Modelling with coloured timed Petri nets and simulation of a dynamic and distributed management system for a manufacturing cell

Tchako, J. F. N.; Beldjilali, Bouziane; Trentesaux, Damien; Tahón, Christian

doi:10.1080/09511929408944621

Cited by 15 publications

(4 citation statements)

References 9 publications

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“…design specifications -the pioneering work of Decker (1987) is recalled, who distinguishes two main dimensions of classification for Distributed Problem Solving, that are control and communication: the former relates to the cooperation degree among agents, the coordination among cooperating agents and the dynamics for reaching coordination; the latter relates to communication paradigm, semantic content and protocol. Later, Tchako (1994) extends the Decker's classification by adding the agent dimension, which describes the characteristics of the agents populating the MAS, e.g. adaptivity and autonomy.…”

Section: Definitions and Categorizations Of Multi-agent Systemsmentioning

confidence: 99%

Multi-agent systems in production planning and control: an overview

Caridi

Cavalieri

2004

Production Planning & Control

143

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The ever fast changes of customers’ needs and demands ask for reconfigurable and adaptive production systems, which can provide companies with the proper level of agility and effectiveness, without disregarding at the same time cost factors. In the last decade, a large amount of research works on the adoption of multi-agent systems (MAS) in several industrial environments has flourished. This approach, unlike traditional centralized or multilevel hierarchical approaches, assumes the presence of several decision-making entities, distributed inside the manufacturing system, interacting and cooperating each other in order to achieve optimal global performance. Aim of this paper is at first to provide readers, which are not experienced with the multi-agent approach, with some definitions and categorizations of this paradigm. Secondarily, by making use of an extensive database of more than 100 contributions on this field, authors intend to evaluate how multi-agents systems have really impacted on the industrial practices at an enterprise and at a broader supply chain level. Finally, driven by the past research experiences of the authors and by the extensive literature search, considerations and remarks on the real potential benefits and on the major issues currently inhibiting the spread out of this paradigm are reported

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Section: Definitions and Categorizations Of Multi-agent Systemsmentioning

confidence: 99%

Multi-agent systems in production planning and control: an overview

Caridi

Cavalieri

2004

Production Planning & Control

143

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“…They might also be taken into account for configuring, analysing, and evaluating different solution alternatives in case of RS problems, conceptualised as stochastic control problems and modelled as stochastic Petri nets, e.g., at [72]. Some of the extended models have been used for communication protocols verification in DAS [182]. For experimenting with generic FMS real-time control and scheduling policies embedded in a rule based (Prolog) expert system, [28] use coloured Petri net as a powerful tool for modelling and analysing the discrete event-driven system of a generic operating FMS.…”

Section: Petri Netsmentioning

confidence: 99%

Reactive Scheduling in Real Time Production Control

Szelke

Monostori

1999

Modeling Manufacturing Systems

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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.

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“…However, as the CPN definition is very concise, specific processes have to be modeled with appropriate modules that sometimes are not easy to conceive. Many specific processes have been modeled with CPNs [e.g., [4][5][6][7][8], but ordering and selecting criteria, which are very common in discrete event simulation, have been scarcely explored in the CPN literature. This literature includes only a few models of ordering and none of complex selecting criteria.…”

Section: Introductionmentioning

confidence: 99%

Colored Petri Net Modules of Complex Selecting Criteria

Arjona¹,

Bueno

2003

SIMULATION

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Colored Petri nets (CPNs) have been widely used as a mathematical modeling tool of dynamical systems, both for control and simulation. CPNs are very appealing and appear uncomplicated because of their simple graphical representation. However, as the CPN definition is very concise, specific processes have to be modeled with appropriate modules that sometimes are not easy to conceive. To broaden the use of CPNs in discrete event simulation, the authors present two CPN modules that model complex selecting criteria. The first module selects a constant number of entities from a set of places. The second module selects a variable number of entities from a set of places. The modules simulate the execution of the criteria, avoiding the use of superfluous components. These CPN modules were developed for automatic top-down refinement of CPN models but can also be used as templates for manual refinement of CPNs.

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Modelling with coloured timed Petri nets and simulation of a dynamic and distributed management system for a manufacturing cell

Cited by 15 publications

References 9 publications

Multi-agent systems in production planning and control: an overview

Multi-agent systems in production planning and control: an overview

Reactive Scheduling in Real Time Production Control

Colored Petri Net Modules of Complex Selecting Criteria

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