Discrete Event Processes Research Articles

Analyzing a class of stochastic SIRS models under imperfect vaccination

This work is devoted to a class of stochastic infectious disease models, namely, stochastic SIRS models under imperfect vaccination. Because of the inclusion of the vaccination, the underlying models are more difficult to analyze than that of the previously considered in the literature. Our main effort is devoted to overcoming the difficulties and deriving a complete characterization of longtime behavior of systems. Numerical examples are provided to present computational evidence. These examples also provide insight that the discrete event process (the random switching) can reverse persistence to extinction, and vice versa.

Splitting Long Event Sequences Drawn from Cyclic Processes for Discovering Workflow Nets

This paper addresses the preprocessing of event sequences issued from cyclic discrete event processes, which perform activities continuously whose delimitation of jobs or cases is not explicit. The sequences include several occurrences of the same events due to the iterative behaviour, such that discovery methods conceived for workflow nets (WFN) cannot process such sequences. In order to handle this issue, a novel technique for splitting a set of long event traces S = {Sk} (|S| ≥ 1) exhibiting the behaviour of cyclic processes is presented. The aim of this technique is to obtain from S a log λ = {σi} of event traces representing the same behaviour, which can be processed by methods that discover WFN. The procedures derived from this technique have polynomial-time complexity.

Automated modelling of discrete-event processes. Discovering Petri nets including silent transitions by classifying event traces

ABSTRACT This paper addresses the problem of automated modelling of discrete-event processes from event traces that capture the process behaviour. A novel method for building Petri nets (PN) that include silent transitions is presented. The method discovers workflow nets (WFN), a class of PN, from logs of event traces λ. The proposed approach is based on a classification of traces such that λ is partitioned into four classes: normal traces (λN), which do not involve the firing of silent transitions; skip traces (λS), redo traces (λR), and switch traces (λSW), which involve the firing of skip, redo, and switch type silent transitions, respectively. Afterwards, traces in λN are used to build a base WFN N, which may include silent transitions of type initialise and finalise. Then, N is refined by adding the silent transitions corresponding to the classes oftraces λS, λR, and λSW. The algorithms of the method have polynomial-time complexity; implementation and tests using artificial event logs are presented.

Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes.

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network's universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.

Logistical Simulation Modeling for Planning a Soil Remediation Process

This paper describes the development of a discrete event simulation model using the FlexSim software to support planning for soil remediation at Korean nuclear power plants that are undergoing decommissioning. Soil remediation may be required if site characterization shows that there has been radioactive contamination of soil from plant operations or the decommissioning process. The simulation model was developed using a dry soil separation and soil washing process. Preliminary soil data from the Kori 1 nuclear power plant was used in the model. It was shown that a batch process such as soil washing can be effectively modeled as a discrete event process. Efficient allocation of resources and efficient waste management including volume and classification reduction can be achieved by use of the model for planning the soil remediation process. Cost will be an important criterion in the choice of suitable technologies for soil remediation but is not included in this conceptual model.

Development and application of a neural network based coating weight control system for a hot-dip galvanizing line

The hot-dip galvanizing line (HDGL) is a typical order-driven discrete-event process in steelmaking. It has some complicated dynamic characteristics such as a large time-varying delay, strong nonlinearity, and unmeasured disturbance, all of which lead to the difficulty of an online coating weight controller design. We propose a novel neural network based control system to solve these problems. The proposed method has been successfully applied to a real production line at VaLin LY Steel Co., Loudi, China. The industrial application results show the effectiveness and efficiency of the proposed method, including significant reductions in the variance of the coating weight and the transition time.

Toward Improving Surgical Outcomes by Incorporating Cognitive Load Measurement into Process-Driven Guidance.

This paper summarizes the accomplishments and recent directions of our medical safety project. Our process-based approach uses a detailed, rigorously-defined, and carefully validated process model to provide a dynamically updated, context-aware and thus, "Smart" Checklist to help process performers understand and manage their pending tasks [7]. This paper focuses on support for teams of performers, working independently as well as in close collaboration, in stressful situations that are life critical. Our recent work has three main thrusts: provide effective real-time guidance for closely collaborating teams; develop and evaluate techniques for measuring cognitive load based on biometric observations and human surveys; and, using these measurements plus analysis and discrete event process simulation, predict cognitive load throughout the process model and propose process modifications to help performers better manage high cognitive load situations. This project is a collaboration among software engineers, surgical team members, human factors researchers, and medical equipment instrumentation experts. Experimental prototype capabilities are being built and evaluated based upon process models of two cardiovascular surgery processes, Aortic Valve Replacement (AVR) and Coronary Artery Bypass Grafting (CABG). In this paper we describe our approach for each of the three research thrusts by illustrating our work for heparinization, a common subprocess of both AVR and CABG. Heparinization is a high-risk error-prone procedure that involves complex team interactions and thus highlights the importance of this work for improving patient outcomes.

