Discrete Event Research Articles

Safe Performance of an Industrial Autonomous Ground Vehicle in the Supervisory Control Framework

A Cyberphysical system, being an autonomous guided vehicle (AGV) and having diverse applications such as thematic parks and product transfer in manufacturing units, is modeled and controlled. The models of all subsystems of the AGV are provided in discrete event systems (DES) form following the Ramadge–Wonham (R–W) framework. The safe performance of the AGV, being the desired behavior of the system, is presented in the form of desired rules and translated into a set of regular languages. Then, the regular languages are realized as supervisory automata in the framework of Supervisory Control Theory (SCT). To ease implementation and coordination of the control architecture, the supervisors are designed to be in two-state automata forms. The controllability of the regular languages, regarding the AGV, will be proved, using the physical realizability (PR) of the synchronous product of the automata of the system and the supervisors. Also, the nonblocking property of all the controlled automata will be proven to be satisfied. Simulation of the controlled AGV will validate the proposed method.

Local stability of traffic equilibria in an isotropic network

For a static economic model of auto traffic in an isotropic zone, this paper classifies possible equilibria into three types, by whether traffic is hypercongested and by the relative slopes of “supply” and “demand” curves. We then conduct a local stability analysis of each type when density, demand and the unit travel time (inverse speed) evolve gradually and simultaneously according to dynamical systems of differential equations. Some hypercongested equilibria may be stable when demand adjusts quickly enough to congestion. Other hypercongested equilibria, which have counterintuitive comparative statics, are never stable. Non-hypercongested equilibria can be unstable under special circumstances. A discrete event simulation with a dynamic Poisson arrival process supports the results of the formal analysis.

Duality of controllability and observability in proportional equal conflict timed continuous Petri Nets

Controllability and observability properties have been widely studied in Timed Continuous Petri Nets (TCPNs), a class of piecewise affine systems, in order to analyze and control crowded discrete event systems. This work studies the concept of duality applied to TCPNs as a vehicle to establish links between controllability and observability, i.e., a synergy to improve the understanding of these properties and to enlarge the class of nets that can be analyzed. To achieve this, we study the concepts of rank-controllability and rank-observability. They capture structural conditions for controllability and observability. Afterwards, the computation of dual nets for Fork-Attribution (FA), Choice-Free (CF), Join-Free (JF), and Proportional Equal Conflict (PEQ) TCPNs subclasses are presented. By using the dual definition, several relations between the primal’s controllability and its dual’s observability are stated. Particularly, in FA rank-controllability and rank-observability are dual properties. In consistent and strongly connected CF, JF, and PEQ nets, the rank-observability of the dual is sufficient for the rank-controllability of the primal. The opposite implication holds for CF and PEQ if the self-loop places, added by the dual construction methodology, are measurable.

Assessment of Petri nets Regulation Control methodologies for automation systems

Industry 4.0 and smart manufacturing bring new interesting possibilities and challenges, a particular one is the large-scale automation with minimal set-up time and flexibility. To face this challenge, control approaches based on Discrete Event Systems (DES), such as Supervisory Control Theory (SCT), Generalized Mutual Exclusions Constraints (GMEC) and Petri net-based Regulation Control (RC), may provide convenient solutions; however, few works have been reported in the literature describing the application of these approaches. In this work, the RC approach based on Petri nets is applied to three case studies: (1) an electro-pneumatic cell controlled by a Programming Logic Controller device (PLC); (2) a simulated hydraulic process controlled by a PLC (a virtual commissioning scheme); (3) a simulated robotic manufacturing cell controlled by a software tool. In addition, an assessment of the existing methodologies in RC for modelling, specification, control synthesis and control implementation is conducted. The findings of this work provide three contributions: (1) a practical validation of the applied methodologies and the identification of required extensions; (2) a discussion about the validity of frequently considered assumptions and hypotheses in DES; (3) a reference for practitioners on the implementation of the RC approach, as well as its limitations and advantages.

