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Petri Net Based Modeling and Property Analysis of Distributed Discrete Event System

Petri Net Based Modeling and Property Analysis of Distributed Discrete Event System

A Discrete Event System (DES) is a dynamic system that progresses according to the unexpected occurrence of events at probably unknown asymmetrical intervals of time. We can have such systems arising in different conditions ranging from operating systems of a computer to the control of complex processes. DES has a large impact on research and was created on an interdisciplinary context. It is connected to several areas of mathematics, among which the most consistent contributions were brought by automata and formal languages, queuing systems, Petri net and algebraic theory of synchronization. Other tools for DES used earlier are Automata and formal language models. Petri Net is efficiently used for deadlock prevention [7] in system and for fault identification in DES [9].
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Modeling of the Reactive Navigation of Autonomous Robot using the Discrete Event System Specification DEVS

Modeling of the Reactive Navigation of Autonomous Robot using the Discrete Event System Specification DEVS

Modeling and simulation are essential tools to analyze the behavior of dynamic systems. Several methods have been proposed to improve the process of analyzing the behavior of these systems. These proposals attempt to achieve more realistic models, relatively simple and highly flexible. Our focus is on DEVS (Discrete Event System Specification) that has been widely used to study the dynamics of discrete event systems.

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An Integrated Discrete-Event/System Dynamics Simulation Model of Breast Cancer Screening for Older US Women.

An Integrated Discrete-Event/System Dynamics Simulation Model of Breast Cancer Screening for Older US Women.

SD modeling is suited for health care systems because it has the ability to model complex systems whose boundaries extend into other organizations. In health care, it is very rare that a system of interest involves well-defined boundaries and does not interact with the surrounding environment. This is compounded by the fact that health care has multiple stakeholders who have different perspectives regarding different aspects of system operation. In DES, an overwhelming amount of expert opinion is needed to define model variables and parameters and the relationships between these quantities, because the necessary data is often limited or not available at all. Data requirements for SD models are much less since the view of the model is more aggregated and at a higher level, but the required data are harder to get. Another key advantage of SD models is that they typically run very fast, while on the other hand, execution time is a major concern with of DES models.
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Fuzzy Time Control Modeling Of Discrete Event Systems

Fuzzy Time Control Modeling Of Discrete Event Systems

Abstract—Linguistic modeling of complex irregular systems is helpful for the generation of decision making controls. In the various existing Fuzzy models, proposed by Mamdani, Sugeno, and Tsukamoto, the concepts of the set of membership functions and different Fuzzy logic rules to reason about data were addressed. The time control issues were not discussed in these models. In this paper, a new model is proposed with initial membership functions of the fuzzy model and the linguistic fuzzy rules with time control membership function of the binary valued outputs instead of crisp values. The system is named fuzzy logic time control system (FLTCS) with the proposed timing approach and implemented with discrete event system DEVS.
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Using of DEVS and MAS Tools for Modeling and Simulation of an Industrial Steam Generator

Using of DEVS and MAS Tools for Modeling and Simulation of an Industrial Steam Generator

Complex systems are made of many elementary com- ponents in interaction. To model such systems, it is generally more convenient to decompose them into sub- systems that are simpler to handle. This new division is to be made in a methodical way, by identification and complete definition of the various structures, actions and interactions of those sub-systems. In this work, the decomposition of the overall system into sub-systems is based primarily on the use of the Discrete EVent System Specification (DEVS) formalism. The obtained atomic and coupled models are formally verified and validated. Then, we use the Multi-agent Development KIT ( MAD-KIT ) Multi Agent Systems ( MAS ) opera- tional tools to implement an industrial simulator. This simulator is used by beginner operators in the petroleum field to ameliorate the process of training and learning without stopping the real processes. The advantage of this approach is its adaptability as well as its possibilities of extension ( addition of new functionalities ) . Moreover, the decomposition into sub-systems reduces significantly the complexity of the elements being implemented and therefore, allows a great modularity and a better legibility to the system. Our work is realised in collaboration with the production department of natural gas liquefaction (GL1/K Skikda), one of the principal hydrocarbons poles from SONATRACH complex in Algeria.
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Testability of a swarm robot using a system of systems approach and discrete event simulation

Testability of a swarm robot using a system of systems approach and discrete event simulation

