Discrete Event Systems Research Articles

Modeling and Simulation of Unmanned Swarm System Based on Dynamic Structure DEVS

As a complex system with collaborative cooperation and capability emergence, the unmanned swarm system plays an important role in military, agriculture, aerospace and other fields. With the increasing complexity of unmanned swarm systems, traditional modeling methods cannot effectively describe the dynamics of swarm systems. In order to effectively design, use and evaluate unmanned swarm systems, this article conducts modeling and simulation of unmanned swarm systems based on DSDEVS(Dynamic Structure Discrete Event System Specification). Describe the unmanned swarm system from both behavioral and structural aspects. Based on component modeling, the microscopic behaviors of the unmanned platforms that constitute the unmanned swarm are described, and coupling relationships are established to describe the collaborative structure between unmanned platforms. According to the state transfer and event interaction characteristics of the unmanned swarm system, a synchronization mechanism is added to support changes in the swarm structure. This enables the unmanned swarm system to make adaptive structural adjustments according to environmental changes and its own goals during simulation operation, so as to achieve a state of enhanced capabilities and structural stability to complete the task. Finally, the effectiveness and feasibility of this method were verified through a simulation experiment of joint reconnaissance by a UAV swarm.

State-Based Opacity Verification of Networked Discrete Event Systems Using Labeled Petri Nets

State-Based Opacity Verification of Networked Discrete Event Systems Using Labeled Petri Nets

Program Evaluation and Review Technique (PERT) Analysis to Predict Completion Time and Project Risk Using Discrete Event System Simulation Method

The prediction of project completion time, which is important in project management, is only based on an estimate of three numbers, namely the fastest, slowest, and presumably time. The common practice of applying normal distribution through Monte Carlo simulation in Program Evaluation and Review Technique (PERT) research often fails to accurately represent project activity durations, leading to potentially biased project completion prediction. Based on these problems, a different method is proposed, namely, Discrete Event Simulation (DES). The research aims to evaluate the effectiveness of the simmer package in R in conducting PERT analysis. Specifically, there are three objectives in the research: 1) develop a simulation model to predict how long a project will take and find the critical path, 2) create an R script to simulate discrete events on a PERT network, and 3) explore the simulation output using the simmer package in the form of summary statistics and estimation of project risk. Then, a library research with a descriptive and exploratory method is used for data collection. The hypothetical network is used to obtain the numerical results, which provide the predicted value of the project completion, the critical path, and the risk level. Simulation, including 100 replications, results in a predicted project completion time and a standard deviation of 20.7 and 2.2 weeks, respectively. The DES method has been proven highly effective in predicting the completion time of a project described by the PERT network. In addition, it offers increased flexibility.

Cloud Migration Strategies: Ensuring Seamless Integration and Scalability in Dynamic Business Environments

The migration of a company's information systems to a cloud platform is a crucial part of the transformation of IT infrastructure. However, due to the mixture of business goals and limitations in existing IT systems, the selection and realization of these goals is not a simple one. In this paper, we present a model describing the possibility of migrating business information systems into a cloud environment by following the stochastic model approach. By properly identifying and defining various process aspects, it is relatively simple to adapt analysis and limit the transition probability matrix to well-known stochastic models. Careful manipulation of such a matrix allows using standard formulas for scaling discrete event systems to solve cloud migration problems. A detailed case study with simulation results, pointing to some of the practical limitations of the model, is presented at the end of this paper.

Control of Discrete Event Systems by Using Symbolic Transition Model: An Application to Power Grids

AbstractIn this paper, a new symbolic modeling framework is proposed for the control of discrete event systems (discrete controller synthesis). Reactive infinite-state systems are generally dynamic systems with inputs and outputs. Modeling the outputs in existing symbolic modeling methods for reactive infinite-state systems has not been extensively discussed in the literature. It is crucial to realistically model the outputs and obtain reliable controllers. One of the main contributions and innovations in this study is the ability to symbolically model their output. In this direction, symbolic transition models are offered, where events are defined as guarded symbolic transitions. The framework considers input/output reactive infinite-state systems to satisfy safety and optimization objectives. Effective algorithms and safety control policies are offered to solve the safety control problem (i.e., satisfy the safety objective) for input/output infinite-state systems. Another contribution in this work provides optimization targets within a given cost function based on state transitions, without specifying the target states. The framework is then applied to power grids to demonstrate the effectiveness of the symbolic transition models and experimentally evaluate the new control algorithms. The results showed that the approach is very effective compared to other approaches found in the literature. The computation times required to achieve both safety and optimization objectives are obtained relatively quickly compared to other deterministic methods found in the literature.

