Discrete-event Model Research Articles

Discrete-event modelling of the capacity of the toll collection exit point and the formation of congestion

Discrete-event modelling of the capacity of the toll collection exit point and the formation of congestion

Discrete-event modelling of the capacity of the toll collection exit point and the formation of congestion

Discrete-event modelling of the capacity of the toll collection exit point and the formation of congestion

Online detection of failures generated by storage simulator

Modern large-scale data-farms consist of hundreds of thousands of storage devices that span distributed infrastructure. Devices used in modern data centers (such as controllers, links, SSD- and HDD-disks) can fail due to hardware as well as software problems. Such failures or anomalies can be detected by monitoring the activity of components using machine learning techniques. In order to use these techniques, researchers need plenty of historical data of devices in normal and failure mode for training algorithms. In this work, we challenge two problems: 1) lack of storage data in the methods above by creating a simulator and 2) applying existing online algorithms that can faster detect a failure occurred in one of the components.We created a Go-based (golang) package for simulating the behavior of modern storage infrastructure. The software is based on the discrete-event modeling paradigm and captures the structure and dynamics of high-level storage system building blocks. The package's exible structure allows us to create a model of a real-world storage system with a configurable number of components. The primary area of interest is exploring the storage machine's behavior under stress testing or exploitation in the medium-or long-term for observing failures of its components.To discover failures in the time series distribution generated by the simulator, we modified a change point detection algorithm that works in online mode. The goal of the change-point detection is to discover differences in time series distribution. This work describes an approach for failure detection in time series data based on direct density ratio estimation via binary classifiers.

Maintaining biodiversity promotes the multifunctionality of social-ecological systems: holistic modelling of a mountain system

Monitoring the provision of multiple ecosystem services (ES) in social-ecological systems is a major challenge. Most tools usually tackle the problem by modelling individual ES, but do not perform a holistic analysis of a dynamic and integrated system. We developed a discrete-event model (DORIAN) and explored its potential for assessing biodiversity and multifunctionality of a mountain ski resort subjected to a changing climate. We represented this social-ecological system as a network comprising 16 binary components and 51 processes that define component interactions. We identified 22 economy- and ecology-related ES, depending on the presence/absence of components. We simulated six scenarios representing different economic, environmental and climatic situations and calculated a score (the sum of proxies for biodiversity or ES), corresponding to the level of biodiversity and multifunctionality. Results showed that climate change reduced the system’s multifunctionality and increased the number of degraded states, as well as the trajectories from healthy to degraded states. With increasing levels of biodiversity, only ecology-related ES were boosted at low biodiversity levels, while both high levels of ecology- and economy-related ES were maintained at high biodiversity levels. This result demonstrates the importance of conserving high biodiversity in a social ecological system, for an optimal “biodiversity – multifunctionality” win-win strategy.

A DISCRETE STOCHASTIC MODEL TO DETERMINE FOOD QUALITY BY USING SIMULATION: A STRAWBERRY CASE STUDY

ABSTRACT The agri-food supply chain (AFSC) sets a series of challenges in the production, storage, distribution, and transportation processes, as the market's requirements for fresh food combine food quality and safety attributes. This research suggests a discrete event modeling to study the behavior of food quality in all supply chains (SCs) in terms of temperature and time variables in a stochastic manner. The ProModel software was used to simulate strawberry quality in the SC, considering a distribution route to export the fruit from Mexico to the United States. The suggested model together with a computer simulation analyzes the fruit quality from two perspectives: with and without a cold chain (CC). The obtained results revealed that in the case of CC usage, when consumed the strawberry's average quality was 85%. In the approach without CC, the results revealed that when the strawberry arrives and remains in the distribution center, it loses more than 40% of its quality. This proposal may help those involved in the AFSC to make decisions that allow them to maximize quality and profit, minimize waste, improve distribution routes, and control stock levels.