The framework is presented in three conceptual areas: metadata and data require- ments, discrete event system specification, and the interface between real and virtual worlds. All steps of the model continuity method (simulation experiments, real time simulations, and agent-in-the-loop simulations) are supported, although the focus of this work will be on connecting the simulated world to the real system for AIL setups. The overview of this AIL simulation framework is contained in Figure 4.1 and shows the real world and virtual environment. They are connected through the agent model and a communications module. These modules each contain a driver to enable connections through the underlaying operating system and hardware devices. The agent model resides in a virtual environment which contains information about the location of all agents and obstacles and resembles the surroundings of the real system.
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Multi-Dimensional Supervisory Fuzzy Logic Time Control DEV Processing System for Industrial Applications

Multi-Dimensional Supervisory Fuzzy Logic Time Control DEV Processing System for Industrial Applications

Abstract— This research paper presents the design model of a fuzzy logic time control discrete event DEV system under the control of multi-agents based supervisory control in local and distributed environment for industrial application of a processing plant regarding the specific product of certain quality and amount. The designing concepts of fuzzy logic time control discrete event system are summarized in various modes of operation and a new supervisory approach using multi-agents is implemented in local and distributed control modes. This research work will enhance the capability of fuzzy logic time control systems in process automation with potential benefits of multi-dimensional control and supervision.
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Modelling and compilation method for multi-PLC control program

Modelling and compilation method for multi-PLC control program

discrete event system, and analyse the dependencies of variables and instructions of the program, transit serial control programs into parallel discrete events, then assign the variables and instructions to different devices. Recently, discrete event system modelling tools include Automata, timed petri-net, event graph, etc. Since the difficulty of analysis is increasing with the complexity of the system, the automata and timed petri-net are difficult to apply in complicated control systems. Event graph, with simplified expression method and powerful modelling capacity, received a wide application in discrete event system (Xia et al., 2012) and becomes a hot topic right now. Nazari et al. (2012) proposed a determination method of component blocking and network blocking in Fully Connected event graphs. Declerck (2011) analysed external trajectories and token deaths in event graphs. Amari et al. (2012) gave a max-plus control design for temporal constraints meeting in timed event graphs.
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A Proposed Grinding and Mixing System using Fuzzy Time Control Discrete Event Model for Industrial Applications

A Proposed Grinding and Mixing System using Fuzzy Time Control Discrete Event Model for Industrial Applications

Fuzzy logic time control system needs a fuzzifier, inference kernel connected with knowledge base including data base, rule base and output membership functions (for output variables and output time control). In this system as shown in Fig. 2, two defuzzifiers: one for output variable, and another for output time control are used [2].Time control pulsar converts the time crisp value into a pulse of specific time duration. In analog to digital converter (ADC) pulse strobe unit, ADC converts the output crisp value into binary code and pulse strobe part allows the code to pass for the specific pulse duration. This binary code is used to activate the discrete event control system to generate specific event for a certain time. In this way combination of fuzzy logic time control system and discrete event system will form a fuzzy discrete event control system [3], [6].
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An investigation on test driven discrete event simulation

An investigation on test driven discrete event simulation

AnyLogic is a recent and well known simulation toolkit. This toolkit consists of several libraries of objects which covers a wide spectrum of components necessary for designing different kinds of simulation paradigms (system dynamics, discrete event, and agent based). In AnyLogic, a model can be represented graphically while some Java code snippet can be added to implement the objects, component interactions and dynamics of the system. Though the implementation and modelling are not totally separate as it is the case with the Repast toolkit, the Anylogic toolkit is equipped with debugging and tracing utilities. An API library is also provided for users who wish to take a more advanced programming approach. Essentially, what the toolkit does is to assist the modeller to write the minimum amount of Java code. Based on these observations, and due to the fact that Anylogic follows an object oriented approach, one can easily conclude that TDD is feasible within the Anylogic framework. However, this claim is yet to be validated.
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independent agents, each one with their own objectives, and are generally capable to interact with each other and with their environment. Therefore, interactions established between the individual agents and the environment are also modelled. Each agent has the capacity to evolve over the time and adapt to new environmental conditions or objectives. One of the fundamental points of agent-based simulation is the concept of emergence. The agents’ behaviour is modelled at the individual level, and the global behaviour emerges as a result of the interactions with many individuals, each one following its own behaviours and rules. Neither the expert nor the modeller imposes conditions on the overall behaviour of the system directly, due to it emerges as a result of the conditions imposed on the basic system components and their interactions. That is why ABS modelling is also called bottom-up modelling, corresponding to the macroscopic patterns that emerge from the decentralised interactions of simpler individual components Holland (1998). This bottom-up approach allows capturing the complexity and dynamicity of the modelled system. In ABS interactions between the basic components of the system are studied, therefore the system can be modelled even in the absence of the knowledge about the global interdependencies Izquierdo et al., 2008)).
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Exact on-event expressions for discrete potential systems

