Discrete-event Model Research Articles

A Diagnosis Study on a Train Passenger Access System using Petri Net Models

In this paper, we conduct a diagnosis analysis of the passenger access system, while considering a high level abstraction perspective that allows for adapting discrete event models to represent the system behavior. Firstly, we establish Petri net behavioral models for the global system functions, including the nominal operating mode and various faulty behaviors. Then, based on the established Petri net models, a diagnoser-based approach is brought into play to investigate the diagnosability of the system regarding the different predetermined classes of failures.

A Discrete Event Simulation Model for Inbound Baggage Handling

Abstract Inbound baggage handling represents a crucial process among airport terminal’s activities as it affects directly airport performance and passengers’ service quality perception. It is important to make the inbound process as efficient as possible and to explore solutions to enhance the performance of the system, thus reducing passengers’ waiting time at baggage carousels. The aim of this paper is to present a detailed discrete event model of inbound baggage handling at a large regional Italian airport. The simulation model allows to fully understand the whole process and to identify bottlenecks and critical operations. The model is validated by comparing the simulation results with real data.

Model-Based Monitoring of a Train Passenger Access System

The passenger access system (PAS) is a complex mechatronic train onboard module with high reliability and safety requirements. This module fulfills one of the dozen main onboard functions onboard train. Consequently, any related fault occurrence may have a serious impact on the safety and availability of the whole train operation. In this context, developing effective automated monitoring and diagnostic techniques for the PAS, as early as from the design phase of the system, becomes an essential and challenging task. In this paper, we carry out a monitoring study on this system, while considering a sufficiently high-level abstraction perspective that allows for adapting discrete event models representing the behavior of the system. First, we establish a Petri net behavioral model that includes the nominal operating mode as well as various faulty behaviors. Then, based on the established Petri net models, a fault detection approach is used to investigate the diagnosability property and synthesize the diagnosers regarding different predetermined classes of failures. Finally, we show how the outputs of the diagnosability analysis can help make efficient design choices that allow for improving the safety of the whole system.

Enhanced discrete event model for system identification with the aim of fault detection

In this paper, we present a new model for discrete-event system identification that is suitable for fault detection, called Deterministic Automaton with Outputs and Conditional Transitions (DAOCT). The model is computed from observed fault-free paths, and represents the fault-free system behavior. In practice, a trade-off between size and accuracy of the identified automaton has to be found. In order to obtain compact models, loops are introduced in the model, which implies that sequences that are not observed can be generated by the model leading to an exceeding language. This exceeding language is associated with possible non-detectable faults, and must be reduced in order to use the model for fault detection. We show, in this paper, that the exceeding language generated by the DAOCT is smaller than the exceeding language generated by other models proposed in the literature, reducing, therefore, the number of possible non-detectable faults. We also show that if the identified DAOCT does not have cyclic paths, then the exceeding language is empty, and the model represents all and only all observed fault-free sequences generated by the system. A practical example is used to illustrate the results of the paper.

Model of passenger rolling stock maintenance

In order to improve the quality of maintenance and repair of a passenger rolling stock, Russian Railways JSC requires transition to routine maintenance. The paper studies the main stages of building a discrete-event model of a passenger rolling stock maintenance using AnyLogic environment. The following key parameters of maintenance are used: maintenance facilities location, rolling stock operability index, probability index for operational efficiency (downtime and failure probability), maintenance and repair costs. The maintenance model is implemented in the integrated modeling library. Simulation model enables to implement the modeled system and obtain performance indicators of routine maintenance system operation. By simulating various conditions it is possible to evaluate efficiency of the studied maintenance system for a high-speed passenger rolling stock.

