and Gordon (1993) , ,/ ,/ ¢ Kim and Kim (1994) , / Harmonosky and Robohn (1995) J ¢ Hatono et al (1992) ,/ Choobineh and Shivani (1992 Shaw (1986) ,/ ¢" Shen and Chang (1986) ¢ Subramanyam and Askin (1986) ,/ Maimon (1987) / ,/ ¢ Bu-Hulaiga and Chakravarty (1988) ¢ , / Maley et al (1988) ,/ O'Grady and Lee (1988) ¢ ¢ Shaw (1988) ¢ ¢ Young and Rossi (1988) ,/ ,/ Kusiak (1989) ,/ Shaw and Whinston (1989) , / Joshi et al (1992) ,/ Teltz and Elbestawi (1992) ,/ Karabuk and Sabuncuoglu (1993) ¢ / Heikkila et aL (1994) ,/ Lefrancois and Montreuil (1994) ,/ Ranky (1992) ,/ ,/ ,/ Ravichandran and Chakravarty (1986) ¢ Gentina and Corbeel (1987) , / Ferreira et al (1991) / ¢ / Shih and Sekiguchi (1991) , / Huang and Chang (1992) ¢ Lee and DiCesare (1992) , / Camurri et al (1993) ¢ Raju and Chetty (1993a) ,/ Raju and Cherty (1993b) ,/ ¢ Cheng et al (1994) ,/ ,/ Masory (1994) , / Valavanis et al (1994) ,/ Lin and Chung (1986) ¢ Wu and Wysk (1988) / ,/ Rabelo and Alptekin (1989) ,/ Banks (1992) (1991) ,/ ¢ Wu (1992) / Sagi et al …”

Section: Research Statusmentioning

confidence: 99%

“…The generation process used heuristic algorithm A*. In a related paper, Shaw and Whinston (1989) used similar concepts, and executed FMS scheduling by means of a non-linear planning algorithm. They decomposed an njob-m-machine problem into n subproblems, each one defined as a routeing of one job.…”

Section: Research Statusmentioning

confidence: 99%

The state of the art in intelligent real-time FMS control: a comprehensive survey

Shukla

Chen

1996

J Intell Manuf

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Flexible manufacturing systems (FMS) have been developed with the hope that they would provide a means to tackle a threefold challenge -better quality, lower cost and shorter lead times -by integrating machine tools, robots, material handling and storage systems, and computers. Control of the integrated system presented a new set of problems as well as challenges, which have been receiving considerable attention from the academic community as well as from industrial system users. Intelligent control, which involves using computers to assist in decision making at various stages of the control process, has been advocated by many researchers as a possible avenue to reach a solution to these problems. This paper provides a review of the state of the art in intelligent control of FMS, in an attempt to supplement earlier general reviews via a more focused perspective. The principles of several techniques, namely simulation, knowledge based, example based, petri nets, and hybrid approaches are briefly introduced, and publications are reviewed, followed by discussions ontheir potential. Suggestions for further research and development are also enumerated.

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“…In addition, the assortment of dynamic dispatching rules can be incorporated in the Schedule-Generation Algorithm as heuristic functions (Shaw [1986d] In effect, the scheduling problem of the cellular system is partitioned into two decisions:…”

Section: The Networking Environment In Cimmentioning

confidence: 99%

An Artificial Intelligence Approach to the Scheduling of Flexible Manufacturing Systems

Cited by 48 publications

References 19 publications

Intelligent scheduling of FMSs with inductive learning capability using neural networks

Intelligent scheduling of FMSs with inductive learning capability using neural networks

The state of the art in intelligent real-time FMS control: a comprehensive survey

Dynamic scheduling in cellular manufacturing systems: A framework for networked decision making

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