The Next-Generation Ecosystem Experiment Arctic Rainfall Simulator: a tool to understand the effects of changing rainfall patterns in the Arctic

Abstract Rainfall frequency and intensity are expected to increase in the Arctic, with potential detrimental impacts on permafrost, leading to enhanced thawing and carbon release to the atmosphere. However, there have been very few studies on the effect of discrete rain events on permafrost in the Arctic and sub-Arctic. Conducting controlled rainfall experiments within permafrost landscapes can provide an improved understanding of the effect of changing intensity, duration, and timing of rain events on permafrost tundra ecosystems. Here, we describe the design and implementation of the Next-Generation Ecosystem Experiment Arctic Rainfall Simulator (NARS), a variable intensity (4–82 mm/h) rainfall simulator that can be used to study the effects of rainfall on permafrost stability. The NARS design includes a 3D-printed 4 cm H-flume and uses an eTape resistivity sensor that was calibrated (R2 = 0.9–0.96) to measure discharge from the system. NARS is designed to be lightweight, simple to construct, and can be easily deployed in remote locations. As a field validation of updated rainfall simulator design and modernized controls, NARS was tested on the Seward Peninsula, AK. Because of its portability, versatility in deployment, dimensions, and rainfall intensity, NARS represents a methodological innovation for researching the impacts of rainfall on permafrost environments.

Measuring gas discharge in contact electrification

Contact electrification in a gas medium is usually followed by partial surface charge dissipation caused by dielectric breakdown of the gas triggered during separation of the surfaces. It is widely assumed that such discharge obeys the classical Paschen’s law, which describes the general dependence of the breakdown voltage on the product of gas pressure and gap distance. However, quantification of this relationship in contact electrification involving insulators is impeded by challenges in nondestructive in situ measurement of the gap voltage. The present work implements an electrode-free strategy for capturing discrete discharge events by monitoring the gap voltage via Coulomb force, providing experimental evidence of Paschen curves governing nitrogen breakdown in silicone-acrylic and copper-nylon contact electrification. It offers an alternative approach for characterizing either the ionization energies of gases or the secondary-electron-emission properties of surfaces without the requirement of a power supply, which can potentially benefit applications ranging from the design of insulative materials to the development of triboelectric sensors and generators.

Modeling, analysis, and optimization of complex nuclear processes and facilities via computational methods: The HALEU process case study

Improving and adapting industrial systems to timely meet changing programmatic and market demands is an important goal to achieve, including when operating and maintaining complex nuclear processes and facilities. However, changes to these complex systems are costly, particularly when they are already in place and bounded to stringent requirements and constraints such as when handling radioactive material and contaminated equipment. These conditions often exist when treating spent nuclear fuel remotely within shielded nuclear radiation chambers, commonly referred as hot cells, to condition nuclear material and/or fabricate products for utilization in other nuclear enterprises such as in the manufacture of advanced nuclear fuel. The illustrative case considered here is the production of high assay low enriched uranium (HALEU) products supporting the deployment of advanced nuclear reactors. For the HALEU program, resources invested were and are being systematically analyzed so that these investments are maximized in a facility that is nearly 60 years old. A methodology that has effectively enabled optimized and improvements in the Spent Fuel Treatment (SFT) program, and consequently the HALEU program, involves discrete event simulation as addressed in this article. The quantification of multiple productivity metrics, including material processing rates, cycle times, bottlenecks, number of material transfers as well as equipment, workstation, and material handling utilization, has resulted in a myriad of diverse discoveries and data-informed decisions regarding process layout and constituent, labor levels and schedules, selection of new process units, storage needs, and other critical process configurations. This article describes such a computational capability being applied for decision-making, illustrates its application to an actual process and program, provides illustrative results, and argues how computational methods for the modeling, analysis, and optimization of complex processes and facilities does lead to informed decisions derived from data and not only from intuition.