Resilient architecture for cyber-physical production systems

Production systems are a typical example of cyber-physical systems (CPS) in which a variety of machines, actuators, sensors and control systems are interwoven to produce products as efficiently as possible. Even though sophisticated condition monitoring systems are deployed, stoppage, breaks, and other types of failures still happen. To avoid catastrophic operational disruptions, desirably the production system itself resiliently and autonomously responses to failures. This paper reports a design method for a resilient architecture of a cyber-physical production system that can deal with disturbances and failures in a discrete-event process. A physical demonstrator was built to demonstrate its reconfiguration capabilities.

Introducing a new mine ventilation design method by integrating discrete-event mining process simulation and ventilation simulation

Introducing a new mine ventilation design method by integrating discrete-event mining process simulation and ventilation simulation

Structure and Randomness of Continuous-Time, Discrete-Event Processes

Loosely speaking, the Shannon entropy rate is used to gauge a stochastic process' intrinsic randomness; the statistical complexity gives the cost of predicting the process. We calculate, for the first time, the entropy rate and statistical complexity of stochastic processes generated by finite unifilar hidden semi-Markov models---memoryful, state-dependent versions of renewal processes. Calculating these quantities requires introducing novel mathematical objects ({\epsilon}-machines of hidden semi-Markov processes) and new information-theoretic methods to stochastic processes.

Discovering Petri Net Models of Discrete-Event Processes by Computing T-Invariants

This paper addresses the problem of discovering a Petri Net (PN) from a long event sequence representing the behavior of discrete-event processes. A method for building a 1-bounded PN able to execute the events sequence $S$ is presented; it is based on determining causality and concurrence relations between events and computing the t-invariants. This novel method determines the structure and the initial marking of an ordinary PN, which reproduces the behavior in $S$ . The algorithms derived from the method are efficient and have been implemented and tested on numerous examples of diverse complexity. Note to Practitioners —Model discovery is useful to perform reverse engineering of ill-known systems. The algorithms proposed in this paper build 1-bounded PN models, which are enough powerful to describe many discrete-event processes from industry. The efficiency of the method allows processing very large sequences. Thus, an automated modeling tool can be developed for dealing with data issued from real systems.

Scheduling and cost estimation simulation for transport and installation of floating hybrid generator platform

Concerns such as global warming, depletion of fossil fuels, and the dangers associated with the nuclear energy have led to the research and development of offshore wind and wave energy sources. However, the transportation and installation (T&I) planning for offshore energy platform is still being conducted in an unmanaged manner. In this paper, the T&I process of floating hybrid generator platforms is investigated. The schedule and cost estimation are simulated as a discrete event process, an alternative T&I process is evaluated, and the optimum T&I plan (with respect to the shortest duration or the lowest cost) is identified using various techniques.

Multi-level Modeling and Simulation of Manufacturing Systems for Lightweight Automotive Components

Hybrid lightweight parts aim at improving the economical and ecological performance of automobiles by reducing weight and, consequently, CO2 emissions. The environmental advantageousness requires careful attention during the car's design phase to prevent problem shifting from use phase into production. Due to the degree of novelty of hybrid components, reliable data about energy and resource demands in production is not yet available. This work presents a multi-level simulation framework for coupling models from different disciplines in order to derive LCA-relevant data. Exemplarily, a discrete-event process chain simulation is connected with a physical process model for a forming process.

Modelling and Simulation of a Batch Poly(Vinyl Chloride) Reactor

A population balance model for batch suspension polymerization of vinyl chloride is presented. Break up into two equal volumes of droplets, binary aggregation and random mass exchanges between the colliding droplets are assumed, while the kinetic data of vinyl chloride polymerization are taken from the literature. The model and the process are analyzed by simulation applying a novel Monte Carlo method taking into account breakage and aggregation of droplets as well as micromixing induced by droplet binary collisions as stochastic discrete event processes. The results revealed that this method of modelling describes phenomena in suspension polymerization of vinyl chloride properly.

Linear optimal control strategies for production systems with a discrete-event demand pattern

A class of production systems are considered in this paper with the aim of determining closed-loop strategies providing the production effort as functions of the system state. The key feature of the considered class of systems is that the demand and the way it is satisfied are asynchronous sequences of part requests and instantaneous withdrawals, respectively, generated by discrete-event processes, whereas the production process has continuous-time dynamics. The optimization problem, whose objective is to minimize inventory and lateness costs, is restated as an optimal control problem by exploiting some structural properties of the optimal solution. Then, optimal closed-loop strategies are found with dynamic programming techniques.

Specification, construction, and exact reduction of state transition system models of biochemical processes.

