Discrete Event Research Articles

On Detectability of a Class of Hybrid Systems

In this paper we model a hybrid system by using a hybrid machine consisting of an automaton and a set of continuous-variable subsystems. We investigate the problem of determining the (current) discrete state of the hybrid system. Extending the terminologies in discrete event systems, a hybrid system is detectable if its discrete state can be uniquely determined. We derive conditions for a hybrid system to be detectable. If a hybrid system is detectable, we determine its current discrete state using a two-level approach. At the discrete-event level, we construct a discrete-event observer to obtain a discrete state estimate that consists of all possible discrete states that the hybrid system may be in. At the continuous-variable level, a set of continuous-variable observers are constructed, if necessary, to uniquely determine the discrete state. An example of a DC microgrid is used to illustrate the theoretical results.

A comprehensive and integrated hospital decision support system for efficient and effective healthcare services delivery using discrete event simulation

The difficulty that hospital management has been experiencing over the past decade in balancing demand and capacity needs is unprecedented in the United Kingdom. Due to a shortage of capacity, hospitals cannot treat all patients. We developed a whole hospital-level decision support system to assess and respond to the needs of local populations. We integrated a comparative forecasting approach and discrete event simulation modelling using Hospital Episode Statistics and local datasets. It is clear from the literature that this level of whole hospital simulation model has never been developed before (an innovative decision support system). First, the demands of all hospital specialties were forecasted, and the forecasts were embedded into the simulation model as input. Secondly, a simulation model was developed to capture the patient pathway of all specialties. The model integrates every component of a hospital to aid with efficient and effective use of scarce resources (e.g., staff and beds). As a result, the hospital can meet the increasing demand with its current resources. According to the scenario analysis, the hospital bed occupancy rate will reach the national target (i.e., 85%), and the total hospital revenue will increase by approximately 13%, with a 10% increase in A&E and outpatient and a 20% increase in inpatient demand. In conclusion, the hospital-level simulation model can become a crucial instrument for decision-makers to provide an efficient service for hospitals in England and other parts of the world.

Application of systems modeling language (SysML) and discrete event simulation to address patient waiting time issues in healthcare

A robust health care system is crucial to reducing patient stress and contributing to economic growth. The future of the health care industry depends upon a reliable and efficient system to deal with the increasing number of patients. However, in today's healthcare system, patients face negative experiences as a result of long wait times. Now the pressing question is how to develop an effective healthcare system? To address this issue, this study uses systems engineering modeling language (SysML) coupled with a simulation approach to assess the performance of the healthcare system, identify the problem, and offer recommended alternatives. To elaborate, a formal modeling language, SysML will be used to visualize the complicated system architecture and interactions between its different components through a set of diagrams. A blood laboratory within an outpatient clinic located at southern US State is considered a testing bed. The detailed architecture of the system of interest is studied, and required data are collected for modeling and simulation. This article will provide a reference point for practitioners who would like to apply the SysML approach to address health sector-related issues.

Unprotected Left-Turn Behavior Model Capturing Path Variations at Intersections

Path dispersion (the spatial distribution of vehicular paths) is an important feature of traffic flow inside intersections and differs from traffic flow running along traffic lanes at road segment, especially under conflicting movements. The path dispersion reflects the operational features of traffic flow and is related to driving behaviour, arrival flow patterns, layout design, and the traffic control and management scheme. This study aims to improve the understanding of the overall path dispersion of unprotected left-turns and the opposing through movement. A behavioural simulation model was established to represent the overall path dispersion. Human behaviours regarding vehicle trajectory planning with and without conflicting vehicles were modelled based on optimal control and integrated into the proposed discrete event simulation framework. The descriptive power and accuracy of the proposed simulation model were validated using empirical data. The effects of the spatial size of the intersection, crossing angle, and traffic volume on the path dispersion of the left-turn and through movement were explored based on numerical experiments. The results show that the proposed simulation model can represent the path dispersion of left-turn and opposing through movement well for both the calibrated intersections and newly added intersections without model parameter recalibration with an average error of 8.92%.

Supervised Learning of Multievent Transition Matrices in Fuzzy Discrete-Event Systems.

In this article, supervised learning of fuzzy discrete-event systems (FDES) is investigated. A learning algorithm that performs supervised learning for multievent transition matrices of a sequence of fuzzy discrete events is derived. FDES can be used to describe a large class of practical systems that consist of fuzzy discrete states, fuzzy discrete events, and transitions among fuzzy discrete states via fuzzy discrete events. Because fuzzy discrete states, fuzzy discrete events, and fuzzy transitions are well defined in FDES, the FDES model is highly explainable, which is important in many applications, especially in biomedical applications. Based on this explainable model, the proposed learning algorithm can be used to learn events and event sequences in the model. Hence, it allows system developers to build an explainable model for a complex system based on the data available. Simulations using MATLAB are conducted to verify the effectiveness of the proposed algorithm.