Real-time optimization for relocation and dispatching of Emergency Medical Services with balanced workload and outsourced ride-hailing services

Real-time optimization of Emergency medical services (EMS) leads to excessive and unbalanced workload unless balance is directly modeled. Online redeployment policies aim to reduce response times, compensating for the coverage gap left by busy ambulances, however, it is important to account for the additional effort required to relocate to a new position. Additionally, irregular demand patterns result in unequal distributions of workload among the personnel. In this regard, we propose a new survival-based real-time optimization framework that maximizes the survival probability of patients suffering from life-threatening emergencies while maintaining a balanced workload among the crew. Our approach implements an optimization model inside a discrete event simulation model that feeds the former with real-time vehicle utilization metrics to maintain an optimal overall relocation policy. Moreover, we evaluate the impact on the system’s congestion of a mixed dispatched policy that includes external ride-hailing services for minor emergencies. Our case study in New York City indicates that the proposed model increases the average percentage of life-threatening emergencies responded in less than four minutes in more than 10% through the use of survival functions compared to a coverage model from the literature. Furthermore, even though the fleet’s aggregated driving distances increased compared to the coverage approach, our online model effectively balanced the additional workload among the crew. Additionally, ride-hailing services’ introduction presents an important opportunity for reducing personnel workload by 13.5%, on average, and operational costs in $141,220 per day of operation for the entire system. Finally, when ambulance availability is severely reduced, outsourced services significantly help maintaining the response level for low severity emergencies.

Generation of mathematical programming representations for discrete event simulation models of timed petri nets

This work proposes a mathematical programming (MP) representation of discrete event simulation of timed Petri nets (TPN). Currently, mathematical programming techniques are not widely applied to optimize discrete event systems due to the difficulty of formulating models capable to correctly represent the system dynamics. This work connects the two fruitful research fields, i.e., mathematical programming and Timed Petri Nets. In the MP formalism, the decision variables of the model correspond to the transition firing times and the markings of the TPN, whereas the constraints represent the state transition logic and temporal sequences among events. The MP model and a simulation run of the TPN are then totally equivalent, and this equivalence has been validated through an application in the queuing network field. Using a TPN model as input, the MP model can be routinely generated and used as a white box for further tasks such as sensitivity analysis, cut generation in optimization procedures, and proof of formal properties.

Speed Optimization in DEVS-Based Simulations: A Memoization Approach

The DEVS model, designed for general discrete event simulation, explores the event status and time advance of all DEVS atomic models deployed at the time of the simulation, and then performs the scheduled simulation step. Each simulation step is accompanied by a re-exploration the event status and time advance, which is needed for maintaining the casual order of the entire model. It is time consuming to simulate a large-scale DEVS model. In a similar vein, attempts to perform an HDL simulation in a DEVS space increase simulation costs by incurring repeated search costs for model transitions. In this study, we performed a statistical analysis of engine behavior to improve simulation speed and we proposed a DP-based memoization technique for the coupled model. Through our method, we can expect significant performance improvements that range statistically from 7.4 to 11.7 times.

A framework for modeling, generating, simulating, and predicting carbon dioxide dispersion indoors using cell-DEVS and deep learning

Carbon dioxide concentration in enclosed spaces is an air quality indicator that affects occupants’ well-being. To maintain healthy carbon dioxide levels indoors, enclosed space settings must be adjusted to maximize air quality while minimizing energy consumption. Studying the effect of these settings on carbon dioxide concentration levels is not feasible through physical experimentation and data collection. This problem can be solved by using validated simulation models, generating indoor settings scenarios, simulating those scenarios, and studying results. In previous work, we presented a formal Cellular Discrete Event System Specifications simulation model for studying carbon dioxide dispersion in rooms with various settings. However, designers may need to predict the results of altering large combinations of settings on air quality. Generating and simulating multiple scenarios with different combinations of space settings to test their effect on indoor air quality is time-consuming. In this research, we solve the two problems of the lack of ground truth data and the inefficiency of producing and studying simulation results for many combinations of settings by proposing a novel framework. The framework utilizes a Cellular Discrete Event System Specifications model, simulates different scenarios of enclosed spaces with various settings, and collects simulation results to form a data set to train a deep neural network. Without needing to generate all possible scenarios, the trained deep neural network is used to predict unknown settings of the closed space when other settings are altered. The framework facilitates configuring enclosed spaces to enhance air quality. We illustrate the framework uses through a case study.

