Discrete Event Systems Research Articles

Polynomial-time verification of pattern diagnosability for timed discrete event systems

Polynomial-time verification of pattern diagnosability for timed discrete event systems

Supervisory Control of Networked Timed Discrete Event Systems With Bandwidth Constraints

Supervisory Control of Networked Timed Discrete Event Systems With Bandwidth Constraints

Computation of synchronous diagnosis bases of discrete-event systems

Computation of synchronous diagnosis bases of discrete-event systems

Simulation-Based Development of Internet of Cyber-Things Using DEVS

Simulation-based development is a structured approach that uses formal models to design and test system behavior before building the actual system. The Internet of Things (IoT) connects physical devices equipped with sensors and software to collect and exchange data. Cyber-Physical Systems (CPSs) integrate computing directly into physical processes to enable real-time control. This paper reviews the Discrete-Event System Specification (DEVS) formalism and explores how it can serve as a unified framework for designing, simulating, and implementing systems that combine IoT and CPS—referred to as the Internet of Cyber-Things (IoCT). Through case studies that include home automation, solar energy monitoring, conflict management, and swarm robotics, the paper reviews how DEVS enables construction of modular, scalable, and reusable models. The role of the System Entity Structure (SES) is also discussed, highlighting its contribution in organizing models and generating alternative system configurations. With this background as basis, the paper evaluates whether DEVS provides the necessary modeling power and continuity across stages to support the development of complex IoCT systems. The paper concludes that DEVS offers a robust and flexible foundation for developing IoCT systems, supporting both expressiveness and seamless transition from design to real-world deployment.

Nonblocking Modular Supervisory Control of Discrete Event Systems via Reinforcement Learning and K-Means Clustering

Traditional supervisory control methods for the nonblocking control of discrete event systems often suffer from exponential computational complexity. Reinforcement learning-based approaches mitigate state explosion by sampling many random sequences instead of computing the synchronous product of multiple modular supervisors, but they struggle with limited reachable state spaces. A primary novelty of this study is to use the K-means clustering method for online inference with the learned state-action values. The clustering method divides all events at a state into the good group and the bad group. The events in the good group are allowed by the supervisor. The obtained supervisor policy can ensure both system constraints and larger control freedom compared to conventional RL-based supervisors. The proposed framework is validated by two case studies: an industrial transfer line (TL) system and an automated guided vehicle (AGV) system. In the TL case study, nonblocking reachable states increase from 56 to 72, while in the AGV case study, a substantial expansion from 481 to 3558 states is observed. Our new method achieves a balance between computational efficiency and nonblocking supervisory control.

Enhancing Routed DEVS Models with Event Tracking

The Routed Discrete Event System Specification (RDEVS) is a modular and hierarchical Modeling and Simulation (M&S) formalism based on the Discrete Event System Specification (DEVS) formalism that provides a set of design models for dealing with routing problems over DEVS. At the formal level, RDEVS models (as DEVS models themselves) are defined mathematically. However, software implementations of both formalisms are based on an object-oriented paradigm. Furthermore, at the implementation design level, the RDEVS formalism is represented by a conceptual model that uses DEVS simulators as execution engines. Even when RDEVS models can be executed with DEVS simulators, the resulting data (obtained as execution outputs) remains DEVS-based, restricting the study of event flows between models influenced by routing policies. This paper shows how the RDEVS formalism design was enhanced to include event tracking in the models without altering their expected behavior during simulation. Such an improvement is based on adding new features to existing RDEVS components. These features are defined as trackers, which are responsible for getting structured data from events exchanged during RDEVS executions. The proposed solution employs the Decorator pattern as a software engineering option to achieve the required goal. It was deployed as a Java package attached to the RDEVS library, devoted to collecting structured event flow data using JavaScript Object Notation (JSON). The results highlight the modeling benefits of adding event tracking to the original capabilities of the RDEVS formalism. For the M&S community, the novel contribution is an advance in understanding how best modeling practices of software engineering can be used to enhance their software tools in general and the RDEVS formalism in particular.