Implications of the Theory of Basic Human Values for the Second Demographic Transition: Interdependence and Individualism in the Era of Self-Fulfillment

I examine the implications of a modern psychological theory of values for the Second Demographic Transition (SDT). The SDT derives its values theory and measurement from Maslow, who noted that resource-rich environments cause value shifts towards personal-focused growth values. However, Maslow has been replaced by the theory of basic human values (TBHV) which distinguishes person and social-focused growth values. This distinction has two important implications for the SDT. First, some individualistic and self-expressive values identified by the SDT are not growth but basic need motivated and therefore functions of resource-poor environments. Second, the TBHV values on interdependence and independence are strongly influenced by gender and reflect preferences for family and care or career. Therefore, these values can be used to address critiques of the SDT based on the stalled gender revolution. I show that distinguishing values as described in the TBHV can be useful for the SDT. I find that benevolence (interdependence) is positively and openness to change (autonomy/stimulation) is negatively related to marriage in the Netherlands using longitudinal panel data and discrete event history models.

Computer simulation as a macroergonomic approach to assessing nurse workload and biomechanics related to COVID-19 patient care

This study uses Digital Human Modelling (DHM) and Discrete Event Simulation (DES) to examine how caring for COVID-19-positive (C+) patients affects nurses' workload and care-quality. DHM inputs include: nurse anthropometrics, task postures, and hand forces. DES inputs include: unit-layout, patient care data, COVID-19 status & impact on tasks, and task execution-logic. The study shows that reducing nurses' biomechanical workload increases mental workload and decreases direct patient care, potentially leading to stress, burnout, and errors. Compared to pre-pandemic conditions, when nurses were assigned five C+ patients, cumulative bilateral shoulder moments and lumbar load decreased by 38%, 36%, and 46%, respectively. However, this was accompanied by increases in mental workload (242%), task waiting-time (70%), and missed-care (353%). These effects were driven by the large increase in required infection control routines. Combining DHM and DES can help evaluate workplace/task designs and provide valuable insights for healthcare system design-policy setting and operational management decision-making.

Memories of Extinction and Human Subjectivity Challenging the Human-Nonhuman Divide Through Technology in Kawakami Hiromi’s Ōkina torini sarawarenaiyō

This paper seeks to analyse the role of technology and memory in Kawakami Hiromi’s novel Ōkina torini sarawarenaiyō. Set thousands of years in the future, in a world that has been subjected to several catastrophes, the novel highlights the interconnections between humanity and technology in order to defamiliarize ideas of human centrality and exceptionalism. Kawakami’s characters are forced to face the cyclical possibility of human extinction, which turns into a recurring threat rather than an abstract and discrete event. In this narrative time chronically affected by crises, the definition of what it means to be human is constantly challenged and renegotiated, allowing for the development of non-anthropocentric forms of existence. This paper will demonstrate how, in a world in which the human species is chronically experiencing the threat of extinction, technologies – such as AI entities, clones and factories – allow the renegotiation of ideas of human centrality and singularity, effectively suggesting possibilities for survival outside of a humanist and anthropocentric framework.

A comparison of financial and operational efficiency between smart and traditional insufflation for laparoscopic surgery: A granular analysis

ObjectiveSmart insufflation (SI) techniques relying on valve and membrane-free insufflation are increasing in usage. Although considerable literature exists demonstrating the benefits of SI on procedural ease and patient outcomes, there remains a paucity describing the financial impact of these devices. The purpose of this study was to determine the financial and efficiency impact of these devices on the operating room and inpatient wards of a hospital. MethodsA discrete event simulation model representing a typical mid-sized North American hospital comparing SI to traditional insufflation (TI) was generated. The National Surgical Quality Improvement database from 2015 to 2019 was used to populate the model with data supplemented from the literature. Outcomes included length of stay (LOS), duration of surgery (DOS), annual procedure volume, profit, return on investment (ROI), and gross profit margin (GPM). From the literature review, DOS savings were 10–32 minutes/case, while LOS savings were 0–3 days/case. ResultsImplementation of an SI led to an increase in annual throughput of 42–346 (4.4%–36.6%) cases for all procedures and 38 to 297 (4.3%–33.3%) cases for complex procedures. LOS was found to be decreased by 175–614 (18.3%–64.2%) days for all procedures and 231 to 614 (35.6%–77.9%) cases for complex procedures with the implementation of an SI. Together, this resulted in an increase in net profit of $104,685 per annum. The ROI of SI over the TI device was >1000%, and the GPM for the TI was 90.0%, while the GPM for the SI was 71.7%. ConclusionDespite the initial financial investment being greater, the implementation of SI offsets these expenses and yields significant financial benefits. Our study demonstrates the financial benefits of SI over TI and illustrates how granular operational and financial analyses of technologies are essential to aid in sound healthcare procurement decision making.

