Modeling Of Discrete Event Systems Research Articles

Predicting weight dispersion in seabass aquaculture using Discrete Event System simulation and Machine Learning modeling

Marine aquaculture, particularly in the Mediterranean region, faces the challenge of minimizing growth dispersion, which has a direct impact on the production cycle, market value and sustainability of the sector. Conventional grading methods are resource intensive and potentially detrimental to fish health. The current study presented an innovative approach in predicting fish weight dispersion in European seabass (Dicentrarchus labrax) aquaculture. Seabass is one of the two major fish species cultivated on the Mediterranean coast, with a fattening cycle of 18–24 months. During this period, several grading operations are carried out to minimize growth dispersion. The intricate feed-fish-water system, characterized by complex interactions among feeding regimes, fish behavior, individual metabolism and environmental factors, is the focus of the study. The comprehensive, five-step methodology addresses this complexity. The process begins with a Discrete Event System (DES) model that simulates the feed-fish-water dynamics, taking into account individual fish metabolism. This is followed by the development of a surrogate machine learning (ML) regressor model, which is trained on DES simulation data to efficiently predict growth distribution. The model is then calibrated and customized for specific fish stocks and production tanks. The preliminary results from 21 tanks in two trials with European seabass (D. labrax) showed the effectiveness of the method. The results from the simulation models achieved a R2 of 99.9 % and a Mean Absolute Percentage Error (MAPE) of 1.1 % for the prediction of mean final weight and a R2 of 90.3 % with a MAPE of 8.1 % for the standard deviation of final weight. In summary, this study represents a significant advance in the planning and management of seabass aquaculture. Given the lack of effective prediction tools in the aquaculture industry, the proposed methodology has the potential to reduce risks and inefficiencies, thus possibly optimizing aquaculture practices by increasing sustainability and profitability.

Modeling and Control of Discrete-Event Systems Under Joint Sensor–Actuator Cyberattacks

Modeling and Control of Discrete-Event Systems Under Joint Sensor–Actuator Cyberattacks

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.

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.

A compositional simulation framework for Abstract State Machine models of Discrete Event Systems

Modeling complex system requirements often requires specifying system components in separate models, which can be validated and verified in isolation from each other, and then integrating all components’ behavior in order to validate the operation of the whole system. If models are executable, as for state-based formal specifications, engines to orchestrate the simulation of separate component operational models are extremely useful. This paper presents an approach for the co-simulation, according to predefined orchestration schemas, of state-based models of separate components of a Discrete Event System. More precisely, we exploit the Abstract State Machine (ASM) formal method as state-based formalism, and we ( i ) define a set of operators to compose ASMs that communicate with each other through I/O events, and ( ii ) present an engine to execute the compositional simulation of the ASMs as a whole assembly. As proof of concepts, we use a set of model examples of Discrete Event Systems of increasing complexity to show the application of our approach and to evaluate its effectiveness in co-simulating models of real systems.

A new global optimization for hybrid automaton Identification

System Identification to a hybrid automaton is a complex and flourishing research field involving expertise in both continuous dynamics and discrete event system modeling. Current methodologies for hybrid automaton Identification predominantly rely on a framework centered around the sequential resolution of local signal processing optimization problems. This paper seeks to enhance the existing framework by introducing a novel step designed to address certain limitations inherent in the non-global optimization aspect of a sequential resolution framework. The proposed approach enables each Identification step not only to address its local optimization challenges but also to contribute to the optimization of a global cost function including model distance, sequential fidelity, and automaton structure.

An Information Security Engineering Framework for Modeling Packet Filtering Firewall Using Neutrosophic Petri Nets

Due to the Internet’s explosive growth, network security is now a major concern; as a result, tracking network traffic is essential for a variety of uses, including improving system efficiency, fixing bugs in the network, and keeping sensitive data secure. Firewalls are a crucial component of enterprise-wide security architectures because they protect individual networks from intrusion. The efficiency of a firewall can be negatively impacted by issues with its design, configuration, monitoring, and administration. Recent firewall security methods do not have the rigor to manage the vagueness that comes with filtering packets from the exterior. Knowledge representation and reasoning are two areas where fuzzy Petri nets (FPNs) receive extensive usage as a modeling tool. Despite their widespread success, FPNs’ limitations in the security engineering field stem from the fact that it is difficult to represent different kinds of uncertainty. This article details the construction of a novel packet-filtering firewall model that addresses the limitations of current FPN-based filtering methods. The primary contribution is to employ Simplified Neutrosophic Petri nets (SNPNs) as a tool for modeling discrete event systems in the area of firewall packet filtering that are characterized by imprecise knowledge. Because of SNPNs’ symbolic ability, the packet filtration model can be quickly and easily established, examined, enhanced, and maintained. Based on the idea that the ambiguity of a packet’s movement can be described by if–then fuzzy production rules realized by the truth-membership function, the indeterminacy-membership function, and the falsity-membership functional, we adopt the neutrosophic logic for modelling PN transition objects. In addition, we simulate the dynamic behavior of the tracking system in light of the ambiguity inherent in packet filtering by presenting a two-level filtering method to improve the ranking of the filtering rules list. Results from experiments on a local area network back up the efficacy of the proposed method and illustrate how it can increase the firewall’s susceptibility to threats posed by network traffic.