Synthesis of resilient fallback control system under cyber-attacks via supervisory control

Industrial control systems (ICS) require system design and operation under cyber-attacks. This study aims to design a fallback control system that can switch from normal control to fallback control and verify its superiority. The target system is a factory automation (FA) system consisting of a normal programmable logic controller (PLC) and a fallback PLC. In this system, we design a fallback logic that takes over control in the case of cyber-attacks. The design of this logic requires a system model that manages smooth state transitions between normal control and fallback control in an integrated manner under cyber-attacks. In response, we model a control program in the framework of discrete event systems (DESs) and apply supervisory control to derive a supervisor model that can manage the system in an integrated manner. To ensure the controllability of the FA system during cyber-attacks, we design a control specification that includes a detection function to enable rapid switching of the control state. As a result, we generate the fallback logic from the supervisor model with guaranteed controllability under cyber-attacks and implement it in a fallback PLC to verify the effectiveness of the proposed logic.

A uniform approach to compare architectures in decentralized discrete-event systems

Solutions to decentralized discrete-event systems problems are characterized by the way local decisions are fused to yield a global decision. A fusion rule is colloquially called an architecture. Current approaches do not provide a direct way to compare existing architectures. Determining whether an architecture is more permissive than another architecture had relied on producing examples ad hoc and on individual inspiration that puts the conditions for solvability in each architecture into some form that admits comparison. In response to these research efforts, a method based on morphisms between graphs has been extracted to yield a uniform approach to compare the permissiveness of the architectures.

Modeler in a box: how can large language models aid in the simulation modeling process?

We examine the potential of prompting a large language model-based chatbot, ChatGPT, to generate functional simulation model code from a prose-based narrative. The simple narrative describes how the mode of transportation for elementary school students changed due to the COVID-19 pandemic and related factors, including a lack of available bus drivers, lack of mask enforcement on buses, and inclement weather. We document the process of providing this narrative to ChatGPT and further prompting the chatbot to generate model code to represent the narrative and to make it executable. We test ChatGPT’s ability to use prose descriptions of a phenomenon to generate simulation models using three paradigms: discrete event system, system dynamics, and agent-based modeling. Our findings reveal that ChatGPT could not produce concise or executable models, facing higher difficulty when asked to do so in programming languages it was less familiar with. This analysis underscores the strengths and limitations of the current state of this technology for modeling and simulation. Furthermore, we propose how future advancements in Large Language Models may aid the simulation modeling process, increasing transparency and participation in multidisciplinary team efforts.

Information control in networked discrete event systems

How to control information exchange among different users is an important problem in networked systems with many users/agents. Generally speaking, there are several considerations in control of information exchange in a networked system, including (1) to ensure a friend user has sufficient information to perform its tasks, (2) to deprive an adversary user its information to perform its tasks, (3) to minimize information exchange among friend users so that the risk of information leaking is minimized, and (4) to maximize information broadcasted to all users to achieve maximum transparency. In this paper, we investigate the information control problems in the framework of discrete event systems. Based on the problem at hand, we divide users in a networked system into two or more groups. Users in the same group are consider as friends and users in a different group are consider as adversaries. Several information control problems are investigated and solved using a systematic and rigorous approach. Methods are developed to design controllers that send minimum information to its friends to help them to perform their tasks and broadcast maximum information without helping its adversaries.

Petri net based diagnostic approach for concurrent discrete event systems

Abstract This paper introduces a new diagnostic approach for concurrent discrete event systems (DES) in automated production plants. The proposed diagnostic procedure is based on Petri net models of the DES. Discrepancies between the Petri net models and event sequences, which are observed during the operation of the production plant, are detected as potential faults. A case-based diagnosis is then carried out to identify fault types that are easy to interpret for humans. The proposed diagnostic procedure is evaluated using an industrial conveying system.

Building Models in Pairs for Cross‐Verification Using SDL and DEVS

AbstractThe objective of the paper is to present a methodology that can be used to translate a model from one formalism to another allowing model reuse and cross‐verification. With the use of formal languages, the model specifications can be expressed in a rigorous and univocal way, and the model can be validated against the specifications or conceptual model. Expressing the same model in different formal languages opens the conceptual model validation to a varied number of specialists. Also, in the context of modeling a new system, where there is no data to perform validation against a real system, having pairs of models can be used to perform cross‐model verification to ensure that the model specifications or conceptual models are correctly implemented by comparing the results produced by both models. Specifically, a method is presented to translate a model conceptualized on specification and description language (SDL) to discrete event system specification (DEVS). The transformation mechanism between SDL and DEVS formalisms is described. The methodology is exemplified with a disease spread model for COVID‐19, and it is shown how the results obtained by the two models can be used for cross‐verification.

A Learning-Based Approach for Diagnosis and Diagnosability of Unknown Discrete Event Systems.