Discrete-event models for conservation assessment of integrated ecosystems

Ecosystems are complex and data-intensive systems, and the ecologists still struggle to understand them in an integrated manner. Models that miss key dynamics can possibly lead to fallacious conclusions about the ecosystem fate. To address these limits and encompass whole and realistic ecosystems, we develop here a qualitative model with the help of discrete-event models. This model, based on formal Petri nets, was able to integrate biotic, abiotic and human-related components (e.g. grazing) along with their processes into the same interaction network. The model was also able to grasp ecosystem development, as defined by sharp changes of the interaction network structure itself. Furthermore, the model was possibilistic and thus rigorously computed all possible ecosystem states reached after a specific (present-day) initial state. This innovative approach in ecology then allows to rigorously and exhaustively identifying all possible ecosystem trajectories and to study their impacts and outcomes. For the first time in a realistic ecosystem, we illustrated such discrete and qualitative models in the case study of temporary marshes in the Mediterranean part of France, the Camargue delta. The model demonstrated that when marshes are exposed to extensive grazing the presence of marsh heritage species (i.e. with a conservation value) is facilitated by opening up the vegetation through various trajectories. This supports the commonly used management practices of extensive grazing to conserve certain protected habitats. The detailed analysis of the computed ecosystem trajectories allows exploring a range of recommendations for management strategies.

A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic.

Several research and development teams around the world are working towards COVID-19 vaccines. As vaccines are expected to be developed and produced, preparedness and planning for mass vaccination and immunization will become an important aspect of the pandemic management. Mass vaccination has been used by public health agencies in the past and is being proposed as a viable option for COVID-19 immunization. To be able to rapidly and safely immunize a large number of people against SARS-CoV-2, different mass vaccination options are available. Drive-through facilities have been successfully used in the past for immunization against other diseases and for testing during COVID-19. In this paper we introduce a drive-through vaccination simulation tool that can be used to enhance the planning, design, operation, and feasibility and effectiveness assessment of such facilities. The simulation tool is a hybrid model that integrates discrete event and agent-based modeling techniques. The simulation outputs visually and numerically show the average processing and waiting times and the number of cars and people that can be served (throughput values) under different numbers of staff, service lanes, screening, registration, immunization, and recovery times.

Computational approach for researching visitor flow dynamics at public venues and mass gathering events

Visitor flow dynamics at public venues has been studied by using mathematical models of the non-stationary queuing system and discrete-event modelling. Computational approach for research visitor flows quantitative characteristics is described. The dependence of input rate from time in non-stationary queuing system model is like the dependencies of rate of visitors to football matches. Quantitative characteristics are waiting line length and waiting time (time spend in queue) from time for different parameters of the input rate and the service rate device. It is proven that the maximum values of the studied characteristics and the corresponding time values are described by deterministic functions that depend on the maximum intensity of visitor flow and the average service speed. The form of these functions is well described.

The digital twin of the business process model

In this paper, we propose a solution to the problem of organizational management based on high-precision simulation models designed for numerical forecasting of the parameters of business process models - duration, labor intensity and resource intensity, cost. The solution is implemented as a prototype twin of the business process system model. To solve the problem of predicting the numerical indicators of a business process model, the formula and algorithmic apparatus of stochastic GERT networks is used. BPMS ELMA was used as a technical platform for the implementation of a digital twin prototype. To solve this problem, the apparatus of stochastic GERT networks is used. An edge of the GERT network is associated with the operation of the business process, and the node of the GERT network with an event or gateway of the business process. An algorithm for translating a model of a business process into an equivalent GERT network, as well as calculating the parameters of the law of distribution of the cost of a business process based on the obtained GERT network, is given. The proposed tool, implemented in the digital twin modeling module, allows solving the problems of predicting the dynamics of discrete-event models described in BPMN notation without using statistical simulation models. Continuous updating of the structure of the GERT-model in accordance with the modification of the process in the ELMA system and clarification of the parameters of probabilities, the type of distribution laws and their parameters based on the accumulated data of the digital shadow of the business process allows us to talk about the implementation of a high-precision forecasting model of the business process in the proposed digital twin model.