Comparison of mathematical models in reverse logistics: case of pesticide containers and packaging

This article presents an analysis of the use of Mixed Integer Linear Programming (MILP) and Discrete Event Simulation in reverse logistics networks, specifically applied to the process of collection and final disposal of empty pesticide containers and packaging in the Department of Boyacá- Colombia, which faces a problem due to the non-return and collection of all empty pesticide containers and packages generated by farmers. Due to the above, initially, a literature search on reverse logistics research is carried out, to subsequently propose a deterministic and stochastic model using linear programming and simulation respectively, to analyze which of these mathematical tools offers and represents better the process under study. The results show that the deterministic model offers better results in terms of planning the quantities to be collected in each of the farms, however, it does not contemplate the possible variations in different periods. On the contrary, the simulation, having a stochastic component, includes the variability of the generation of pesticide containers and packages, better identifying the bottlenecks that may occur in the process. In conclusion, it is evident that both models offer advantages for the reverse logistics process, where the ideal could be the combination of the deterministic with the stochastic.

Enhancement the Performance of Multi-Level and Multi-Commodity in Supply Chain: A Simulation Approach

In today's global economy, effective supply chain management plays a pivotal role in the success of a business. The repercussions of a business strategy on the entire supply chain remain uncertain until it is implemented. Utilizing simulations offers the opportunity to gauge performance before implementing the strategy. The primary aim of employing supply chain simulation is to analyze the effects of different strategies on profit enhancement and cost reduction across all supply chain tiers. This research paper has formulated a discrete event simulation model using Arena software to evaluate and enhance the operational efficiency of the detergent supply chain. The problem involves multiple levels and commodities, encompassing four manufacturers, two intermediate storage warehouses, and four main distributors following an (s, S) inventory control approach. Shortages are permitted, leading to a partial loss or back-ordering of products. The overarching objective is to minimize the overall inventory costs within the system, accounting for holding costs at each tier, managing shortages, and the expense incurred due to lost sales. A range of scenarios are developed to set control parameters, with the evaluation of supply chain performance falling into two main categories: financial and operational considerations.

Optimisation of Buffer Allocations in Manufacturing Systems: A Study on Intra and Outbound Logistics Systems Using Finite Queueing Networks

Optimal buffer allocations can significantly improve system throughput by managing variability and disruptions in manufacturing or service operations. Organisations can minimise waiting times and bottlenecks by strategically placing buffers along the flow path, leading to a smoother and more efficient production or service delivery process. Determining the optimal size of buffers poses a challenging dilemma, as it involves balancing the cost of buffer allocation, system throughput, and waiting times at each service station. This paper presents a framework that utilises finite queueing networks for performance analysis and optimisation of topologies, specifically focusing on buffer allocations. The proposed framework incorporates a finite closed queuing network to model the intra-logistics material transfer process and a finite open queueing network to model the outbound logistics process within a manufacturing setup. The generalised expansion method (GEM) is employed to calculate network performance measures of the system, considering the blocking phenomenon. Discrete event simulation (DES) models are constructed using simulation software, integrating optimisation configurations to determine optimal buffer allocations to maximise system throughput. The findings of this study have significant implications for decision-making processes and offer opportunities to enhance the efficiency of manufacturing systems. By leveraging the proposed framework, organisations can gain valuable insights into supply chain performance, identify potential bottlenecks, and optimise buffer allocations to achieve improved operational efficiency and overall system throughput.

Security Quantification for Discrete Event Systems Based on the Worth of States

This work addresses the problem of quantifying opacity for discrete event systems. We consider a passive intruder who knows the overall structure of a system but has limited observational capabilities and tries to infer the secret of this system based on the captured information flow. Researchers have developed various approaches to quantify opacity to compensate for the lack of precision of qualitative opacity in describing the degree of security of a system. Most existing works on quantifying opacity study specified probabilistic problems in the framework of probabilistic systems, where the behaviors or states of a system are classified as secret or non-secret. In this work, we quantify opacity by a state-worth function, which associates each state of a system with the worth it carries. To this end, we present a novel category of opacity, called worthy opacity, characterizing whether the worth of information exposed to the outside world during the system’s evolution is below a threshold. We first provide an online approach for verifying worthy opacity using the notion of a run matrix proposed in this research. Then, we investigate a class of systems satisfying the so-called 1-cycle returned property and present a worthy opacity verification algorithm for this class. Finally, an example in the context of smart buildings is provided.