Supervised optimal control in complex continuous systems with trajectory imitation and reinforcement learning

Supervisory control theory (SCT) is widely used as safeguard mechanism with control of discrete event systems (DESs). In complex continuous systems, in order to avoid system’s behavior violating specifications, the supervised control problem of these systems is quite different. Continuous state and action spaces of high dimension make languages of automaton no longer suitable for describing the information of specifications which remains challenging on control of real physical systems. Reinforcement learning (RL) automatically learns complex decisions through trial and error, but it requires the design of precise reward functions combined with domain knowledge. For complex scenarios where the reward function cannot be achieved or is only with sparse rewards, we proposed a novel supervised optimal control framework based on trajectory imitation (TI) and reinforcement learning (RL) in this paper. Firstly, behavior cloning (BC) is adopted to pre-train the policy model based on a small number of human demonstrations. Secondly, a generative adversarial imitation learning (GAIL) method is carried out to obtain the implicit characteristics of demonstration data. Furthermore, after the primary and implicit features are extracted by the above steps, a Demo-based RL algorithm is designed by adding the demonstration data to the RL replay buffer with augmented loss function to enhance the system performance to its maximum potential. Finally, the proposed method is validated through multiple simulation experiments on object relocation and tool using task of dexterous multifingered hands. In handling the more complex tool using task, the proposed approach achieves a 19.7% decrease in convergence time as opposed to the latest method. And the proposed method for the two tasks results in policies that display natural movements and shows higher robustness compared with the baseline model.

Joint opacity and opacity against state-estimate-intersection-based intrusion of discrete-event systems

Joint opacity and opacity against state-estimate-intersection-based intrusion of discrete-event systems

A simulated annealing method for minimum initial marking estimation in labeled Petri nets: Applications and comparative studies

State estimation plays an important role in the field of system supervisory control. With the increase in the scale and complexity of practical systems, problems of state/event estimation for discrete event systems in Petri nets have attracted significant attention. One of the well-studied problems is how to estimate the minimum initial markings (MIMs) in a known labeled Petri net based on the observation of its event sequence. The existing approaches have disadvantages such as (1) much higher computational complexity in order to increase the MIM quantity; and (2) much fewer MIMs in order to decrease the computational complexity. In this paper, a variant of simulated annealing (SA) algorithm is developed to determine the set of MIMs. We take advantage of SA for global search in the problem’s search space to find initial markings that are minimal in their token counts, and then use the selection strategy of SA to determine MIMs from them. Finally, the effectiveness of the proposed method is illustrated through two simulation experiments of industrial applications. Comparisons with existing work are conducted to demonstrate the merits of our method.

Modelling of queuing systems using blockchain based on Markov process for smart healthcare systems

Queueing theory employs mathematical analysis to establish effectiveness metrics. Optimization models are then formulated using significant and efficient measures, such as data, to ascertain system efficiency and requirements. Each queuing system represents a discrete event system problem, and simulating these systems aids in addressing challenges and conducting practical performance analysis. Blockchain offers various benefits, including redistribution, accessibility, durability, reliability, constancy, anonymity, auditability, and data security. Its applications span across cryptocurrencies, financial services, reputation management, the 'Internet of Things’, the sharing economy, and social and community services. Notably, foundational theory is increasingly pertinent in the blockchain field. For instance, performance analysis and optimization of blockchain systems rely on mathematical models like Markov processes and queueing theory. In smart healthcare, blockchain technology enhances disease diagnosis, patient care, and overall quality of life. Due to the substantial patient data stored on blockchain in smart healthcare architectures, queueing models are indispensable for efficient data processing. This paper leverages Markov chains to establish queueing theory for blockchain systems and assess the performance of smart healthcare architecture. A "Markovian-batch-service" queueing framework is devised for this purpose, modeling input and processing parameters essential for reliable queuing network simulations.