This article develops a novel active-learning technique for fault diagnosis of an initially unknown finite-state discrete event system (DES). The proposed method constructs a diagnosis tool (termed diagnoser), which is able to detect and identify occurred faults by tracking the observable behaviors of the system under diagnosis. The proposed algorithm utilizes an active-learning mechanism to incrementally collect the information about the system to construct the diagnoser. This is achieved by completing a series of observation tables in a systematic way, resulting in the construction of the diagnoser. It is proven that the proposed algorithm terminates after a finite number of iterations and returns a correctly conjectured diagnoser. The developed diagnoser is a deterministic finite-state automaton. Furthermore, we have proven that the developed diagnoser consists of a minimum number of states. A sufficient condition for diagnosability of the system under diagnosis is derived, which guarantees the diagnosis of faults within a bounded number of observations. The developed method is applied to two case-studies, illustrating the steps of the proposed algorithm and its capability of diagnosing multiple faults.

On the verification of detectability for timed discrete event systems

In this paper, we investigate the problem of state estimation and detection in the context of timed discrete-event systems. Specifically, we study the verification of detectability, a fundamental state estimation property for dynamic systems. Existing works on this topic mainly focus on untimed DESs. In some applications, however, real-time information is critical for the purpose of system analysis. To this end, in this paper, we investigate the verification of detectability for timed DESs modeled by partially-observed timed automata. Three notions of detectability, strong detectability, weak detectability and delayed detectability, are studied in a dense-time setting, which characterizes detectability by time elapsing rather than event updating steps. We show that verifying strong detectability and delayed detectability for partially-observed timed automata is decidable by providing verifiable necessary and sufficient conditions. Furthermore, we show that weak detectability is undecidable in the timed setting by reducing the language universality problem for timed automata to the verification problem of weak detectability. Our results extend the detectability analysis of DESs from the untimed setting to a timed setting.

On the application of the calculus of positively constructed formulas for the study of controlled discrete-event systems

The article is devoted to the development of an approach to solving the main problems of the theory of supervisory control of logical discrete-event systems (DES), based on their representation in the form of positively constructed formulas (PCF). We consider logical DESs in automata form, understood as generators of some regular languages. The PCF language is a complete first-order language, the formulas of which have a regular structure of alternating type quantifiers and do not contain a negation operator in the syntax. It was previously proven that any formula of the classical first-order predicate calculus can be represented as a PCF. PCFs have a visual tree representation and a natural question-and-answer procedure for searching for an inference using a single inference rule. It is shown how the PCF calculus, developed in the 1990s to solve some problems of control of dynamic systems, makes it possible to solve basic problems of the theory of supervisory control, such as checking the criteria for the existence of supervisory control, automatically modifying restrictions on the behavior of the controlled system, and implementing a supervisor. Due to some features of the PCF calculus, it is possible to use a non-monotonic inference. It is demonstrated how the presented PCF-based method allows for additional event processing during inference. The Bootfrost software system, or the so-called prover, designed to refute the obtained PCFs is also presented, and the features of its implementation are briefly described. As an illustrative example, we consider the problem of controlling an autonomous mobile robot.

Input-Output Analysis on Pia Saronde Production Process Scheduling with Invariant Max-Plus Linear System

Max-plus algebra is one of the analysis methods of discrete event systems which has many applications on systems theory and graph theory. Max-plus algebra is a set of real numbers R combined with =-∞ equipped with operations max (⊕) and plus (⊗), can be denoted [(R]_ε,⊕,⊗) with [(R]_ε=R⋃{ε}) . The production process of pia saronde is one of the problems that can be analyzed using max-plus algebra. The production process of this product is sequentially carried out by making skin dough, filling, baking, cooling, and packaging the pia. The max-plus algebra theory was used in this research to determine the optimal time in the production scheduling of pia saronde. Meanwhile, the Invarian Max-plus Linear System (IMLS), max-plus algebraic theory, and the Discrete Event System (DES) were used to solve the production-related problems. IMLS analysis produces eigenvalues that represent the optimum production time. The results obtained the max-plus algebra model of x(k+1)=A x(k), where A =A⊕B⊗C and y=K⊗x_0⊕H⊗u for input-output IMLS analysis. From the matrix A, eigenvalue λ= 226 and eigenvector v=[278 278 278 279 299 302 324 356 488] were obtained. Furthermore, the value of λ describes the pia production schedule at a time span of 226 minutes.
 Keywords: input-output analysis, pia saronde, scheduling, max-plus linear system

Mapping RDEVSNL-based Definitions of Constrained Network Models to Routed DEVS Simulation Models