Methodology for construction-standard-production-rate-based simulation modeling and production-rate data generation

ABSTRACT While implementing a construction project, production-rate assessment needs to be conducted throughout the different stages of the project because this assessment can determine the success or failure of the project. In South Korea, the construction-standard-production-rate (CSPR) is used for determining the production-rate of individual construction equipment. However, CSPR cannot provide the production-rate of activities considering a combination of more than two different types of equipment (eg, excavator and dump truck used for excavation-load-haul activities). Having ready-made data on such production-rates of different equipment combinations would be beneficial for determining and optimal fleet management plan. Furthermore, such data can be beneficial for implementing optimized scheduling methods using supervised-learning methods. Accordingly, simple discrete event simulation approaches were carried out for data generation and regression analysis for testing the feasibility of the usage of such data. The time inputs used in this study were calculated based on the equations and information given in the CSPR.

Evidence for Magnetic Reconnection as the Precursor to Discrete Aurora at Mars

AbstractDiscrete aurora at Mars are characterized as localized, short‐lasting ultraviolet emissions on the nightside. They are caused by the precipitation of accelerated electron along open magnetic field lines and their collision with the Martian atmosphere. Discrete auroral emissions detected by the Imaging Ultraviolet Spectrograph (IUVS) instrument onboard the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft occur most frequently over the strongest crustal magnetic anomalies at Mars and under particular local time and upstream interplanetary magnetic field (IMF) conditions. These trends suggest that the onset of discrete aurora is controlled by the processes that govern the interaction between the draped IMF and the crustal magnetic anomalies. Here, we analyze MAVEN magnetometer measurements over regions of strong crustal fields during 49 discrete auroral events observed by IUVS. Our results indicate that the draped IMF orientations during each discrete auroral event show a clear tendency to be anti‐parallel to the underlying crustal anomaly magnetic fields near the location of the emission onset. This suggests that magnetic reconnection, a process that favors anti‐parallel magnetic field regimes, between the crustal fields and the draped IMF plays a role in discrete aurora formation. Of the 49 discrete auroral events analyzed, 42 (86%) occurred under draped IMF conditions that are susceptible to reconnection with the underlying crustal anomalies. This investigation produces strong evidence linking discrete aurora to magnetic reconnection at Mars and provides insights into other trends in discrete auroral activation such as local time and upstream IMF conditions.

SOFTWARE GENERATORS IN THE GPSS WORLD ENVIRONMENT FOR DISTRIBUTIONS-PROBABILITY MIXTURES WITH QUALITYASSESSMENT RESULTS

This article presents quality assessment results for the software generators of pseudo-random sequences (PRS) presented in [1; 2] for QS simulation, including second-order hyper-Erlang and hyper-exponential distributions of the second order, that flows organically from these works. These distribution laws are in demand in queuing theory as it provides a big range of changes in the variation coefficient which plays an important role in the average queue delay calculating. There is no data on such generators neither in scientific literature, no in the GPSS WORLD library. Software generator quality evaluation, regardless of the programming language, is usually made according to statistical tests. In this case, chi-squared and Kolmogorov-Smirnov’s tests are used. In addition, the comparison between theoretical and statistical aspects of the corresponding order is provided. Unlike simple distribution laws, for which the use of the above mentioned statistical tests is provided for in well-known software products and does not cause any difficulties, in our case automatic testing is impossible. The well-known packages STATISTICA, STATGAPHICS, Matlab/Simulink, etc. do not provide for the use of the hyper-Erlang and hyper-exponential distribution laws we consider. Therefore, the Pearson statistical test is calculated and checked manually. The presented results should be useful for professionals of discrete event modeling in the GPSS WORLD environment.