A class of extended time Petri nets for modeling and simulation of discrete event systems

Time Petri nets (TPNs) have been widely used for modeling discrete event systems such as manufacturing, supply chain, and military systems. However, TPNs still have many drawbacks in some scenarios where an operation or process is associated with probability, and also lack appropriate simulation algorithms for analyzing different types of systems. In this paper, we address these two issues by proposing a class of extended time Petri nets (ETPNs) and presenting an appropriate simulation algorithm. We illustrate and validate our approach using a hypothetic command and control system, which shows that this approach could be a powerful tool for modeling and analyzing discrete event systems.

A Discrete Event Model of Viability Building in a Public University Organization

In the context of today’s interconnected world, public higher education is confronted with a shift of its role from public good to public service, which is accompanied by new challenges in building and maintaining the viability of public universities. There is a vast literature on this topic, but there is not yet reported a systematic approach in modeling the basic steps of decision making and problem solving to be completed by a public university to remain viable. Starting from the concepts of service system, and consonance and resonance as key viability conditions, this paper proposes a discrete event model of the viability building and maintaining process in a generic public university organization. The model evolution is driven by events conditioned by internal, external, or mixed causes. Some of the uncontrollable events occurring in the process may drive the discrete evolution to disturbance rejection states, where specific recovery strategies are applied. If recovery is successful, disturbances may become sources of innovation. The discrete event model can be used as a theoretical and conceptual tool for building and testing instances of the viability building and maintaining processes in public universities, and also for refining academic evaluation criteria by the authorities.

Simulation modeling of Houston Ship Channel vessel traffic for optimal closure scheduling

This paper focuses on simulation and analysis of the Houston Ship Channel vessel traffic and operation, through a discrete event model in Arena. The model is applied to mitigate the consequences of the channel closure for constructing a new bridge over the waterway. To evaluate different closure scenarios, real world data is analyzed, and a single factor ANOVA is used to find significant vessel waiting time differences. In addition, Fisher pairwise comparison method is applied to specify the best closure alternative. The results reveal that the best closure scenario will decrease waiting time up to 70%. The model can be used for assessing the performance of the system under different decision making frameworks.

Traumatic coagulopathy and massive transfusion: improving outcomes and saving blood

IntroductionDysfunction of the coagulation system, termed trauma-induced coagulopathy (TIC), is a major problem in patients who bleed after injury. Trauma haemorrhage is considered one of the leading preventable causes of death worldwide. Deaths occur early and, despite the presence of trauma teams and large transfusions of blood products, outcomes remain poor.MethodsWe conducted a multimodal programme of work to develop our understanding of coagulopathy and its optimal management. We studied the epidemiology, management and health economics of trauma haemorrhage, including the provision of care during mass casualty events. We combined systematic reviews of the literature with a national study of trauma haemorrhage, its transfusion management and associated health-care costs. We further examined several point-of-care coagulation tools for their ability to diagnose coagulopathy and assess the response to blood component therapy. We progressively implemented our findings into practice and assessed the outcomes of trauma patients presenting to our major trauma centre. To examine different approaches to the provision of blood to casualties in a mass casualty event, we constructed a discrete event model based on data from the 2005 London bombings.Key resultsOur systematic reviews found little strong evidence for the existing diagnostic tools or the practice of delivery of blood components in trauma haemorrhage. Our national study recruited 442 patients in 22 hospitals and found that the 1-year mortality rate for massive haemorrhage approached 50%. Half of these deaths occurred in the first 24 hours after injury and half of these occurred in the first 4 hours. We identified this early time window as a period when the provision of blood component therapy was often below the recommended thresholds and blood component therapy was delivered inconsistently. Studying early TIC we determined that loss of fibrinogen and excessive fibrinolysis were key derangements. We were able to determine that rotational thromboelastometry could identify early coagulopathy within 5 minutes, a large improvement on laboratory tests. We were further able to show how existing damage control resuscitation regimens with high-dose plasma do not maintain haemostatic competency during haemorrhage. In total, the estimated cost of treating a major haemorrhage patient was £20,600 and the estimated cost of treating a massive haemorrhage patient was £24,000. Nationally, the estimated cost of trauma haemorrhage is £85M annually. In mass casualty situations, early results show that the only mutable factor that affects the provision of care to a large degree, in the initial phase of the response, is the level of blood stocks held in the receiving hospital.ConclusionsThis multimodal programme of work has led to new understandings of the epidemiology of trauma haemorrhage and its underlying mechanisms and clinical course. We have defined diagnostic tools and trigger thresholds for identification and management and increased our understanding of how blood component and other therapeutics affect coagulopathy and when they are likely to be most effective. This diagnostic work has been taken forward at an international level to produce new personalised guidelines for the management of trauma haemorrhage. The findings have had important therapeutic implications, which have led to important changes in practice that have been incorporated into new national and international guidelines.FundingThe National Institute for Health Research Programme Grants for Applied Research programme.