A Discrete-Event Simulation of Claude Lévi-Strauss’ Structural Analysis of Myths Based on Symmetry and Double Twist Transformations

This paper presents a modeling and simulation approach in order to perform a generative analysis of folktales aimed at validating Claude Lévi-Strauss’ theory and method. To this aim, a discrete-event simulation is proposed. The simulation is based on the development of a set of discrete-event models dedicated to generating a set of folktales from an initial one, according to Claude Lévi-Strauss’ structural analysis based on symmetry and double twist transformations. This paper describes in detail how these discrete-event models have been implemented in the framework of the DEVSimPy software environment by using myths of Native American mythology and folktales of Corsican oral literature. The validation involved the following steps: (i) definition of a reference folktale (according to Claude Lévi-Strauss’ methodology) (ii) generation of a set of folktales by performing their own transformations (iii) generation of a graph allowing to analyze the links that have been created after performing a set of folktales transformations. Finally, the computational validation of Lévi-Strauss’s method is intended to ground a new research that may reformulate structural analysis and elaborate a neo-structural model of canonical formalization based on transformational morphodynamics. The aim is to conceptualize and measure recursively the structural dynamics and the recurrent patterns of current identity transformations in liberal democracies, especially in US and EU contexts where ethnic/racial divisions and migration challenges are becoming more acute than ever.

Investigation of the Effect of e-Platform Information Security Breaches: A Small and Medium Enterprise Supply Chain Perspective

Many small and medium enterprises (SMEs) engage in dyadic information integration partnerships or partial integration with their direct suppliers and customers. They often utilize e-commerce or cloud computing technology platforms hosted by third-party providers to leverage such partnerships. However, information security breaches and disruptions caused by cyberattacks are commonplace in the information technology industry. The effects of said disruptions and breaches on e-commerce businesses under varied disruption conditions are still uncertain. Furthermore, the effect of security breaches on nonparticipating members of the supply chain is poorly understood, especially under various disruption profiles. Using discrete event modeling, in this article, we explore the impact of disruption caused by information security breaches on supply chain performance and the externality effect of partial integration on nonparticipants. We also examine the impact of breach disruption frequency and remediation length on supply chain performance with varying levels of information sharing. These impacts were studied under two typical inventory replenishment policies for SMEs. It was determined that remediation length should be a prioritized factor in impact management and that flexibility in the inventory replenishment policy can help mitigate the impact of information disruption on the inventory performance of businesses, especially that of nonparticipants, in information-sharing partnerships.

Process Mining Contributions to Discrete-event Simulation Modelling

Abstract Background: Over the last 20 years, process mining has become a vibrant research area due to the advances in data management technologies and techniques and the advent of new process mining tools. Recently, the links between process mining and simulation modelling have become an area of interest. Objectives: The objective of the paper was to demonstrate and assess the role of process mining results as an input for discrete-event simulation modelling, using two different datasets, one of which is considered data-poor while the other one data-rich. Methods/Approach: Statistical calculations and process maps were prepared and presented based on the event log data from two case studies (smart mobility and higher education) using a process mining tool. Then, the implications of the results across the building blocks (entities, activities, control-flows, and resources) of simulation modelling are discussed. Results: Apart from providing a rationale and the framework for simulation that is more efficient modelling based on process mining results, the paper provides contributions in the two case studies by deliberating and identifying potential research topics that could be tackled and supported by the new combined approach. Conclusions: Event logs and process mining provide valuable information and techniques that could be a useful input for simulation modelling, especially in the first steps of building discreteevent models, but also for validation purposes.