OP129 A Cost-Utility Analysis Of Denosumab (Prolia®) For Treating Osteoporosis In Postmenopausal Women: A Swiss Healthcare Payer Perspective

IntroductionOsteoporosis is characterized by decreased bone mass and density, increasing skeletal fragility, and the risk of fragility fracture. Fragility fractures are associated with a high economic burden. Denosumab (Prolia®) is a pharmacological therapy used to treat osteoporosis and reduce the risk of fragility fracture. This study aimed to assess the cost effectiveness of denosumab, compared with other pharmacological therapies (oral bisphosphonates, intravenous [IV] ibandronate, zoledronate, raloxifene, and bazedoxifene) and no treatment, for treating postmenopausal women with osteoporosis.MethodsA discrete event simulation model was developed using a lifetime time horizon. A Swiss healthcare payer perspective was adopted. Time-to-fracture distributions were derived from Swiss-specific Fracture Risk Assessment Tool (FRAX®) probabilities. Reductions in the risk of vertebral and nonvertebral fractures due to treatment were informed by a Bayesian network meta-analysis. Cost-effectiveness frontier analysis was utilized. Pairwise incremental cost-effectiveness ratios (ICERs) between denosumab and each comparator were also estimated. Sensitivity analyses were conducted to identify key drivers and explore the overall certainty of findings.ResultsAt a hypothetical willingness-to-pay (WTP) threshold of CHF100,000 (EUR101,630), IV ibandronate was the most cost-effective therapy in women aged 60 years who had a very high risk of fracture, and in women aged 70 or 80 years of any risk level. In women aged 60 years with a lower risk level, zoledronate was the most cost-effective option. Nevertheless, ICERs from pairwise comparisons between denosumab and some comparators (no treatment, bazedoxifene, raloxifene, and/or zoledronate depending on the cohort’s age and risk profile) were below the hypothetical WTP threshold. Higher intervention costs, smaller reductions in the risk of hip fracture, and shorter duration of residual benefit associated with denosumab contributed to the high ICER values seen in pairwise comparisons with oral bisphosphonates (as a class) and IV ibandronate.ConclusionsThe present evaluation supported the cost effectiveness of denosumab against some, but not all, comparators. Nevertheless, these results should be interpreted cautiously in light of uncertainty in the true effect of treatments on fracture risk.

OP168 Costs And Effectiveness Of Whole Exome Sequencing (WES) In Patients With Unsolved Rare Disease Through The Diagnostic Pathway

IntroductionPatients suspected of having a rare genetic disease often experience lengthy and costly diagnostic odysseys. The timing of whole exome sequencing (WES) in the testing sequence, its diagnostic yield and test costs in the sequence all factor into estimates of cost-effectiveness analysis for health technology assessment.MethodsWe modeled the diagnostic pathway using a discrete event simulation model, starting with the first test result. We defined and populated the simulation based on data from the electronic medical records of n=307 from the Care-for-Rare SOLVE multi-center Canadian observational cohort. Five alternative diagnostic pathways were modeled based on the observed data: no WES, and WES as the first, second, third or fourth test in the sequence. WES as the second test in the sequence is considered standard of care in medical genetic centers in Canada. We assessed effectiveness of WES in terms of diagnostic yield, time to diagnosis, and costs as patient-level overall test costs (2020 CAD/USD) across the diagnostic pathway.ResultsCompared to molecular and specialized diagnostic tests only (i.e., no WES), WES increased diagnostic yield from 5 percent to 40 percent. The shortest time to diagnosis for those with a diagnosis was 1.82 years in the diagnostic pathway with WES as the second test. Test costs for each pathway were CAD2,800 (USD2,087, no WES), CAD2,700 (USD2,013, WES as first test), CAD3,500 (USD2,609, WES as second test), CAD4,500 (USD3,354, WES as third test), and CAD5,300 (USD3,951, WES as fourth test).ConclusionsPlacing WES earlier in the diagnostic pathway for patients suspected of having a rare disease is associated with an increased diagnostic yield, reduced time to diagnosis and lower overall test costs with the benefits being greater the earlier in the pathway that WES is implemented.