Exact on-event expressions for discrete potential systems

these events are just the collisions between the spheres. The static and dynamical properties of the system are completely specified by the rate of these events and their characteristics 共e.g., the transfer of momentum, kinetic energy, etc.兲. For example, the pressure of a hard sphere system is directly related to the collision rate. 60 In this work, we examine the collision and velocity statistics of systems composed of stepped potential systems, which include the square-well and square-shoulder potentials. Specifically, we study the veloc- ity distributions of atoms when they are undergoing an event,
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Accounting for Input Uncertainty in Discrete-Event Simulation

Accounting for Input Uncertainty in Discrete-Event Simulation

their simulation packages. Several flexible distributions are proposed in the simula- tion literature with more flexible distributional shapes. Schmeiser and Deutsch (1977) proposeda four parameter family of probability distributions suitable for simulation. Swain et al. (1988) later developeda software package calledFITTR1 to fit Johnson’s translation system (Johnson, 1949). However, these distributions are inadequate for data sets having anomalies as simple as being bimodal with tails. Later Avramidis andWilson (1994) developedthe IDPF procedure to extendthe parameterization of a standard distribution family by minimizing the sum of square errors between the inverse distribution function of this family and its inverse distribution function with a polynomial filter. They developed the IDPF software to improve the fit on the four parameter Johnson family. However, their methoddoes not not always yieldan acceptable fit to the data, and it does not always greatly improve the fit obtained from the Johnson family.
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User interfaces and discrete event simulation models

User interfaces and discrete event simulation models

Any user interaction that has windows, buttons, boxes, icons, and so on, is commonly called a graphical user interface, or GUI. They have also been called “WIMP” (windows, icons, menus, and pointers) and “NERD” (navigation, evaluation, refinement, and demonstration interfaces). All the interaction styles that we have covered so far, with the exception of typed-command languages, could be classified as GUIs. The trend in current computer applications is toward asynchronous, multi-threaded dialogue (event-based dialogue) where many tasks (threads) are available to the end-user at one time, and the sequencing of each thread is independent of the others (Hartson and Hix, 1989). This trend is one of the most significant phenomena in the field today and is exemplified by the Apple Macintosh personal computer and the success of Microsoft Windows and Windows applications. This trend is followed by many simulation systems as well. The trend in using graphical user interfaces is definitely upwards. A survey conducted and reported on by a firm of independent research analysts in the U.S. claims that productivity of GUI users does increase (Wright, 1991). Klinger (1991) argues that visual interfaces upgrade a human’s ability to deal with computer data, but argues that information access is less free due to the complexity inherent in dealing with computer interfaces and that the gain in facility or learning they offer occurs at a significant increase in user interaction time. At the same time, developers’ tools are following this trend as well. More and more tools use visual languages for human- computer interaction. The benefits of such systems are widely agreed (Shneiderman, 1983; Tanimoto and Glinert, 1986; Potosnak, 1988).
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Time Decomposition for Diagnosis of Discrete Event Systems

Time Decomposition for Diagnosis of Discrete Event Systems

The original diagnosability verification algorithm proposed by Sampath et al. [1995] relies on the construction of a diagnoser. The computational complexity of such a construction requires exponential time w.r.t. the number of system states [Jiang et al., 2001; Yoo and Lafortune, 2002]. Jiang et al. [2001] proposed diagnosability testing of DES that avoids the construction of a diagnoser for the system, and runs in polynomial time. This approach is known as the twin plant method. It uses a model and a copy of the model, called the bad twin and the good twin. A bad twin is allowed to contain faulty behaviours while a good twin can only include nominal behaviours that will produce the same observations as faulty behaviours. Then, the twins are synchronised to generate a twin plant. A path is defined as ambiguous if the path contains a fault, and the path is valid in both bad twin and good twin. Finally, a model is diagnosable if there is no loop on any ambiguous path. Otherwise, it is not diagnosable. This is because if there is an ambiguous path that is infinite, then it will not be possible to distinguish between the nominal and faulty behaviours. In other words, a system is diagnosable if there is no two infinite paths in the model that consists of the same observations such that one contains a fault while the other one does not.
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Distributed object-oriented discrete event simulation