Modeling and control of time-constrained partially controllable discrete event systems: an application for a supply chain with the presence of disturbances

Modeling and control of time-constrained partially controllable discrete event systems: an application for a supply chain with the presence of disturbances

A Modeling Formalism for the Identification of Reinitializable Discrete-Event Systems with the Aim of Fault Detection

A Modeling Formalism for the Identification of Reinitializable Discrete-Event Systems with the Aim of Fault Detection

State estimation of timed probabilistic discrete event systems via artificial neural networks

This paper is about state estimation of timed probabilistic discrete event systems. The main contribution is to propose general procedures for developing state estimation approaches based on artificial neural networks. It is assumed that no formal model of the system exists but a data set is available, which contains the history of the timed behaviour of the system. This dataset is exploited to develop a neural network model that uses both logical and temporal information gathered during the functioning of the system as inputs and provides the state probability vector as output. Two main approaches are proposed: (i) state estimation of timed probabilistic discrete event systems over observations: in this case the state estimate is reconstructed at the occurrence of each new observation; (ii) state estimation of timed probabilistic discrete event systems over time: in this case the state estimate is reconstructed at each clock time increment. For each approach, the paper outlines the process of data preprocessing, model building and implementation. The presented approaches pave the way for further applications of machine learning in discrete event systems.

Verification of Opacity Under a K-Delay Orwellian Observation Mechanism

Opacity, an important property of the information flow in discrete-event systems (DESs), characterizes whether the secret information in a system is ambiguous to a passive observer (called an intruder). Observation models play a critical role in the analysis of opacity. In this paper, instead of adopting a fully static observation model or a fully dynamic observation model, we use a novel Orwellian-type observation model to study the verification of the current-state opacity (CSO), where the observability of an unobservable event can be re-interpreted once certain/several specific conditions are met. First, a K-delay Orwellian observation mechanism (KOOM) is proposed as a novel Orwellian-type observation mechanism for extending the existing Orwellian projection. The main characteristics of the KOOM are delaying the inevitable information release and narrowing the release range for historical information to protect the secrets in a system to a greater extent than with the existing Orwellian projection. Second, we formulate the definitions of standard and strong CSO under the KOOM. Finally, we address the verification problem for these two types of opacity by constructing two novel information structures called a standard K-delay verifier and a strong K-delay verifier, respectively. An analysis of the computational complexity and illustrative examples are also presented for the proposed results. Overall, the proposed notions of standard and strong CSO under the KOOM capture the security privacy requirements regarding a delayed release in applications, such as intelligent transportation systems, etc.

ACTIVE FAULT DIAGNOSIS IN AUTOMATED MANUFACTURING SYSTEMS: MODELED USING INTERPRETED PETRI NETS

This paper deals with active diagnosis in Discrete Event System represented as Interpreted Petri Nets, which have been used to model an automated manufacturing system (AMS). In addition, a fault diagnosis scheme is implemented through residuals in an AMS controlled by a Mitsubishi PLC, which is monitored by an embedded system capable of measuring the current in the controllers in order to share information to the diagnoser, through the Modbus communication protocol with the purpose of increasing its reliability in the pronouncement. Key Words: Interpreted Petri Nets, active diagnosis, Factory IO, diagnosability, fault diagnosis.

An Automated Verification Framework for DEVS-Coupled Models to Enhance Efficient Modeling and Simulation

Discrete Event System Specification (DEVS) is a formalism widely used for modeling and simulating complex systems. The main features of DEVS are defining models in a strict mathematical form and representing systems through hierarchical structures. However, when DEVS models have incorrect connection structures and inappropriate behaviors contrary to design intentions, simulation results can be distorted. This can cause serious problems that may lead to inaccurate decision-making. In this paper, we propose an automated verification framework to improve the accuracy and efficiency of coupled models in the DEVS-Python environment. This framework defines test scripts for coupled models, performs automatic verification before simulation execution, and provides the results to users. Experimental results showed that the proposed framework improved execution time by approximately 30–100 times compared to traditional unit testing methods, although memory and CPU usage increased slightly. Despite this increase in resource usage, the proposed framework provides high efficiency and consistent performance in verifying complex DEVS-coupled models.