A control strategy for cylinder deactivation

Cylinder deactivation is an effective measure to reduce the fuel consumption of internal combustion engines. This paper presents a control strategy for the cylinder deactivation (CDA) process and the cylinder reactivation (CRA) process, based on the inversion of a control-oriented discrete-event model. The focus of this paper lies on quantity-controlled stoichiometrically-operated engines with different layouts. Nevertheless, the main results are transferable to other engines, including quality-controlled engines. Two major aspects of CDA and CRA are considered in detail in this paper.The first aspect is the cycle-averaged torque during the CDA and CRA. Due to the reciprocating behaviour of the engine, an unavoidable discontinuity in the cycle-averaged torque is identified, irrespective of the type of engine. The amplitude of this torque ripple depends on the duration of the CDA or CRA and can be quantified a priori. A trade-off between the torque ripple amplitude and the CDA or CRA duration is identified.The second aspect is the amount of fuel consumed during the CDA and CRA. It is shown that to achieve a CDA or CRA with a limited torque ripple amplitude, some combustion cycles at unfavourable low-load conditions must occur. For example, ignition retardation might be used resulting in a significant portion of the fuel energy being lost. As a result an increase in the amount of fuel consumed during the CDA or CRA compared to that of continued operation in activated cylinder mode may arise. The conducted investigations show that this increase in fuel consumption is compensated after a few seconds of operation in the more fuel-efficient deactivated cylinder mode.

Digitalization of Multi-Object Technological Projecting in Terms of Small Batch Production

Decreasing the share of time for the technological training of new product creation is an actual and perspective task. The actuality of the given task always increases with the development of machine-building production. Changes which happen inside the organization of industrial production are connected to the development of consumer demand. Modern tendencies are such that there can be seen a constant lowering of batch products with an increase in the range of their production, and, consequently, a reduction in the production stage of the product life cycle. Under these conditions, it is necessary to react as quickly as possible to changes of external (for example, the arrival of new production orders, adjusting the number of already started production, etc.) and internal conditions (for example, stopping technological equipment for organizational reasons, correcting production routes for parts, etc.). Obviously, the effectiveness of the functioning of the production system will be determined primarily by the right decisions. Errors lead to large material costs and a serious loss of time, which can be fatal for an enterprise. The development goal described in the article is to build a digital model of a production system that allows—on a machine time scale and in the absence of material costs—the analysis of various control scenarios and finding correct solutions in in short time. The methodological basis for constructing such models is the use of simulation modeling with the combination of discrete event and agent modeling. The construction of a model on this principle allows its openness to accumulate information about successful decisions made and use them to solve similar problems in the future. The benefit of this approach is the ability to predict the production situation over time, rational distribution of technological resources, reducing equipment downtime, streamlining the routes of production tasks and determining the stages of acquiring the necessary materials and components. It increases the competitiveness of the enterprise and ensures its economic stability.

Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare

Agent-based approaches have been known to be appropriate as systems and methods in medical administration in recent years. The increased attention to processes led to the recent growth of Business Process Management discipline, which quite exclusively adopt discrete-event modeling and simulation. This paper proposes a medical agent-oriented decision support system to integrate the achievements from management science, agent-based modeling, and artificial intelligence. In particular, we performed a practical application concerning a hospital emergency department medical system. We adopt the widely used multi-agent programmable modeling environment NetLogo. First, we demonstrated the ability to perform a clear representation of healthcare processes where agents (i.e., patients and hospital staff) operate in a 3D environment. This model allows performing a traditional what-if scenario analysis. Second, we explore how performing intelligent management of patients by applying genetic algorithms to find the criteria for the selection process of the subjects in the admission procedure. The results are encouraging towards a more extensive application of agent-oriented methodologies in healthcare management.