Discrete Event Modeling and Simulation of Probabilistic Voting-based Filtering to Find Proper Security Parameters in Wireless Sensor Networks

Discrete Event Modeling and Simulation of Probabilistic Voting-based Filtering to Find Proper Security Parameters in Wireless Sensor Networks

The impact of downtime over the long-term energy yield of a floating wind farm

This paper presents a methodology to evaluate the influence of downtime on the long-term energy yield of a floating wind farm. It is based on the definition and integration of three models: a discrete event model, which simulates failures and reparation times, a floating wind turbine model, which simulates platform motions and operational stops, and a wind farm model, which models wake effects between turbines. The methodology is applied to a wind farm composed by ten floating turbines and located off the coast of Cantabria, Spain. Results indicate that downtime affects significantly the mean energy yield of the farm, which decreases linearly with the mean failure rate, mean reparation time and probability of exceedance of the operational thresholds for the considered case studies.

Understanding the Effect of Statins and Patient Adherence in Atherosclerosis via a Quantitative Systems Pharmacology Model Using a Novel, Hybrid, and Multi-Scale Approach.

Background and Objective: Statins are one of the most prescribed drugs to treat atherosclerosis. They inhibit the hepatic HMG-CoA reductase, causing a reduction of circulating cholesterol and LDL levels. Statins have had undeniable success; however, the benefits of statin therapy crystallize only if patients adhere to the prescribed treatment, which is far away from reality since adherence decreases with time with around half of patients discontinue statin therapy within the first year. The objective of this work is to; firstly, demonstrate a formal in-silico methodology based on a hybrid, multiscale mathematical model used to study the effect of statin treatment on atherosclerosis under different patient scenarios, including cases where the influence of medication adherence is examined and secondly, to propose a flexible simulation framework that allows extensions or simplifications, allowing the possibility to design other complex simulation strategies, both interesting features for software development.Methods: Different mathematical modeling paradigms are used to present the relevant dynamic behavior observed in biological/physiological data and clinical trials. A combination of continuous and discrete event models are coupled to simulate the pharmacokinetics (PK) of statins, their pharmacodynamic (PD) effect on lipoproteins levels (e.g., LDL) and relevant inflammatory pathways whilst simultaneously studying the dynamic effect of flow-related variables on atherosclerosis progression.Results: Different scenarios were tested showing the impact of: (1) patient variability: a virtual population shows differences in plaque growth for different individuals could be as high as 100%; (2) statin effect on atherosclerosis: it is shown how a patient with a 1-year statin treatment will reduce his plaque growth by 2–3% in a 2-year period; (3) medical adherence: we show that a patient missing 10% of the total number of doses could increase the plaque growth by ~1% (after 2 years) compared to the same “regular” patient under a 1-year treatment with statins.Conclusions: The results in this paper describe the effect of pharmacological intervention combined with biological/physiological or behavioral factors in atherosclerosis progression and treatment in specific patients. It also provides an exemplar of basic research that can be practically developed into an application software.