Critical Observability of Stochastic Discrete Event Systems Under Intermittent Loss of Observations

A system is said to be critically observable if the operator can always determine whether the current state belongs to a set of critical states. Due to the communication failures, systems may suffer from intermittent loss of observations, which makes the system not critically observable. In this sense, to characterize critical observability in a quantitative way, this paper extends the notion of critical observability to stochastic discrete event systems modeled as partially observable probabilistic finite automata. Two new notions, called step-based almost critical observability and almost critical observability are proposed, which describe a measure of critical observability for a given system against intermittent loss of observations. We introduce a new language operation to obtain a probabilistic finite automaton describing the behavior of the plant system under intermittent loss of observations. Based on this structure, we also present verification methodologies to check the aforementioned two notions and analyze the complexity. Finally, the results are applied to a raw coal processing system, which shows the effectiveness of the proposed methods.

Mitigating the impacts of climate change: A case study of the city of Tshwane, South Africa

Climate change poses a major global challenge which affects human health and livelihood, ecosystem, critical sectors and infrastructure among others. The objective of this research is to investigate and mitigate the impacts of climate change in the City of Tshwane. A combination of quantitative and case study approaches was employed, with datasets obtained from the South African Weather Service, the City of Tshwane, and the Auditor-General. The data were modeled using a combination of discrete event simulation and system dynamics within the AnyLogic (version 8.2.3) environment. The results showed an increase in climate trends from 1981 to 2022, suggesting a growing frequency of heat waves, heavier rainfall, and more frequent floods, among other phenomena. The study recommends the use of simulation approaches to assess the impacts of climate change on the city’s infrastructure during the planning process. The findings can assist the City of Tshwane in planning for future climate scenarios, enabling stakeholders to develop sustainable strategies for climate change adaptation and mitigation, with the goal of creating a climate change-resilient city. If the suggested climate change policies are implemented, there would be a 15% annual decrease in extreme events (including excessive temperature, flooding, and drought), a 3% reduction in greenhouse gas emissions, and a 25% decrease in gross domestic product loss, alongside a 15% improvement in infrastructure performance.

Optimization-based computation of bounded sequences to reach target states in DESs

The enumeration of legal transition paths leading to a target state (or set of states) is of paramount importance in the control of discrete event systems, but is hindered by the state explosion problem. A method is proposed in this paper, in the context of Petri nets, to calculate and enumerate firing count vectors for which there exists at least an admissible transition sequence leading to a given target marking. The method is shown to improve the approach based on singular complementary transition invariants proposed by Kostin and combines an integer linear programming formulation that finds the shortest minimal solution and a branching procedure that realizes a partition of the solution set. The enumeration can be restricted to minimal solutions or extended to non-minimal ones. Moreover, the approach is extended by adding a further constraint that the target transition sequences should pass by intermediate markings (in a specific order or not). Finally, source, target and via markings can be replaced by sets of markings. Some analytical examples are discussed in detail to show the effectiveness of the proposed approach.

Analyzing New Operation Strategy of Demand-Responsive Transports Using Discrete-Event Simulation Framework

Demand-responsive transport (DRT) provides flexible ride-sharing by dynamically adjusting routes based on real-time user demand, making it suitable for complex urban mobility needs. This study proposes a modular simulation framework based on the DEVS (Discrete Event System Specification) formalism and introduces an “express service” strategy that enables direct trips without intermediate stops. The framework supports scenario-based analysis using key performance indicators (KPIs) and allows for flexible testing of operational strategies. Two experiments were conducted: the first validated the simulation model under varying demand and fleet conditions; and the second assessed the impact of the express service. Results showed that express passengers experienced significantly shorter waiting and riding times, while standard passenger service remained stable. The strategy also improved operational efficiency under constrained resources. This study contributes to a configurable simulation platform for evaluating differentiated DRT services and provides practical insights for adaptive service planning, especially in urban settings where tiered mobility solutions are increasingly needed.