Genetically tuned fuzzy scheduling for flexible manufacturing systems

Erkmen, Aydan M.; Erbudak, M.; Anlagan, Omer; Unver, O.

doi:10.1109/robot.1997.614257

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Cited by 6 publications

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“…Different approaches have been employed in order to solve scheduling problems in FMS in an efficient way. For instance, there are works based on Neural Networks [33], Fuzzy Logic [15] and Heuristic and Metaheuristic methods [2,19,22,35,40]. These approaches use artificial intelligence techniques in order to search for the optimal scheduling.…”

Section: Introductionmentioning

confidence: 99%

Control of Flexible Manufacturing Systems under model uncertainty using Supervisory Control Theory and evolutionary computation schedule synthesis

Pena

Costa

Silva

et al. 2016

Information Sciences

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Section: Introductionmentioning

confidence: 99%

Control of Flexible Manufacturing Systems under model uncertainty using Supervisory Control Theory and evolutionary computation schedule synthesis

Pena

Costa

Silva

et al. 2016

Information Sciences

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Job shop scheduling using fuzzy logic

Bilkay

Anlagan

Kılıç

2004

The International Journal of Advanced Manufacturing Technology

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In a flexible manufacturing system (FMS) scheduling problems become extremely complex, even for simple breakdowns, when dynamic uncertainties such as machine breakdowns and the uncertain arrival of jobs are taken into consideration. In the first stage of this study, a fuzzy logic-based algorithm for assigning priorities to part types that are to be machined is proposed. In the second stage, an operation-machine allocation and scheduling algorithm is presented. A criteria contribution equalizer is used in decision-making. The proposed algorithm can re-generate the schedule in case of a machine breakdown, and therefore can be used as an on-line controller. The system architecture and linguistic variables are presented and results showed that the proposed algorithm improves the system efficiency.

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