Optimization of the redundancy allocation problem: Genetic algorithm and Monte Carlo simulation with discrete events

Sistem güvenilirliği, iletişim ve elektronik sistemler gibi ileri teknolojiye sahip modern mühendislik sistemlerinin tasarımı ve analizinde yaygın olarak kullanılan önemli ölçütlerden biridir. Günlük hayatımıza giren, çoğu çalışma alanında ve sosyal yaşamda kullandığımız uygulamaları doğrudan etkileyen altyapı tasarım problemlerinin bir kısmı güvenilirlik ölçütünü dikkate alan eniyileme problemi olarak tanımlanmaktadır. Bu nedenle bu tür problemlerin çözümü, farklı disiplinlerde çalışan birçok araştırmacının yoğun ilgisini çekmektedir. Sistem güvenilirliğinin eniyilenmesi için alan yazında yaygın olarak kullanılan Yedek Bileşen Tahsis Problemi (YBTP), sistemde yer alan bileşenlere paralel olarak yerleştirilen kullanıma hazır bileşenler ile yeni sistemlerin tasarlanması olarak tanımlanabilir. Böylece, güvenilirlik başarım ölçütü dikkate alınarak daha yüksek güvenilirlik değerine sahip sistem tasarımlarının elde edilmesi sağlanabilmektedir. Sistem özelliklerine bağlı olarak seçilen uygun yöntemle sistemin güvenilirlik değeri elde edilir. Bu çalışmada, artan arıza ve tamir oranlarının dikkate alındığı YBTP’de sistem güvenilirliğini gerçekçi bir yaklaşımla tahmin etmek için Kesikli Olaylı Benzetim (KOB) modeli, sistemin eniyilenmesi için ise Genetik Algoritma (GA) geliştirilmiştir. KOB modelinin geçerliliği test problemleri üzerinde gösterilmiştir. Önerilen yaklaşımla, YBTP için daha yüksek güvenilirliğe sahip sistem tasarımlarının daha düşük maliyetlerle elde edilebilmesi sağlanmıştır.

Duty-cycle-aware low-thrust trajectory optimization using embedded homotopy

In space mission design, an important operational constraint is planned periodic coast arcs during which various mission-critical tasks such as navigation or ground communications are performed. These constraints, typically implemented as thruster duty cycles, can be characterized as discrete coast arcs enforced periodically during intervals of deterministic thrusting. A typical approach to incorporate duty cycles is by enforcing maximum thrust levels that are less than a nominal value. This work presents an embedded homotopy indirect optimization method for a smooth enforcement of discrete duty-cycle constraints for low-thrust trajectory optimization. The complexity of enforcing such discrete constraints is two-fold: (1) a precise event-detection strategy is needed to determine the beginning of deterministic thrust arcs, which must be successively updated along the trajectory and (2) a smooth modeling of the discrete coast events occurring on the order of 10s or even 100s depending on the duration of deterministic thrust arcs. Duty cycle constraints are enforced at the level of the Hamiltonian using a Composite Smooth Control (CSC) method, producing high-resolution optimal control solutions conscious of planned duty cycle coast arcs. Numerical solutions are presented for the heliocentric transfer phases of three space missions. The results are then validated against the Evolutionary Mission Trajectory Generator (EMTG), one of NASA’s global trajectory optimization tools used for interplanetary mission design and capable of the explicit enforcement of periodic duty cycle coasting. The proposed indirect method is suitable for obtaining high-resolution trajectory solutions with realistically enforced thruster duty cycle, which could then be used as high-quality initial guesses in flight-fidelity mission planning tools. The proposed smooth modeling of discrete coast arcs can also be customized to enforce periodic and non-periodic thrust shutdown constraints.