The Routed DEVS (RDEVS) formalism has been introduced recently to provide a reasonable formalization for the simulation of routing processes over Discrete Event System Specification (DEVS) models. Due to its novelty, new software tools are required to improve the Modeling and Simulation (MS) tasks related to the RDEVS formalism. This paper presents the mapping between constrained network models obtained from textual specifications of routing processes and RDEVS simulation models implemented in Java. RDEVSNL context-free grammar (previously defined) is used to support the textual specification of a routing process as a constrained network model. Such grammar is based on a metamodel that defines the syntactical elements. This metamodel is used in this paper as a middleware that allows mapping constrained network model concepts with RDEVS simulation models. From such a constrained network model template, RDEVS Java implementations are obtained. The proposal is part of a work-in-progress intended to develop MS software tools for the RDEVS formalism using well-known abstractions to get the computational models through conceptual mapping. Using these tools, modelers can specify simulation models without needing to codify any routing implementation. The main benefits are i) reduction of implementation times and ii) satisfactory simulation model correctness regarding the RDEVS formalism.

Optimal supervisory control of discrete event systems for cyclic tasks

In this paper, we investigate the problem of optimal supervisory control for cyclic tasks in the context of discrete-event systems (DES). We consider the completion of each single task as the visit of a marked state, and overall control objective is to complete tasks cyclically in the sense that marked states are visited infinitely often. Following the standard optimal supervisory control framework, two types of costs, disable cost and occurrence cost, are considered. However, instead of considering the standard accumulated total cost or the average cost per event, we consider the measure for the control performance using the average cost per task. We show that such an optimality measure is more suitable for tasks that need to be completed cyclically. Our goal is to design a live and non-blocking supervisor such that the average cost per task in the worst-case is minimized. To solve the problem, we propose a game-theoretical approach by converting the optimal control problem as a two-player graph game. Structural properties of the converted game are discussed. In particular, we show that this game can be solved by a set of mean payoff decision problems, for which effective algorithms exist. Our problem can be considered as a special instance of the general ratio-game in the literature. However, by exploring new structural property for this problem, we achieve superior computational efficiency when compared to the conventional solution designed for more general problem formulations. Illustrative examples are provided to demonstrate the proposed algorithm.

Discrete-event simulation of continuous-time systems: evolution and state of the art of quantized state system methods

In this work, we attempt to bring together the origins, main results, and recent advances on discrete-event simulation of continuous-time systems. Starting from the early approaches that aimed to represent continuous-time dynamics within the discrete-event system specification (DEVS) formalism framework, the work shows how these ideas gave place to the formalization of the quantized state system (QSS) family of numerical integration algorithms. Then, we describe the QSS algorithms, their properties, their extensions, and the main practical software tools implementing them. We also present a selection of simulation examples illustrating the main features and advantages through comparisons with state-of-the-art continuous-time simulation solvers.

A knowledge interchange broker composition modeling framework for simulating water, energy, and water-energy nexus systems

Understanding the dynamics of complex systems requires developing and combining different kinds of models that can be simulated separately and together. Modeling the interactions as separate models contributes to building flexible hybrid simulation frameworks. In this research, a Discrete Event System Specification–based Interaction Model (DEVS-IM) framework is developed based on the Knowledge Interchange Broker (KIB) approach. This KIB-based RESTful modeling composition framework is shown to enable systematic modeling and simulation of interactions between disparate simulatable models. It supports storing IMs developed for componentized Water Evaluation and Planning System (WEAP) and Low Emissions Analysis Platform (LEAP) tools. It generates the skeleton of DEVS-IMs stored in database for DEVS-Suite simulator. An exemplar model consisting of water, energy, and IMs demonstrates this methodology for developing nexus models of water–energy systems.

Identification of labeled Petri nets from finite automata

Automata and Petri nets are two typical models of discrete event systems. The paper studies the problem of converting a finite automaton into a Petri net that satisfies the specified structural features. More specifically, we identify a labeled Petri net from a nondeterminstic finite automaton such that the reachability graph of the identified net is isomorphic to the given automaton, meaning that the Petri net models exactly the same dynamic system as the automaton. The identified net necessarily satisfies the specified structural requirements, such as the numbers of places and transitions, absence of self-loops, the exact structure of a part of the net, and so on. Since label Petri nets and nondeterminstic finite automata are generalizations of Petri nets and deterministic finite automata, respectively, the proposed approach can identify a larger spectrum of Petri nets and effectively handle the nondeterminstic cases in the given automaton. Four kinds of conditions are first extracted from the given automaton, namely deterministic enabling conditions, nondeterministic enabling conditions (i.e., multiple transitions with the same label are enabled simultaneously at a marking), transition disabling conditions, and marking inequality conditions. Then, an integer linear programming problem is formulated by characterizing these four kinds of conditions using integer linear constraints. A labeled Petri net satisfying the structural requirements is obtained by solving the integer linear programming problem. We also present two examples to show the possible applications of the proposed identification approach.