Learn-to-supervise: Causal reinforcement learning for high-level control in industrial processes

Possessing efficient supervisory control systems is crucial for maintaining the desired operational performance of complex industrial processes. Several challenges face the developers of these systems, such as requiring accurate physical models, dealing with the variability and uncertainty of process operating conditions and coordinating between local controllers to reach desired global performance. This paper proposes an intelligent supervisory control approach based on causal reinforcement learning (CRL) to effectively manipulate the controllers’ setpoints of the process in a way that optimizes its key performance indicators (KPIs), thereby improving the energy efficiency of the process. The approach adopts deep reinforcement learning (DRL) to develop an efficient control policy through interaction with a process simulation. The DRL training history is then exploited using interpretable machine learning and process mining to build a discrete event system (DES) model, in the form of a state-event graph. The DES model identifies causal relationships between events and provides interpretability to the control policy developed by the DRL method. The DES discovered is exploited as a Markov decision process to apply the Q-learning algorithm as a CRL supervisor. The supervisor incorporates causal knowledge into its training process, thus improving the DRL control policy developed and identifying the event paths that optimize the process’s KPIs. The proposed approach is validated using two heat recovery systems in a pulp & paper mill. It successfully achieves a control policy that reduces energy consumption by up to 15.6% for the first system and 5.02% for the second, compared to the expert’s baseline methods.

Identification of unobservable behavior in stochastic discrete event systems with a low number of sensors

Dynamic discrete event systems (DDES) are systems that evolve from the asynchronous occurrence of discrete events. Their versatility has become a critical modeling tool in different applications. Finding models that define the behavior of DES is a topic that has been addressed from different approaches, depending on the type of system to be modeled and the model's objective. This article focuses on the identification of timed models for stochastic discrete event systems. The identified model includes both observable and unobservable behavior. The objective of the method is achieved through the following steps:•Identifying the sequences of events observed at different time instances during the closed-loop operation of the system (observed language),•Inferring the stochastic behavior of time between events and modeling the observable behavior as a stochastic timed Interpreted Petri Net (st-IPN),•and finally, inferring the non-observable behavior using the language projection operation between the observed language and the language generated by the st-IPN.This method has novel aspects because it uses timed events, can be applied to systems with a low number of sensors and can infer unobservable behavior for any sequence of events.

On Quantitative Properties Preservation in Reconfigurable Generalized Stochastic Petri Nets

Generalized stochastic Petri nets (GSPNs) have been extended to several dynamic-structure formalisms providing suitable tools for the modeling and verification of reconfigurable discrete-event systems (R-DESs). However, analyzing the performance of large-complex R-DESs remains a big challenging issue. Indeed, dynamic-structure GSPNs still rely on old-fashioned techniques often causing the state-space explosion problem. In this article, we present a new technique for the quantitative analysis of a dynamic-structure formalism called reconfigurable GSPNs without computing the whole state space. This work describes new reconfiguration forms used to preserve desired quantitative properties of parts of interest after each reconfiguration. Therefore, it is only required to verify the examined properties at an initial configuration. The proposed technique is proven to effectively reduce the state space and shorten the computation time in such cases. Finally, some experimental results are provided to illustrate that, from a computational perspective, the developed approach outperforms the existing tools.

The Model Matching Problem for Max-Plus Linear Systems: A Geometric Approach

Linear systems over the max-plus algebra provide a suitable formalism to model discrete event systems where synchronization, without competition, is involved. In this paper, we consider a formulation of the model matching problem for systems of such class, in which the output of a given system, called the plant, is forced, by a suitable input, to track exactly that of a given model. A necessary and sufficient condition for its solvability is obtained by making a suitable use of geometric methods in the framework of systems over the max-plus algebra.