Distributed object-oriented discrete event simulation

extension to Common Lisp; eliminates any special syntax for message passing; object-oriented methods invoked with same system as Common Lisp function reference Pascal Plus: Simula doesn'[r]

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Discrete Event Net Based Modeling and Control System Design for Real Time Concurrent Control of Multiple Robot Systems

Discrete Event Net Based Modeling and Control System Design for Real Time Concurrent Control of Multiple Robot Systems

Recently, based on the rapid development of the micro- processor technology, the factory automation systems have been continuing to become more and more large- scaled, complicated, and integrated. The two flows of control and data must be organized in the system, but to achieve it, system concept, system architecture, and sys- tem design method are not established sufficiently. Some techniques derived from Petri nets have been success- fully introduced as an effective tool for representing con- trol specifications including concurrent processes, which are characteristic of complex systems, analyzing system properties and designing control systems [1]. Especially, Petri nets are used to indicate the flow of control, and by decomposing the net, the concept of upper and lower control levels is clarified. The coordinator is defined as the agent which consistently executes the flow of control in the upper control level bringing about cooperation among the local machine controllers in the lower control level. The microprocessors seem to be the most suitable to realize the coordinator. They can be used to realize the coordinators corresponding to decomposed nets, besides the coordinator in the upper control level, using software directly on the same hardware.
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Discrete measurement, continuous time and event history modeling

Discrete measurement, continuous time and event history modeling

Discrete measurement introduces bias to duration models when the discrete measured variable is treated as the exact continuous time value. Limited measurements indicate intervals within which a continuous time variable lies. Discrete measurement leads to an interval-censored variable. Statistical techniques for optimal parameter estimation under conditions of general interval-censoring are already well developed for both the Cox proportional hazard model and parametric estimators. In this study I demonstrate how duration models can be improved by analyzing the discrete measured variable as a systematically interval-censored variable. Through simulations, the superiority of the interval-censored to the midpoint-imputed estimator, in terms of both root mean-squared-error and bias, is established. Replications using an important study on the duration of civil war (Fearon 2004) demonstrate that the use of midpoint imputation can bias results in the direction of a type I inference error.
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DISCRETE-EVENT SIMULATION OF PRODUCTION AND SALES PROCESSES IN A COMPANY 

DISCRETE-EVENT SIMULATION OF PRODUCTION AND SALES PROCESSES IN A COMPANY 

Understanding of complex problems, their roots and effects on performance and ready deliveries requires “what-if” scenarios that can be supported by simulation techniques (Robinson, 1994). It is important to synchronize business processes and align corresponding strategies of marketing, production, sales and logistics. First of all, managers have to consider company’s production and sales processes from push/pull views of logistics and Customer Order Decoupling Point, CODP, and types of production system (van der Vorst, Beulens and van Beek, 2005). Also they need to choose or modify inventory policies, e.g. ( R, Q ), ( s, Q ), (
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Modeling of a Closed-Loop Maritime Transportation System with Discrete Event Simulation and Multi-Criteria Decision Analysis

Modeling of a Closed-Loop Maritime Transportation System with Discrete Event Simulation and Multi-Criteria Decision Analysis

Abstract - The present paper approaches the development and application of a Discrete Event Simulation (DES) model with a Multiple Criteria Decision Analysis (MCDA) tool for the simulation and sizing of a closed-loop maritime transportation system. This system is responsible for the supply of raw materials, especially iron ore, to a steel plant, and its sizing involves the analysis of the transportation fleet and of the storage area for the inputs to the steel making process. This work characterizes the problem, shows the methodology employed and highlights the main results achieved by the simulation. Concomitantly, the choice of the best solution between the simulation results is made based in the application of the MCDA methodology. The main conclusion of the study is that the use of DES combined with MCDA is an efficient way to help decision-making on complex logistics transportations systems.
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