Formal models for control of flexible manufacturing cells: physical and system model

Joshi, Sanjay; Mettala, Erik; Smith, Jeffrey S.; Wysk, Richard A.

doi:10.1109/70.406940

Cited by 45 publications

(16 citation statements)

References 19 publications

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“…In order to avoid the complexity of real-time scheduling of 1274 N. Buyurgan and A. Mendoza manufacturing systems, heuristics is used for representing the dynamic behaviour of FMSs (Vancheeswaran andTownsend 1993, He et al 1993). Over the last decade, simulation models have also been utilized as real-time schedulers for manufacturing systems (Smith et al 1994, Harmonosky 1995, Joshi et al 1995, Chan and Chan 2004. Artificial intelligence-based approaches are generally used to solve operational problems of manufacturing systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance-based dynamic scheduling model for flexible manufacturing systems

Buyurgan¹,

Mendoza²

2006

International Journal of Production Research

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A performance-based dynamic scheduling model for random flexible manufacturing systems (FMSs) is presented. The model is built on the mathematical background of supervisory control theory of discrete event systems. The dynamic FMS scheduling is based on the optimization of desired performance measures. A control theory-based system representation is coupled with a goal programming-based multi-criteria dynamic scheduling algorithm. An effectiveness function, representing a performance index, is formulated to enumerate the possible outputs of future schedules. Short-term job scheduling and dispatching decisions are made based on the values obtained by optimizing the effectiveness function. Preventive actions are taken to reduce the difference between actual and desired target values. To analyse the real-time performance of the proposed model, a software environment that included various Visual Basic Application Õ modules, simulation package Arena Õ , and Microsoft Access Õ database was developed. The experimentation was conducted (a) to determine the optimum look-ahead horizons for the proposed model and (b) to compare the model with conventional scheduling decision rules. The results showed that the proposed model outperformed well-known priority rules for most of the common performance measures.

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Section: Literature Reviewmentioning

confidence: 99%

Performance-based dynamic scheduling model for flexible manufacturing systems

Buyurgan¹,

Mendoza²

2006

International Journal of Production Research

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“…The role of operators is important in increasing the flexibility of a material handling system (Joshi et al, 1995). Even with today's advanced manufacturing systems, people have not been replaced entirely by automation (Fasth et al, 2011) because people (1) add flexibility; (2) are better at perceiving patterns; (3) improvise procedures when congestion occurs; (4) reason inductively to avoid deadlock and bottlenecks; and (5) exercise judgment concerning critical activities (Sheridan, 2000).…”

Section: Materials Handling Flexibilitymentioning

confidence: 99%

Effect of cognitive automation in a material handling system on manufacturing flexibility

Choe

Tew

Tong

2015

International Journal of Production Economics

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“…A series of academic research efforts focused more on the real-time operational details of the production activity and the formal characterization of the underlying system behaviours, can be found in Naylor and Volz (1987), Behuniak et al (1992), Tirpak et al (1992), Cossins and Ferreira (1992), Jafari (1992), Joshi et al (1995) and Fanti et al (1996). A key contribution of these works has been shifting the emphasis from the strictly performance oriented issues in manufacturing system related research, to issues also concerning the logical correctness and robustness of the system operation, a crucial factor for the automation of these environments.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, there has been some effort to address these problems during the controller design phase, but the currently implemented policies seem to be completely customized to the particular fab operation, and have been developed in a rather ad-hoc fashion. Hence, the resulting control logic is not formally validated, and cannot be easily reconfigured under changing circumstances (Joshi et al 1995).…”

Section: Introductionmentioning

confidence: 99%

A distributed, event-driven control architecture for flexibly automated manufacturing systems

Park¹,

Reveliotis²,

Bodner³

et al. 2002

International Journal of Computer Integrated Manufacturing

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This paper presents a new distributed real-time control architecture for flexibly automated production systems. The modelling assumptions underlying the design are driven by, and abstract, the structure and operations of the emerging 300 mm semiconductor manufacturing fab, one of the most extensively automated environments in contemporary manufacturing. The key element of the controller design itself, which differentiates it from past efforts, is the distribution of the control function to the constituent components of the system shop-floor architecture, while maintaining both the logical correctness and the efficiency of the system behaviour. The architecture was designed to be easily implementable in the actual system shop-floor, and therefore is aligned with, and augments, current practices in these environments. From a theoretical perspective, the proposed design has employed, integrated and extended a series of theoretical results from the field of Discrete Event Dynamical Systems. It is our expectation that the proposed architecture will also provide a formal framework for further analytical studies on the performance evaluation and performance-oriented control/scheduling of the considered class of manufacturing systems.

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Formal models for control of flexible manufacturing cells: physical and system model

Cited by 45 publications

References 19 publications

Performance-based dynamic scheduling model for flexible manufacturing systems

Performance-based dynamic scheduling model for flexible manufacturing systems

Effect of cognitive automation in a material handling system on manufacturing flexibility

A distributed, event-driven control architecture for flexibly automated manufacturing systems

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