Discrete Event Simulation Model Research Articles

A novel method of refining the performance of rail systems: an evaluation of system dynamics using discrete event simulation

PurposeThis paper aims to trial a novel method of improving the performance of rail systems. Accordingly, an evaluation of rail system dynamics (SD) using discrete event simulation (DES) will be undertaken. Globally, cities and their transportation systems face ongoing challenges with many of these resulting from complicated rail SD. To evaluate these challenges, this study utilized DES as the basis of the analysis of Melbourne Metro Rail's SD. The transportation SD processes including efficiency and reliability were also developed.Design/methodology/approachUsing DES, this research examines and determines the Melbourne Metro Rail's SD. Although the Melbourne Metro Rail is still in progress, the DES developed in this research examined the system requirements of functionality, performance and integration. As the basis of this examination, the Melbourne Metro Rail's optimization was simulated using the developed DES. As the basis of the experiment, a total of 50 trials were simulated. This included 25 samples for each of efficiency and reliability. The simulation not only scrutinized the SD but also underlined some of its shortfalls.FindingsThis study found that information and communication technology (ICT) was the pinnacle of system application. The DES development highlighted that both efficiency and reliability rates are the essential SD and thus fundamental for Melbourne Metro Rail system functionality. Specifically, the three elements of SD, capacity, continuity and integration are considered critical in improving the system functionality of Melbourne Metro Rail.Research limitations/implicationsThis particular mega rail infrastructure system was carefully analyzed, and subsequently, the DES was developed. However, since the DES is at its inception, the results are relatively limited without inclusive system calibration or validation process. Nonetheless, with some modifications, such as using different KPIs to evaluate additional systems variables and setting appropriate parameters to test the system reliability measures at different intensities, the developed DES can be modified to examine and evaluate other rail systems. However, if a broader system analysis is required, the DES model subsequently needs to be modified to specific system parameters.Practical implicationsThrough evaluation of Melbourne's Metro Rail in the manner described above, this research has shown the developed DES is a useful platform to understand and evaluate system efficiency and reliability. Such an evaluation is considered important when implementing new transport systems, particularly when they are being integrated into existing networks.Social implicationsEfficient rail networks are critical for modern cities and such systems, while inherently complex, aid local economies and societal cohesion through predictable and reliable movement of people. Through improved system functionality and greater efficiencies, plus improved passenger safety, security and comfort, the traveling public will benefit from the enhanced reliability of the transportation network that results from research as that provided in this paper.Originality/valueThis research paper is the first of its kind specifically focusing on the application of DES on the Melbourne Metro Rail System. The developed model aligns with the efficiency optimization framework, which is central to rail systems. The model shows the relationship between increased efficiency and optimizing system reliability. In comparison with more advanced mathematical modeling, the DES presented in this research provides robust, but yet rapid and uncomplicated system enhancements. These findings can better prepare rail professionals to adequately plan and devise appropriate system measures.

Cytomegalovirus Matching in Deceased Donor Kidney Allocation: Results From a U.S. National Simulation Model.

Cytomegalovirus (CMV) infects >60% of adults and can pose an independent risk factor for allograft loss and mortality in solid organ transplant recipients. The purpose of this study is to evaluate the impact of a nationwide implementation of CMV seromatching (donor/recipient: D-/R- and D+/R+) in the U.S. deceased donor kidney allocation system (KAS). Adult candidates on the U.S. kidney-only transplant waiting list and deceased donor kidneys offered to the U.S. transplant centers were considered. A discrete-event simulation model, simulating the pre-COVID-19 period from January 1, 2015, to January 1, 2018, was used to compare the performances of currently employed KAS-250 policy (without CMV matching) to various simulated CMV matching policies parameterized by calculated panel reactive antibody exception threshold. Outcomes included CMV serodistribution, waiting time, access to transplantation among various groups, transplant rate, graft survival, kidney discard rate, and antigen-mismatch distribution, stratified by CMV serostatus. CMV matching policy with a calculated panel reactive antibody exception threshold of 50% (namely, the CMV">50%" policy) strikes a better balance between benefits and drawbacks of CMV matching. Compared with KAS-250, CMV">50%" reduced CMV high-risk (D+/R-) transplants (6.1% versus 18.1%) and increased CMV low-risk (D-/R-) transplants (27.2% versus 13.1%); increased transplant rate for CMV R- patients (11.54 versus 12.57) but decreased for R+ patients (10.68 versus 10.48), yielding an increase in aggregate (11.09 versus 10.94); and reduced mean time to transplantation (by 6 wk); and reduced kidney discard rate (25.7% versus 26.2%). Our findings underscore the feasibility and potential advantages of a nationwide CMV seromatching policy in kidney transplantation.

