Monitoring manufacturing systems by means of Petri nets with imprecise markings

Valette, Robert; Cardoso, Janétte; Dubois, Didier

doi:10.1109/isic.1989.238688

Cited by 68 publications

(13 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence all the transitions leading to/from "bad states" are also eliminated. For example, let us consider a case of M 14 = [2, 3,6,7,8,12]. Here all the conveyors are in idle state.…”

Section: Controller Synthesis Using Thementioning

confidence: 99%

Fuzzy Automation Controller for Flexible Manufacturing Systems

P.R¹,

Bhat²,

Meenatchisundaram³

2010

Int. Jour. Des. Manf. Tech.

View full text Add to dashboard Cite

The behavior mapping of systems has undergone rapid change with advances in technology. (FAPN) as a modeling tool. It presents a systematic approach to the synthesis of Fuzzy Petri net based supervisor for the forbidden state problem using supervisory design procedure. The controlled model of the system can be constructed from this FAPN net structure. The implementation is using Flexible Manufacturing System (FMS) as an example of DES. The results can be interpreted and applied to high level manufacturing systems, where the role of the supervisor is to coordinate the control of multiple machines or to low level manufacturing systems, where the control function is to switch ON/OFF with respect to the dynamics between different valves.

show abstract

“…Hence all the transitions leading to/from "bad states" are also eliminated. For example, let us consider a case of M 14 = [2, 3,6,7,8,12]. Here all the conveyors are in idle state.…”

Section: Controller Synthesis Using Thementioning

confidence: 99%

Fuzzy Automation Controller for Flexible Manufacturing Systems

P.R¹,

Bhat²,

Meenatchisundaram³

2010

Int. Jour. Des. Manf. Tech.

View full text Add to dashboard Cite

show abstract

“…One of the goals of manufacturing process management is the ability to continuously improve the schedule execution performance, which needs an intermittent adaptation of control strategies to the demands of the current status of executed processes in the shop floor. This ability must be supported by real-time execution monitoring, i.e., an organised way of modelling and measuring processes [187] in order to provide feedback for enabling the above improvement. Performance trend forecasts, based on a regular sampling of statistics, are also considered as essential feedback information for schedule re-optimisation, and, thus, improving the schedule execution stability.…”

Section: Monitoring -A Basis Of Rsjps In Real-time Production Controlmentioning

confidence: 99%

“…The behavioural models characterise the dynamics of the computer controlled process, in the above example: large scale distributed production processes in a steel mill, considered as the interaction of a collection of process variables which evolve dynamically in sets of discrete operating regimes. Behavioural model based monitoring [187] can also be used in manufacturing systenlS with diverse processes where the pattern based monitoring techniques prove to be inappropriate [77] because it would be difficult to establish the patterns due to variations in facilities/processes.…”

Section: Monitoring -A Basis Of Rsjps In Real-time Production Controlmentioning

confidence: 99%

Reactive Scheduling in Real Time Production Control

Szelke

Monostori

1999

Modeling Manufacturing Systems

View full text Add to dashboard Cite

Reactive operation managenlent -Predictive, reactiveand proactive schedulingContinuous, steady improvement of manufacturing operation management from information technology (IT) perspective is a key requirement to manufacturing enterprises competing under the pressure of changing market demands.Manufacturing systems often operate in complex environments rife with uncertainty. The growing complexity of manufacturing operation management lies in the nonexpected tasks/events, nonlinearities, and a multitude of interactions which arise when attempting to control various activities in dynamic shop floors. This complexity and uncertainty seriously limit the effectiveness of conventional control and scheduling approaches. Reactive/proactive systems of shop floor control and scheduling with adaptive behaviour that is ensured by some embedded learning capability and based on real-time monitoring of executed processes, represent the viable alternative.The broad goal of manufacturing operation management, like other resource constrained, multi-agent planning/scheduling problems, is to achieve a co-ordinated efficient behaviour of manufacturing in servicing production demands while responding to changes in shop floors in a timely and cost-effective manner [164]. Operation scheduling, viewed a major issue of manufacturing operation management, has remained a complex co-ordination task despite the recent trends and efforts towards a structural rationalisation in manufacturing (e.g., through the implementation of group technology and JIT strategies). Quality of the operation scheduling process generally has a profound effect on the overall factory performance. Advance generation of the factory schedules is considered central to co-ordinate manufacturing activities in order to meet some organisational objectives, usually the three-pronged objective of producing parts of higher quality at lower cost within a shorter lead time. The generation of advance (predictive) schedules is also demanded for anticipating potential performance obstacles (e.g., resource contention), and for timely assuring physical preconditions (availability of all the needed resources, presetting of processes) to the execution of scheduled manufacturing activities, in order to minimise the disturbing effects on the overall manufacturing system operation.

show abstract

“…In recent years, many authors have investigated the applications of fuzzy set theory or fuzzy logic to Petri net theory. Among them, Valette et al 5 presented a brief description of fuzzy time Petri nets and outlined a procedure for computing fuzzy markings and fuzzy firing dates, in the context of imprecise markings for modeling real-time control and monitoring manufacturing systems. In essence, they have extended the crisp time interval of Merlin's time Petri net to a fuzzy time interval using a class of high-level nets, called Petri nets with objects.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy time semirings and fuzzy-timing colored Petri nets

1999

Int. J. Intell. Syst.

View full text Add to dashboard Cite

Monitoring manufacturing systems by means of Petri nets with imprecise markings

Cited by 68 publications

References 5 publications

Fuzzy Automation Controller for Flexible Manufacturing Systems

Fuzzy Automation Controller for Flexible Manufacturing Systems

Reactive Scheduling in Real Time Production Control

Fuzzy time semirings and fuzzy-timing colored Petri nets

Contact Info

Product

Resources

About