A survey on compositional algorithms for verification and synthesis in supervisory control

AbstractThis survey gives an overview of the current research on compositional algorithms for verification and synthesis of modular systems modelled as interacting finite-state machines. Compositional algorithms operate by repeatedly simplifying individual components of a large system, replacing them by smaller so-called abstractions, while preserving critical properties. In this way, the exponential growth of the state space can be limited, making it possible to analyse much bigger state spaces than possible by standard state space exploration. This paper gives an introduction to the principles underlying compositional methods, followed by a survey of algorithmic solutions from the recent literature that use compositional methods to analyse systems automatically. The focus is on applications in supervisory control of discrete event systems, particularly on methods that verify critical properties or synthesise controllable and nonblocking supervisors.

Studying waves of prediction in the brain using narratives

Narrative stimuli offer a unique opportunity for research in cognitive neuroscience because they evoke cognitive processes that are difficult or impossible to study with traditional paradigms. An especially compelling feature of narratives is their temporal structure, which allows for meaningful predictions about upcoming events. As we proceed through a narrative, we can maintain a complex set of short- and long-term guesses about the future and continually refine our predictions as the story unfolds. Experiments using narratives can allow researchers to probe the ways in which memory systems are flexibly used during perception, including the mechanisms by which continuous experiences are segmented into discrete events. Despite the challenges of using narratives and other naturalistic stimuli in experimental research, these approaches offer a new window into critical components of real-world cognition.

The emergency hierarchical medical system in China: Improving patient waiting times and operation management

ABSTRACT The emergency hierarchical medical system, a multi-hospital collaborative approach, is one of the innovative healthcare management modes in China during the COVID-19 epidemic. This study aims to prove the waiting cost-saving effectiveness of the emergency hierarchical medical system. Two discrete event simulation models based on this system and traditional admission system will be constructed, and the M/M/1 queue structure with outpatient phase and hospitalization phase will be used to calculate the waiting time of those two systems. Furthermore, we do sensitivity analyses of hospital level, in-hospital disease cross-infection, and resource allocation to get the least costly strategy and to provide operational guidance. In the result, based on the regional virtual hospital structure constructed, the emergency hierarchical medical system outperforms the traditional system when the number of infection patients exceeds a threshold. Besides, the rate of in-hospital cross-infection does not affect the cost-saving effectiveness of system, and the centralized hospital should be set considering the hospital service ability. Moreover, given limited medical resources during the epidemic, the resource allocation between hospitals and patients can be optimized to improve the system’s cost-saving.

Discrete log anomaly detection: A novel time-aware graph-based link prediction approach

With the implementation of online-service information systems, it is important to detect system anomalies. Logs, serving as the system runtime information, are the key resources to understand system statuses. The existing automatic log anomaly detection approaches focused on the temporal features deriving from sequential nature of logs/events, which can be suitable for limited-sized information systems with homogeneous structure. However, with increasing complexity of systems, the time-aware relationships among discrete logs/events become non-trivial features. Different from the previous works, a novel framework for log anomaly detection is proposed from the perspective of link prediction. First, a temporal event graph is constructed to model the time-aware relationships between events, which is derived from discrete event sequences and possesses the evolutionary properties of sequential events. Second, we pre-train a graph convolutional neural network by using graph contrastive learning to capture local topological features. Third, a temporal convolutional network is used to learn the temporal features. Finally, based on generative adversarial network, link prediction is implemented by generating the next moment event graph, which also is the basis for anomaly detection. Experiments are conducted on public datasets to verify the validity of the model, and superior results are achieved compared to the state-of-the-art methods.

Learn-to-supervise: Causal reinforcement learning for high-level control in industrial processes

Possessing efficient supervisory control systems is crucial for maintaining the desired operational performance of complex industrial processes. Several challenges face the developers of these systems, such as requiring accurate physical models, dealing with the variability and uncertainty of process operating conditions and coordinating between local controllers to reach desired global performance. This paper proposes an intelligent supervisory control approach based on causal reinforcement learning (CRL) to effectively manipulate the controllers’ setpoints of the process in a way that optimizes its key performance indicators (KPIs), thereby improving the energy efficiency of the process. The approach adopts deep reinforcement learning (DRL) to develop an efficient control policy through interaction with a process simulation. The DRL training history is then exploited using interpretable machine learning and process mining to build a discrete event system (DES) model, in the form of a state-event graph. The DES model identifies causal relationships between events and provides interpretability to the control policy developed by the DRL method. The DES discovered is exploited as a Markov decision process to apply the Q-learning algorithm as a CRL supervisor. The supervisor incorporates causal knowledge into its training process, thus improving the DRL control policy developed and identifying the event paths that optimize the process’s KPIs. The proposed approach is validated using two heat recovery systems in a pulp & paper mill. It successfully achieves a control policy that reduces energy consumption by up to 15.6% for the first system and 5.02% for the second, compared to the expert’s baseline methods.