Operation analysis of headend process facility using discrete event simulation

A new method based on discrete event system is proposed for fault diagnosis of a certain kind of circuit, such as a relay control circuit. The original algorithm to obtain a minimal test set is improved. By using the improved algorithm the finest testable partition can be obtained and the complex testability analysis is avoided. The basic model of the proposed method and the general rule for fault diagnosis are described in detail. At first, the relay control circuit is described with corresponding combinational logic circuit model. Then, the fault diagnosis model of the circuit-under-test is constructed based on the given DES model. This method is applied to diagnose a relay control circuit. The results indicate that this method can diagnose this kind of circuit effectively and the new algorithm has a much lower computational complexity than the original algorithm. The fault elements can be localized exactly and this method has high practical value.

This paper is concerned with fault detection and location of discrete event systems (DES) modeled using interpreted Petri nets (IPN). The approach held deals with IPN models in which the marking is partially known. A diagnosis scheme that tracks the DES inputs and outputs is proposed; it allows detecting and locating an unexpected behavior within the DES. The diagnoser handles a monitoring model, expressed also as an IPN, which is computed from the DES model through an induced conservative marking law.

Control theories for discrete event systems modeled as finite state machines have been well developed over the years in addressing various fundamental control issues. However, modeling in finite state machines has long suffered the potential problem of state explosion that renders it unsuitable for many practical applications. In this paper, we propose an approach that appends finite sets of parameters to finite state machines in modeling discrete event systems. We show with an example that many discrete event applications can then be represented efficiently in this approach and the state explosion problem can then been mitigated.

In order to make it possible to effectively represent deterministic uncertainties and vagueness as well as the human subjective observation and judgement inherent to many real-world problems, especially those in biomedicine, we introduce, in this paper, fuzzy states and fuzzy events and generalize (crisp) discrete event systems (DES) to fuzzy DES. The largely graph-based current framework of the crisp DES is unsuitable for the expansion, and we have thus reformulated it using state vectors and event transition matrices which can be extended to fuzzy vectors and matrices by allowing their elements to take values between 0 and 1. To measure information related to fuzzy DES, we generalize the crisp DES observability. The new observability allows one to determine whether or not the system output observed is sufficient for decision making. Finally, we extend the optimal control of DES to fuzzy DES. The new fuzzy DES theory is consistent with the existing theory, both at the conceptual and the computation levels, in that the former contains the latter as a special case when the memberships must be either 0 or 1. Numerical examples are provided to illustrate the theoretical development.

This paper presents a Discrete Event System (DES) model of human operators in a classification task. Two classes of operators, novices and experts, are defined based on the operators' levels of expertise in classification. The operator's behavior is characterized by a task model, which describes how a task is serviced, and a mental model, which predicts the operator's mental arousal (a function of the operator's workload). A supervisory controller ensures that the operator's mental arousal be in a “good” state. An experiment to validate the task model is discussed and a preliminary interface for model validation and analysis is presented.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Movement of entities in discrete event simulation typically requires predefined paths with decision points that dictate entity movement. Human-like travel is difficult to model correctly with these constraints because that is not how people move and large individual differences exist in capabilities and strategies. Agent based modeling is considered a better way to simulate the real-time interaction of people with their environment. In this paper we propose to integrate agent based modeling with discrete event simulation to simulate the movement of people in a discrete event system. An agent based module was constructed within the AutoMod simulation package, and a test case was modeled in which people (agents) at a theme-park interact with objects and people in their environment to get directions and then walk or take a tram to their final destination. We explain the details of model implementation and describe the verification and initial validation of the model.

The paper describes a methodology for creating simulation models of discrete event systems and for executing them on the Web platform. Models defined as extended Petri nets are built following schemas describing their available elements and admissible structures. Simulation portal provides access to Java class libraries of model elements, to XML documents of process schemas and defined models, as well as to several functions concerning model definition, execution of simulation jobs, analysis and visualization of results.

This paper presents an approach to perform Activity Based Costing (ABC) with Petri nets. ABC is a powerful methodology to calculate various of discrete event systems (e.g. production systems). Petri net models of discrete event systems are easy to create, comprehend, and to perform analysis, the mathematics behind Petri nets is also easy to grasp. This paper shows how ABC can be performed with Petri nets, so that various involved in a discrete event system can be easily measured with the help of the Petri net model. After a short introduction to ABC and Petri nets, this paper shows the approach which basically embeds various cost drivers as firing costs and firing times of transitions in a Petri net model. This work also presents a case study that is done with GPenSIM, a toolbox realized with this approach.

Modelling Basic Discrete Event Systems - A Key Factor in Supporting the Representation and Analysis of Complex Production Systems

Many technological, industrial or economical systems are described by discrete event system (DES) models. The decision making processes that arise in the design and operation of this kind of systems can be afforded by means of algorithmic methodologies. A large range of approaches based on the simulation of the behavior of the system have been reported to answer this problem. Their main advantage consists of being applicable to most of the systems. An important drawback is the significant computational resources required to perform an exhaustive exploration of the state space due to the combinatorial explosion. A manual choice of a reduced set of configurations to be simulated can be improved by the use of parametric Petri nets and a metaheuristic search of the most promising ones. In this paper, a review of some definitions of parametric Petri net found in the literature is presented, as well as a definition for the general framework of stating optimization problems of both, the operation and the design of the model of the DES. Moreover, a methodology to obtain such a parametric Petri net, called compound Petri net, from an easier-to-obtain set of alternative Petri nets is proposed and an application example is given.

In this paper a hybrid approach is proposed for supervisory control of discrete event systems (DES) subject to forbidden states. Assuming that an uncontrolled bounded Petri net (PN) model of a (plant) DES and a set of forbidden state specifications are given, the proposed approach computes a maximally permissive and nonblocking closed-loop hybrid model. The first step is to simplify the given PN model by means of PN reduction rules. The simplified model and the specifications are then represented as buffers, and supervisory control theory (SCT) is applied to obtain a Ramadge–Wonham (RW) supervisor in the form of an automaton. After reduction of the latter’s state size by a ‘control congruence’, the simplified RW supervisor is represented by a so-called auto-net and coupled to the given uncontrolled PN plant model by means of inhibitor arcs to represent the disabling actions. The plant model and supervisor auto-net run concurrently, synchronizing on shared events. This procedure provides a maximally permissive and nonblocking ‘hybrid’ (mixed PN/automaton) closed-loop controlled system. The method is straightforward logically, graphically, and technologically. Its applicability is shown by two examples, one of them a workcell from the PN control literature.

Residuals-based fault diagnosis of industrial automation systems using timed and untimed Interpreted Petri nets

Computer simulation is a discipline which intersects operations research, mathematical statistics and computer science, and it dynamic simulates system or process by using computer. The paper studies financial institution queuing problem by using discrete event system modeling and simulation to provide the basis for improving the efficiency and quality of the service. Firstly, using a graph to depict the structure of financial institution queuing system for customers; then, the corresponding stochastic service system model was established based on queuing theory; finally, queued simulation program by using IPO chart has been designed in detail. Contents of this article provide a basis for scientific management decision for financial institution, but the system model of queuing theory is too complex, so the model should be simplified in practice.

Building reduced petri net models of discrete manufacturing systems