Model-checking precision agriculture logistics: the case of the differential harvest

The development, in the last decades, of technologies for precision agriculture allows the acquisition of crop data with a high spatial resolution. This offers possibilities for innovative control and raises new logistics issues that may be solved using discrete event models. In vineyards, some technologies make it possible to define zones with different qualities of grapes and sort the grapes at harvest to make different vintages. In this context, the Differential Harvest Problem (DHP) consists in finding a trajectory of the harvesting machine in the field in order to obtain at least a given quantity of higher quality grapes and minimising working time. In available literature, the DHP has been solved using Constraint Programming. In this paper, we investigate if it is possible to solve the DHP using the Cost Optimal Reachability Analysis feature of a model-checking tool such as UPPAAL-CORA. A model named DHP_PTA has been designed based on the priced timed automata formalism and the UPPAAL-CORA tool. The method made it possible to obtain the optimal trajectory of a harvesting machine for a vine plot composed of up to 14 rows. The study is based on real vineyard data. This paper is an extended version of a communication presented at WODES 2018 (Saddem-Yagoubi IFAC-PapersOnLine 51(7):57–63, 2018).

A discrete event model for analysis and verification of time‐constrained business processes

SummaryIn the purpose of analyzing and checking time constraints in business processes, we propose to model and simulate them in the “discrete event system specification” (DEVS) formalism. The proposed model will contain atomic DEVS models representing the activities, supervised by, other DEVS control models, and based on one to one mapping. Hence, guaranteeing a close concordance with the temporal requirements. Thus, the simulation of the model results in outputs about the successful execution regarding to the temporal constraints soundness. Therefore, the modeler will be able to predict unsuitable temporal behavior. Thus, the model responds to the lack of modeling tools that do not offer enough formal specification of time constraints at the earlier phases of design as in, the standard “business process management notation.” The proposed model handles different type of time constraints in business process such as intra‐activities, inter‐activities, and inter‐process time constraints. The proposed model is implemented into JAVADEVS using DEVS‐Suite platform.

SANgo: a storage infrastructure simulator with reinforcement learning support.

We introduce SANgo (Storage Area Network in the Go language)—a Go-based package for simulating the behavior of modern storage infrastructure. The software is based on the discrete-event modeling paradigm and captures the structure and dynamics of high-level storage system building blocks. The flexible structure of the package allows us to create a model of a real storage system with a configurable number of components. The granularity of the simulated system can be defined depending on the replicated patterns of actual system behavior. Accurate replication enables us to reach the primary goal of our simulator—to explore the stability boundaries of real storage systems. To meet this goal, SANgo offers a variety of interfaces for easy monitoring and tuning of the simulated model. These interfaces allow us to track the number of metrics of such components as storage controllers, network connections, and hard-drives. Other interfaces allow altering the parameter values of the simulated system effectively in real-time, thus providing the possibility for training a realistic digital twin using, for example, the reinforcement learning (RL) approach. One can train an RL model to reduce discrepancies between simulated and real SAN data. The external control algorithm can adjust the simulator parameters to make the difference as small as possible. SANgo supports the standard OpenAI gym interface; thus, the software can serve as a benchmark for comparison of different learning algorithms.

‘Turning the tide’ on hyperglycemia in pregnancy: insights from multiscale dynamic simulation modeling

IntroductionHyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies. Strategies to...

Discrete Event Systems Specifications Modelling and Simulation of Wireless Networking Applications

ABSTRACT We discuss the use of formal discrete-event modelling and simulation for networking applications, in particular, for mobile networks and Wireless Sensor Networks (WSN). We show how one can develop discrete-event model libraries based on the DEVS formalism for mobile networks, allowing tracking the users’ upload status in a given area of coverage, while using a non-cooperative algorithm or Coordinated Multipoint (CoMP) synchronisation. We show a DEVS model library for CoMP-based networks, showing how control messaging can be managed among Evolved Node Base stations (eNBs). We build a case study comparing two CoMP approaches: Joint Processing (JP) and Coordinated Scheduling (CS). Finally, we show how DEVS models can be used to model malware propagation in wireless sensor networks based on epidemic theory.