Detection and Localization of Stealthy Cyberattacks in Cyber-Physical Discrete Event Systems

The widespread use of cyber-physical systems (CPS) not only brings new opportunities with it but also introduces the risk of cyber attacks on the system. One of the most dangerous and sophisticated types of attacks are the stealthy attacks that can hide their influence and are not detectable by conventional anomaly detection schemes. In this paper, a detection method for the replay attack and the covert attack on discrete event systems is proposed. Unexpected behavior is introduced into the transmitted signals by the integration of permutation matrices into the sensor and actuator channels. The permutation disrupts the stealthiness of an attacker and allows the detection of cyber attacks. Moreover, an attack localization scheme is proposed, which allows to determine which signals are attacked in case that the attacker only attacks a subset of communication channels and not the whole system.

Analysis of Boarding Strategies on an Airbus A320 Using Discrete Event Simulation

Boarding time constitutes a critical element of turnaround time, which is used to measure the efficiency of airline operations. Therefore, to reduce boarding time, it is imperative to reconsider traditional passenger boarding strategies to make them more efficient. In this sense, this study seeks to analyze the impact of different strategies on boarding times using discrete event simulation on an Airbus 320. Seven boarding strategies have been identified and considered in our study, as follows: random, back-to-front, outside-in, reverse pyramid, blocks, Steffen, and modified optimal. The impact of carrying hand luggage and the presence of priority passengers has been considered, as well as the impact of having a continuous arrival of passengers during the boarding process versus having all passengers available at boarding time. In general, simulation results have pointed out that the outside-in and reverse pyramid strategies are the most effective, improving boarding time by up to 15%, when compared to the random strategy. Moreover, the back-to-front strategy, which is generally implemented by airline companies, has been shown to be the most inefficient strategy. Efficient boarding strategies are expected to contribute to the sustainability of air travel by minimizing the turnaround time, improving operational efficiency, and reducing emissions.

Integrating Reinforcement Learning and Discrete Event Simulation Using the Concept of Experimental Frame: A Case Study With MATLAB/SimEvents

Integrating Reinforcement Learning and Discrete Event Simulation Using the Concept of Experimental Frame: A Case Study With MATLAB/SimEvents

A theoretical study of self-soise generation in turbulent jets using one-dimensional turbulence and Lighthill's acoustic analogy

Modeling and simulation of turbulent jet noise is an ongoing numerical challenge relevant to noise pollution control. In the present study, the concept of a novel dimensionally reduced modeling approach based on the so-called one-dimensional turbulence model (ODT) is discussed. ODT aims to resolve source terms due to velocity and kinematic pressure variations in the acoustic near-field at all relevant scales, but only for a single physical coordinate. In the standalone formulation of the model, the physical coordinate taken as a notional line of sight that is pointing in radial cross-stream direction and advected downstream with the axial velocity of the jet while the microscales evolve. Thereby, turbulence is represented by a stochastically sampled sequence of discrete mapping events that punctuate the deterministic molecular diffusive advancement. It is discussed how an ensemble of small-scale resolving independent flow realizations can be used to estimate turbulent noise sources from a range of scales. The main objective is to present an approach for the prediction of broadband self-noise generated by a turbulent jet using ODT and Lighthill's equation. Dedicated experimental measurements are conducted for verification and validation of the numerical modeling approach.