Fuzzy logic control of material flow in flexible manufacturing systems

Kamel, E.; Biles, William E.

doi:10.1109/isic.1997.626455

Cited by 1 publication

(2 citation statements)

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“…Furthermore, in producing algorithms or generating optimal schedules for FMS, a lot of constraints such as time, material resources, and so on should be considered. Most researchers try to solve this by employing artificial intelligent or/and soft computing techniques such as the rule-based method [6], genetic algorithms [8,9], fuzzy logic [10,11,35], machine learning [36], neural networks [4,13,37], as well as hybrid methods [2,3,12].…”

Section: Motivationmentioning

confidence: 99%

“…Many researchers have proposed various techniques and algorithms to accelerate the production of manufacturing and to enhance its efficiency using available sources. For example, Chen and Guerrero [6] propose a rule-based algorithm of flexible production systems; He et al [7] and Kumar and Shanker [8] present genetic scheduling; Yang and Wu [9] apply adaptive genetic algorithms in dynamic rescheduling; Kamel and Biles [10] and Kong et al [11] describe fuzzy logic control methods; Low et al [12] deal with the multiple objective problem; and Paulli [13] presents a hierarchical approach.…”

Section: Introductionmentioning

confidence: 99%

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Biological computation of optimal task arrangement for a flexible machining cell

Bakar

Watada

2008

Proceedings of the Institution of Mechanical Engineers, Part I:

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A flexible manufacturing system (FMS) plays an important and central role in today's advanced manufacturing. It replaces human tasks (especially those that are highly dangerous ones), efficiently performed tasks, or crucially precise tasks. Considering the NPhard nature of such computation when the numbers of parameters, robots, or/and tasks are increasing. The objective of this paper is to propose a super parallel computation method optimally to rearrange tasks of an FMS in a production line. A biological computing approach is presented to minimize the waiting time of machines and workstations, and maximize the usage of robots. Biological computing with powerful massive parallelism enables the generation of all feasible solutions at one time, as opposed to the limitation of conventional computing in reaching an optimal solution. The proposed method is illustrated using two different examples of single and multiple robots. Finally, solving an FMS problem is explained from a biological computing point of view.

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