The confirmed realities and myths about the benefits and costs of 3D visualization and virtual reality in discrete event modeling and simulation: A descriptive meta-analysis of evidence from research and practice

The past seventeen years has witnessed a significant transformation in discrete event modeling and simulation, and the introduction of new modeling methodology based on three-dimensional (3D) visualization and virtual reality (VR) technologies. While several studies demonstrate the benefits and costs of 3D and VR modeling in discrete event simulation (DES) based on empirical evidence, others rely on anecdotal evidence and experience from practice. Further, there are still some unsubstantiated claims about the gains and challenges arising from the application of 3D and VR technologies in DES. This paper synthesizes the realized benefits and costs associated with modeling and simulation in 3D and VR while differentiating the realities from claims (myths). The results show that 3D and VR techniques offer significant benefits in the major modeling and simulation tasks than speculated among practitioners in the simulation community.

Solving Diagnosability of Hybrid Systems via Abstraction and Discrete Event Techniques

This paper addresses the problem of determining the diagnosability of hybrid systems by abstracting hybrid models to a discrete event setting. From the continuous model the abstraction only remembers two pieces of information: indiscernability between modes (when they are guaranteed to generate different observations) and ephemerality (when the system cannot stay forever in a given set of modes). Then, we use standard discrete event system diagnosability algorithms. The second contribution is an iterative approach to diagnosability that starts from the most abstract discrete event model of the hybrid system. If it is diagnosable, that means that the hybrid system is diagnosable. If it is not, the counterexample generated by the diagnosability procedure is analysed to refine the DES. If no refinement is found, then it can not be proved that the hybrid system is diagnosable. Otherwise, the refinement is included in the abstract DES model and the diagnosability procedure continues.

Diagnosability improvement of dynamic clustering through automatic learning of discrete event models

This paper deals with the problem of improving data-based diagnosis of continuous systems taking advantage of the system control information represented as discrete event dynamics. The approach starts from dynamic clustering results and, combining the information about operational modes, automatically generates a discrete event system that improves clustering results interpretability for decision-making purposes and enhances fault detection capabilities by the inclusion of event related dynamics. The generated timed discrete event system is adaptive thanks to the dynamic nature of the clusterer from which it was learned, namely DyClee. The timed discrete event system brings valuable temporal information to distinguish behaviors that are non-diagnosable based solely on the clustering itself.

A stochastic model to predict occupants’ activities at home for community-/urban-scale energy demand modelling

This paper proposes a stochastic discrete-event model to predict occupants’ activities at home to be used in community-/urban-scale energy demand modelling. The model is designed to consider interactions among household members and generate consistent behaviour across simulated days and times of day with specific time-dependent characteristics. Routine behaviours undertaken routinely everyday such as working and eating meals are placed on the timeline while considering interactions among household members. Gaps between routine behaviours are then filled by non-routine behaviours. Individual specificity and consistency are enhanced by (1) providing input data representing intrapersonal variation instead of interpersonal variation, (2) using relative time to time of routine behaviours, instead of clock time and (3) adjusting the input dataset affecting the selection of non-routine behaviours and their duration. Case studies demonstrated that interactions among household members are reproduced as observed in empirical data and the individual specificity and consistency are enhanced.

Evaluation of a Stratified National Breast Screening Program in the United Kingdom: An Early Model-Based Cost-Effectiveness Analysis.