Biochemical reaction systems may be viewed as discrete event processes characterized by a number of states and state transitions. These systems may be modeled as state transition systems with transitions representing individual reaction events. Since they often involve a large number of interactions, it can be difficult to construct such a model for a system, and since the resulting state-level model can involve a huge number of states, model analysis can be difficult or impossible. Here, we describe methods for the high-level specification of a system using hypergraphs, for the automated generation of a state-level model from a high-level model, and for the exact reduction of a state-level model using information from the high-level model. Exact reduction is achieved through the automated application to the high-level model of the symmetry reduction technique and reduction by decomposition by independent subsystems, allowing potentially significant reductions without the need to generate a full model. The application of the method to biochemical reaction systems is illustrated by models describing a hypothetical ion-channel at several levels of complexity. The method allows for the reduction of the otherwise intractable example models to a manageable size.

Specification, Construction, and Exact Reduction of Finite State Transition System Models of Biochemical Processes

Biochemical reaction systems may be viewed as discrete event processes characterized by a number of states and state transitions. These systems may be modeled as state transition systems with transitions representing individual reaction events. Since they often involve a large number of interactions, it can be difficult to construct such a model for a system, and since the resulting state-level model can involve a huge number of states, model analysis can be difficult or impossible. Here, we describe methods for the high-level specification of a system using hypergraphs, for the automated generation of a state-level model from a high-level model, and for the exact reduction of a state-level model using information from the high-level model. Exact reduction is achieved through the automated application of symmetry reduction and invariant manifold reduction techniques to the high-level model, allowing potentially significant reductions without the need to generate a full model. The application of the method to biochemical reaction systems is illustrated by models describing a hypothetical ion-channel at several levels of complexity. The method allows for the reduction of the otherwise intractable example models to a manageable size.

Modelling of heat exchange between drops in suspension polymerization of vinyl chloride

A population balance model is presented for suspension polymerization of vinyl chloride in a batch reactor to investigate the thermal properties of the system. Reactions are described using a simplified reaction model focusing on the heat generation of the highly exothermic polymerization reactions. The temperature of the continuous phase is assumed to be homogeneous over the reactor and the effects of some temperature rise of droplets due to the exothermic polymerization reactions and the possible heat exchange because of the coalescence/redispersion process are analysed. The population balance equation is solved applying a Monte Carlo method by coupling the deterministic polymerization reactions inside the droplets with the discrete event process induced by collisions of droplets. The results obtained by simulation show that rise in the temperature of droplets over the mean temperature of continuous phase lead to acceleration of the process. The possible size distribution of droplets and no smooth distribution of the initiator in those decrease the process efficiency.

Inventory optimization of distribution networks with discrete-event processes by vendor-managed policies

AbstractThe objective of this work is to study optimal Vendor-Managed Inventory policies in distributions systems in which demand and delivery processes are characterized by a discrete-event dynamics. In Vendor-Managed Inventory policies, the supplier organizes the deliveries to the customer in order to satisfy its demand by minimizing transportation costs and holding costs of the customer. In the considered case, a simple network composed of one supplier and one customer is studied, in which the supplier can use a limited number of capacitated vehicles; the objective is to plan the delivery policy over a finite horizon. The optimization problem resulting from the considered system characterized by discrete-event processes is then rewritten in a discrete-time form. For this problem different solution methods are proposed in the paper, based on the statement of mathematical programming problems and the definition of appropriate algorithms. These methods are then compared through some experimental results.

Bifurcation theory, adaptive dynamics and dynamic energy budget-structured populations of iteroparous species

In this paper, we describe a technique to evaluate the evolutionary dynamics of the timing of spawning for iteroparous species. The life cycle of the species consists of three life stages, embryonic, juvenile and adult whereby the transitions of life stages (gametogenesis, birth and maturation) occur at species-specific sizes. The dynamics of the population is studied in a semi-chemostat environment where the inflowing food concentration is periodic (annual). A dynamic energy budget-based continuous-time model is used to describe the uptake of the food, storage in reserves and allocation of the energy to growth, maintenance, development (embryos, juveniles) and reproduction (adults). A discrete-event process is used for modelling reproduction. At a fixed spawning date of the year, the reproduction buffer is emptied and a new cohort is formed by eggs with a fixed size and energy content. The population consists of cohorts: for each year one consisting of individuals with the same age which die after their last reproduction event. The resulting mathematical model is a finite-dimensional set of ordinary differential equations with fixed 1-year periodic boundary conditions yielding a stroboscopic map. We will study the evolutionary development of the population using the adaptive dynamics approach. The trait is the timing of spawning. Pairwise and mutual invasibility plots are calculated using bifurcation analysis of the stroboscopic map. The evolutionary singular strategy value belonging to the evolutionary endpoint for the trait allows for an interpretation of the reproduction strategy of the population. In a case study, parameter values from the literature for the bivalve Macoma balthica are used.