Application des BDSPNs à la modélisation et à l'évaluation de performances des chaînes logistiques

Karim, Labadi; Chen, Haoxun; Amodeo, Lionel

doi:10.3166/jesa.39.863-886

Cited by 13 publications

(14 citation statements)

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“…Petri nets (PN) and Colored Petri Nets (CPN) have been used as a simulation tools for such systems. 2–5 The same formalism served as a support for a comparative analysis of the performances of the BAL (automatic light block)/KVB (speed control by beacons) systems and ETCS on a European freight corridor project. 6 Numerous other works use PN to model and analyze railway systems.…”

Section: State Of the Artmentioning

confidence: 99%

Robust control for railway transport networks based on stochastic P-timed Petri net models

Gaied

Mhalla

Lefebvre

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article is devoted to the modeling, performance evaluation and robust control of the railway transport network in Sahel Tunisia. The regular increase in the number of passengers makes the management of transportation systems more and more complex. Railway transport requires specific needs. Indeed, many decision and optimization problems occur from the planning phase to the implementation phase. Railway transport networks can be considered as discrete event systems with time constraints. The time factor is a critical parameter, since it includes schedules to be respected in order to avoid overlaps, delays and collisions between trains. The uncertainties affect the service and the availability of transportation resources and, consequently, the transport scheduling plan. Petri nets have been recognized as powerful modeling and analysis tools for discrete event systems with time constraints. Consequently, they are suitable for railway transport systems. In this article, stochastic P-time Petri nets are used for the railway transport networks in Sahel Tunisia. A global model is first detailed. Then, this model is used to analyze the network traffic and evaluate the performance of the system. Robustness again disturbances is introduced and a control strategy is developed to reduce the consequences of the disturbances in order to maintain the expected schedule.

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Section: State Of the Artmentioning

confidence: 99%

Robust control for railway transport networks based on stochastic P-timed Petri net models

Gaied

Mhalla

Lefebvre

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The performance of the model is evaluated and a methodology for approximately determining the optimal rational fractions is provided. Labadi et al [30]- [31]- [33] use Batch Deterministic and Stochastic Petri nets to model various inventory control policies such as order-up-to level policy, batch ordering policy, and (s, S) policy. The performances of these systems are evaluated analytically for the case with exponential lead time.…”

Section: Petri Net Based Approaches For Logistics Systemsmentioning

confidence: 99%

Modeling, Analysis, and Optimization of Logistics Systems Petri Net Based Approaches

Chen

Labadi

Amodeo

2006

2006 International Conference on Service Systems and Service Management

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Driven by fierce global competition, effective supply chain logistics management has become one major concern of many enterprises. This has created needs for modelling, performance analysis and optimization tools that can help the enterprises to optimize their logistics networks and operations. In this paper, after demonstrating the advantages of Petri nets as modelling and analysis tools of logistics systems, an overview of the recent development on Petri net based approaches for modeling, performance analysis, and optimization of supply chains is given and future research opportunities in this area are explored.

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“…BDSPN model is developed from deterministic and stochastic Petri nets (Marsan, et al 1987;Lindemann, 1998) by introducing batch components (batch places, batch tokens, and batch transitions) and new transition enabling and firing rules. Firstly, we recall the basic definition and the dynamical behavior of the model (Labadi, et al 2005(Labadi, et al , 2007Chen, et al 2005).…”

Section: Description Of the Modelmentioning

confidence: 99%

“…Case 2. The BDSPN is not transformable: The modelling of some discrete event systems such as inventory control systems and logistical systems, as shown in (Labadi, et al, 2005(Labadi, et al, , 2007Chen, et al 2005), require the use of the BDSPN model with variables arc weights depending on its M-marking and possibly on some decision parameters of the systems. It is the case of the BDSPN model of an inventory control system whose inventory replenishment decision is based on the inventory position of the stock considered and the reorder and order-up-to-level parameters (see Fig.…”

Section: Case 1 the Bdspn Model Is Transformablementioning

confidence: 99%

“…As we know, industrial systems are often characterized as batch processes where materials are processed in batches and many operations are usually performed in batch modes to take advantages of the economies of scale or because of the batch nature of customer orders. It is shown in our previous papers that the model is a powerful tool for both analysis and simulation of those systems and its capability to meet real needs was demonstrated through applications to logistical systems (Labadi, et al 2005(Labadi, et al , 2007Chen, et al 2005). The objective of this paper is to study the transformation of a BDSPN model into an equivalent classical Petri net model.…”

Section: Introductionmentioning

confidence: 99%

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Transformation Analysis Methods for the BDSPN Model

Labadi¹,

Chen²,

Yalaoui³

2007

Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics

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The work of this paper contributes to the structural analysis of batch deterministic and stochastic Petri nets (BDSPNs). The BDSPN model is a class of Petri nets introduced for the modelling, analysis and performance evaluation of discrete event systems with batch behaviours. The model is particularly suitable for the modelling of flow evolution in discrete quantities (batches of variable sizes) in a system with activities performed in batch modes. In this paper, transformation procedures for some subclasses of BDSPN are developed and the necessity of the introduction of the new model is demonstrated.

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Application des BDSPNs à la modélisation et à l'évaluation de performances des chaînes logistiques

Cited by 13 publications

References 0 publications

Robust control for railway transport networks based on stochastic P-timed Petri net models

Robust control for railway transport networks based on stochastic P-timed Petri net models

Modeling, Analysis, and Optimization of Logistics Systems Petri Net Based Approaches

Transformation Analysis Methods for the BDSPN Model

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