scholarly journals A Monitoring Approach Based on Fuzzy Stochastic P-Timed Petri Nets of a Railway Transport Network

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Anis M’hala
Keyword(s):  
Petri Nets ◽  
Stochastic Petri Nets ◽  
Transport Systems ◽  
Railway Transport ◽  
Timed Petri Nets ◽  
Transport Networks ◽  
Railway Network ◽  
Fuzzy Petri Nets ◽  
Traffic Disturbance

This paper proposes a monitoring approach based on stochastic fuzzy Petri nets (SFPNs) for railway transport networks. In railway transport, the time factor is a critical parameter as it includes constraints to avoid overlaps, delays, and collisions between trains. The temporal uncertainties and constraints that may arise on the railway network may degrade the planned schedules and consequently affect the availability of the transportation system. This leads to many problems in the decision and optimization of the railway transport systems. In this context, we propose a new fuzzy stochastic Petri nets for monitoring (SFPNM). The main goal of the proposed supervision approach is to allow an early detection of traffic disturbance to avoid catastrophic scenarios and preserve stability and security of the studied railway networks. Finally, to demonstrate the effectiveness and accuracy of the approach, an application to the case study of the Tunisian railway network is outlined.

scholarly journals Modeling and Robustness Study of Railway Transport Networks Using P-Timed Petri Nets

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Anis Mhalla ◽  
Mohanned Gaied
Keyword(s):  
Petri Nets ◽  
Traffic Management ◽  
Control Strategies ◽  
Discrete Event ◽  
Transport Systems ◽  
Population Mobility ◽  
Railway Transport ◽  
Transport Networks ◽  
Modeling And Analysis ◽  
Increasing Demand

The importance of public transport systems continues to grow. These systems must respond to an increasing demand for population mobility and traffic disturbances. Rail transport networks can be considered as Discrete Event Systems (DES) with time constraints. The time factor is a critical parameter, since it includes dates to be respected in order to avoid overlaps, delays, and collisions between trains. P-time Petri Nets have been recognized as powerful modeling and analysis tools for railway transport systems. Temporal disturbances in these systems include railway infrastructure, traffic management, and disturbances (weather, obstacles on the tracks, malice, social movement, etc.). The developments presented in this paper are devoted to the modeling and the study of the robustness of the railway transport systems in order to evaluate the stability and the efficiency of these networks. In this study two robust control strategies towards time disturbances are presented. The first one consists of compensating the disturbance as soon as it is observed in order to avoid constraints violation. The second one allows generating, by the control, a temporal lag identical to the disturbance in order to avoid the death of marks on the levels of synchronization transitions of the P-time Petri net model.

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

2019 ◽  
Vol 233 (7) ◽  
pp. 830-846
Author(s):  
Mouhaned Gaied ◽  
Anis M’halla ◽  
Dimitri Lefebvre ◽  
Kamel Ben Othmen
Keyword(s):  
Robust Control ◽  
Petri Nets ◽  
Discrete Event Systems ◽  
Optimization Problems ◽  
Discrete Event ◽  
Transport Systems ◽  
Time Constraints ◽  
Railway Transport ◽  
Transport Networks ◽  
Event Systems

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.

scholarly journals Static Integration Based on Stochastic P-Time Petri Nets of Maintenance Scheduling for Railway Transport Equipment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Anis M’halla
Keyword(s):  
Petri Nets ◽  
Maintenance Scheduling ◽  
Transport Systems ◽  
Time Petri Nets ◽  
Railway Transport ◽  
Transport Networks ◽  
Transport Equipment ◽  
Corrective Maintenance ◽  
Railway Systems ◽  
Computing Algorithm

In transport systems, all equipment requires maintenance, which directly affects the machine’s availability and consequently the planned transport schedule. The purpose of this paper is to carry out a method for integrating recovery jobs in railway systems. The proposed method allows the insertion of preventive and corrective maintenance operations when the transport equipment is available in order to minimize periods of inactivity, avoid catastrophic scenarios, and maintain stability and safety of the studied networks. A computing algorithm, allowing insertion of the planned recovery tasks in periods of metro availability, without changing the initial scheduling solution, is established. Finally, we illustrate the implementation of the proposed approach on Tunisian Sahel railway transport networks.

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