Self-Learning Fuzzy Automaton With Input and Output Fuzzy Sets for System Modelling

Fuzzy Automaton plays a key role in modeling a system as a fuzzy discrete event system and determining the model's properties such as observability and predictability. We recently developed online supervised learning algorithms for the fuzzy automaton to learn its event transition matrix under three different conditions tied to availability of pre- and post-event states, which are the first and only studies in the current literature. In this paper, we first eliminate, in a theoretical manner, the restriction in our prior studies that the individual pre- and post-event states must be identical. We then tackle a more realistic and general condition-learning the transition matrix when neither pre-event nor post-event state is known, which covers the previous three conditions as special cases. The only information assumes to be available is values of the variables and they are only known to be vaguely related to the states. We link the variables to the states through (Gaussian) fuzzy sets owing to the conceptual relationship between a fuzzy set and a fuzzy state. The resulting innovative model is called the Fuzzy Automaton with Input and Output Fuzzy Sets. Stochastic-gradient-descent-based algorithms are derived for the model to iteratively learn online the transition matrix and all the parameters of the fuzzy sets simultaneously. Another significant extension is that unlike our previous models, the new model is capable of determining an individual post-event state even if it is associated with two or more variables, expanding model's utility. It is the first fuzzy discrete event system model that is capable of learning solely based on sensor data without relying on any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">subjective</i> input from humans, removing a significant bottleneck for real-world applications. Computer simulation results are furnished to exhibit learning performance of this new model. This model will be valuable for solving practical problems in healthcare and various industries, and we discuss in detail its potential in modeling of disease treatment decision-making.

Modeling and Simulation of Crude Oil Sea–River Transshipment System in China’s Yangtze River Basin

China’s Yangtze River Basin has an increasingly strong demand for crude oil. As a seaborne import port for crude oil, Ningbo-Zhoushan Port is under pressure to undertake the transshipment of crude oil to various oil terminals in the Yangtze River Basin. To alleviate the stress of crude oil transportation in Ningbo-Zhoushan Port, the port operator proposed the crude oil sea–river transshipment scheme in Nantong Port. Therefore, this paper aims to verify the feasibility of this scheme. We used the discrete event system modeling and entity relationship diagram method to construct the hierarchical and concept models of the Yangtze River Basin’s crude oil sea–river transportation system. Furthermore, we developed corresponding simulation modules on the Witness platform and carried out a simulation experiment of the crude oil sea–river transfer scheme. In the experiment, we analyzed the influence of the transshipment ratio on berth utilization, waiting time, and sailing time of other ports by adjusting the parameter of the transshipment ratio. The experimental results show that when the transshipment rate reaches 100%, the utilization rates of loading and unloading berth in Nantong Port are 4% and 13%, respectively, which evidences that Nantong Port has transshipment potential. At the same time, the simulation experiment’s statistical indicators, such as the utilization rate of oil berths, the queuing time of oil tankers, and the sailing time, not only confirm the feasibility of the crude oil sea–river transshipment scheme of Nantong Port but also confirm that the scheme is helpful to improve crude oil transportation efficiency. The simulation results benefit the port operation decision, and the established model and simulation module can be encapsulated and reused.

Robustness Evaluation Process for Scheduling under Uncertainties

Scheduling production is an important decision issue in the manufacturing domain. With the advent of the era of Industry 4.0, the basic generation of schedules becomes no longer sufficient to face the new constraints of flexibility and agility that characterize the new architecture of production systems. In this context, schedules must take into account an increasingly disrupted environment while maintaining a good performance level. This paper contributes to the identified field of smart manufacturing scheduling by proposing a complete process for assessing the robustness of schedule solutions: i.e., its ability to resist to uncertainties. This process focuses on helping the decision maker in choosing the best scheduling strategy to be implemented. It aims at considering the impact of uncertainties on the robustness performance of predictive schedules. Moreover, it is assumed that data upcoming from connected workshops are available, such that uncertainties can be identified and modelled by stochastic variables This process is supported by stochastic timed automata for modelling these uncertainties. The proposed approach is thus based on Stochastic Discrete Event Systems models and model checking techniques defining a highly reusable and modular process. The solution process is illustrated on an academic example and its performance (generecity and scalability) are deeply evaluated using statistical analysis. The proposed application of the evaluation process is based on the technological opportunities offered by the Industry 4.0.

The use of Time-Interval Automata in the Modeling of Timed Discrete Event Systems and its Application to Opacity

This paper addresses the problem of language opacity for a class of discrete-event systems whose transitions occur within some known time interval after the event becomes enabled. For this purpose, a new class of timed automata is proposed, time-interval automata (TIA), in which the time elapsed is associated with a unique clock, allowing each transition to be associated with a time interval of occurrence. We present new procedures to compute complement, projection of TIA, complete synchronous composition between TIA, which, differently from existing approaches, do not require refinement by partition. We extend the usual language-based definition of opacity to this class of timed automata, present necessary and sufficient conditions for timed language-based opacity, and propose an algorithm for its verification.