To identify the incremental costs and consequences of stratified national breast screening programs (stratified NBSPs) and drivers of relative cost-effectiveness. A decision-analytic model (discrete event simulation) was conceptualized to represent four stratified NBSPs (risk 1, risk 2, masking [supplemental screening for women with higher breast density], and masking and risk 1) compared with the current UK NBSP and no screening. The model assumed a lifetime horizon, the health service perspective to identify costs (£, 2015), and measured consequences in quality-adjusted life-years (QALYs). Multiple data sources were used: systematic reviews of effectiveness and utility, published studies reporting costs, and cohort studies embedded in existing NBSPs. Model parameter uncertainty was assessed using probabilistic sensitivity analysis and one-way sensitivity analysis. The base-case analysis, supported by probabilistic sensitivity analysis, suggested that the risk stratified NBSPs (risk 1 and risk-2) were relatively cost-effective when compared with the current UK NBSP, with incremental cost-effectiveness ratios of £16,689 per QALY and £23,924 per QALY, respectively. Stratified NBSP including masking approaches (supplemental screening for women with higher breast density) was not a cost-effective alternative, with incremental cost-effectiveness ratios of £212,947 per QALY (masking) and £75,254 per QALY (risk 1 and masking). When compared with no screening, all stratified NBSPs could be considered cost-effective. Key drivers of cost-effectiveness were discount rate, natural history model parameters, mammographic sensitivity, and biopsy rates for recalled cases. A key assumption was that the risk model used in the stratification process was perfectly calibrated to the population. This early model-based cost-effectiveness analysis provides indicative evidence for decision makers to understand the key drivers of costs and QALYs for exemplar stratified NBSP.

Observer-Based Controllers for Max-Plus Linear Systems

Max-plus algebra is a suitable algebraic setting to model discrete event systems involving synchronization and delay phenomena which are often found in transportation networks, communications systems, and manufacturing systems. One way of controlling this kind of systems consists in choosing the dates of input events in order to achieve the desired performances, e.g., to obtain output events in order to respect given dates. This kind of control is optimal, according to a just-in-time criterion, if the input-event dates are delayed as much as possible while ensuring the output events to occur before a desired reference date. This paper presents the observer-based controller for max-plus linear systems where only estimations of system states are available for the controller. As in the classical sense, this is a state-feedback control problem, which is solved in two steps: first, an observer computes an estimation of the state by using the input and the output measurements, then, this estimated state is used to compute the state-feedback control action. As a main result, it is shown that the optimal solution of this observer-based control problem leads to a greater control input than the one obtained with the output feedback strategy. A high throughput screening example in drug discovery illustrates this main result by showing that the scheduling obtained from the observer-based controller is better than the scheduling obtained from the output feedback controller.

Fast and Stable Signal Deconvolution via Compressible State-Space Models.

Common biological measurements are in the form of noisy convolutions of signals of interest with possibly unknown and transient blurring kernels. Examples include EEG and calcium imaging data. Thus, signal deconvolution of these measurements is crucial in understanding the underlying biological processes. The objective of this paper is to develop fast and stable solutions for signal deconvolution from noisy, blurred, and undersampled data, where the signals are in the form of discrete events distributed in time and space. We introduce compressible state-space models as a framework to model and estimate such discrete events. These state-space models admit abrupt changes in the states and have a convergent transition matrix, and are coupled with compressive linear measurements. We consider a dynamic compressive sensing optimization problem and develop a fast solution, using two nested expectation maximization algorithms, to jointly estimate the states as well as their transition matrices. Under suitable sparsity assumptions on the dynamics, we prove optimal stability guarantees for the recovery of the states and present a method for the identification of the underlying discrete events with precise confidence bounds. We present simulation studies as well as application to calcium deconvolution and sleep spindle detection, which verify our theoretical results and show significant improvement over existing techniques. Our results show that by explicitly modeling the dynamics of the underlying signals, it is possible to construct signal deconvolution solutions that are scalable, statistically robust, and achieve high temporal resolution. Our proposed methodology provides a framework for modeling and deconvolution of noisy, blurred, and undersampled measurements in a fast and stable fashion, with potential application to a wide range of biological data.

Discrete-event diagnostic model for a distributed computational system. Merging chains

We consider synthesis problems for a discrete event model in a real time distributed computational system. The model reflects information exchange events between software units of the system and is intended to construct special test sequences that detect faults in these exchanges. We propose a version of the model that allows for a reduction in the relative volume of diagnostic information transmitted to diagnostic tools.