Max-Min-Plus-Scaling Systems in a Discrete-Event Framework with an Application in Urban Railway

In this paper we discuss modelling and control of discrete-event systems using max-min-plus-scaling systems. We analyse how the basic operations max, min, plus, and scaling occur in the modelling phase and we discuss some general forms for the system. Because of the different/deviating character of the signals in a discrete-event MMPS model, we will discuss concepts such as time-invariance and steady-state behavior. In the design of a model predictive controller for MMPS systems we have to revisit the cost functions in light of the discrete-event nature of the signals. We finalize this paper with the an intuitive case study on an urban railway line, performing both modelling and control.

MDESops: An Open-Source Software Tool for Discrete Event Systems Modeled by Automata

The salient features of the new software tool MDESops are presented. MDESops is Python-based and open-source. Its focus is the analysis and control of discrete event systems (DES) modeled by finite-state automata. MDESops has core functions to manipulate deterministic and nondeterministic automata, including parallel composition and determinization. It has functions to analyze diagnosability and opacity properties. MDESops also includes functions that implement both standard and more recent algorithmic procedures from the theory of supervisory control of DES, including synthesis of supervisors for partially-observed systems, synthesis of attackers in systems with compromised sensors, and synthesis of supervisors resilient to deception attacks. It is the hope that MDESops can serve as a useful platform for algorithm development and prototyping in DES research.

Modeling and Optimal Supervisory Control of Networked Discrete-Event Systems and Their Application in Traffic Management

In this paper, we investigate the modeling and control of networked discrete-event systems (DESs), where a supervisor is connected to the plant via an observation channel and the control commands issued by the supervisor are delivered to the actuator of the plant via a control channel. Communication delays exist in both the observation channel and the control channel. First, a novel modeling framework for the supervisory control of DESs subject to observation delays and control delays is presented. The framework explicitly models the interaction process between the plant and the supervisor over the communication channels. Compared with the previous work, a more accurate “dynamics” of the closed-loop system is specified. Under this framework, we further discuss how to estimate the states of the closed-loop system in the presence of observation delays and control delays. Based on the state estimation, we synthesize an optimal supervisor on the fly to maximize the controlled behaviors while preventing the system from leaving the desired behaviors under communication delays. We compare the proposed supervisor with the supervisor proposed in the literature and show that the proposed supervisor is more permissive. As an application, we show how the proposed approach can be applied to manage vehicles in a signal intersection. Finally, we show how to extend the proposed framework to model a system whose actuators and sensors are distributed at different sites.

XDEVS: A toolkit for interoperable modeling and simulation of formal discrete event systems

AbstractEmploying Modeling and Simulation (M&amp;S) extensively to analyze and develop complex systems is the norm today. The use of robust M&amp;S formalisms and rigorous methodologies is essential to deal with complexity. Among them, the Discrete Event System Specification (DEVS) provides a solid framework for modeling structural, behavior and information aspects of any complex system. This gives several advantages to analyze and design complex systems: completeness, verifiability, extensibility, and maintainability. DEVS formalism has been implemented in many programming languages and executable on multiple platforms. In this paper, we describe the features of an M&amp;S framework called xDEVS that builds upon the prevalent DEVS Application Programming Interface (API) for both modeling and simulation layers, promoting interoperability between the existing platform‐specific (C++, Java, Python) DEVS implementations. Additionally, the framework can simulate the same model using sequential, parallel, or distributed architectures. The M&amp;S engine has been reinforced with several strategies to improve performance, as well as tools to perform model analysis and verification. Finally, xDEVS also facilitates systems engineers to apply the vision of model‐based systems engineering (MBSE), model‐driven engineering (MDE), and model‐driven systems engineering (MDSE) paradigms. We highlight the features of the proposed xDEVS framework with multiple examples and case studies illustrating the rigor and diversity of application domains it can support.

A Discrete-Event System Approach for Modeling and Mitigating Power System Cascading Failures

A power system cascading failure can propagate through sequential tripping of components in the network. As a result, a complete or partial shutdown may occur. In this article, we develop a new systematic approach to identify and prevent cascading failures in power systems using supervisory control of discrete-event systems (DESs). We build the DES model for a power system in a modular fashion by first modeling its components as (small) automata and then combining these automata using parallel composition. To overcome state explosion, we use online lookahead control that can significantly reduce the number of states to be considered. Since some events such as line trips cannot be disabled but can be preempted by forcing some forcible events such as load shedding, we extend supervisory control of DES to include forcible events. This extension allows us to control power systems effectively. The proposed control is implemented in an implementation platform that we build in MATLAB. The platform uses MATPOWER to simulate a power system and then control it using the proposed DES controller. Simulation studies are carried out for IEEE 6-, 30-, and 118-bus systems. The results verify the effectiveness of the proposed approach.