Energy flexibility in aerospace manufacturing: The case of low carbon intensity production

In this paper, the prospects of energy flexibility in mitigating the environmental impact of aerospace manufacturing are explored. In collaboration with a UK-based aerospace manufacturing enterprise, demand response, in particular production time, is explored under different stochastic scenarios. This is done through a decision-support framework that consists of a stochastic discrete-event simulation model that tests different scenarios under a full factorial design of experiments framework. The simulation model tests various improvement strategies pertaining to prioritisation rules, production start-up rules, and operating hours. The model aids in scheduling energy-intensive processes, so the time of performing such processes can coincide with times of the day when the energy’s carbon intensity is at its lowest. The use case constitutes a family of aluminium structural aerospace components that are characterised by high production rate. Results demonstrate promising potential of the proposed approach, with the best-case scenario resulting in a 7% reduction in CO2e emissions. Analysis of the results demonstrate that operational decisions that do not require infrastructural changes or capital expenditures can contribute favourably to achieving net-zero targets. This research offers useful insights on leveraging operational short-term decisions to meet the aerospace manufacturing’s sector decarbonisation targets.

Capacity Utilization Assessment Using Discrete-Event Simulation: Case Study of a High-Density Crowd Visiting the Holy Rawda in the City of Madinah

Managing a high-density crowd is challenging, especially when space is limited, due to increased risk of crowd disasters. There are many considerations to lower disaster risk and ensure crowd safety such as infrastructure, facility, staff, emergency response plan, education and awareness. This paper investigates capacity management, which is the most critical consideration when space is limited. Discrete-event simulation (DES) technique is highly adept at reproducing complex real-world operations and can incorporate numerous stochastic variables, thereby improving its effectiveness in decision-making. The objective of this study is to assess capacity utilization of small space through formulating space features and crowd dynamic with a DES model. The model is intended to provide support for informed decision-making process related to capacity management. A real case study about a high-density crowd visiting the Holy Rawda was employed. The data related to the visiting parameters and visit experience KPIs were captured through a range of interviews and actual visits. Comparing the results of the simulation scenarios against the proposed KPIs had a surprising result, there is only 79% utilization of the area, although, intensive high-density crowd were featured during all the visits. It means better management of the crowd may allow 21% more visitors to visit the Holy Rawda.

Towards sharing tools and artefacts for reusable simulations in healthcare

ABSTRACT Discrete-event simulation (DES) is a widely used computational method in health services and health economic studies. Despite increasing recognition of the advantages of open, reusable DES models for both healthcare practitioners and simulation researchers, in practice very few authors share their model code alongside a published paper. In the context of Free and Open Source Software (FOSS), this paper presents a pilot framework called STARS: Sharing Tools and Artefacts for Reusable Simulations to begin to address the challenges and leverage the opportunities of sharing DES models in healthcare. STARS aligns with existing guidelines and documentation, including reproducibility initiatives, and enables computer models to be shared with users of differing technical abilities. We demonstrate the feasibility and applicability of STARS with three applied DES examples using Python. Our framework supports the development of open, reusable DES models which can enable partner healthcare organisations to preview, validate, and use models. Academic research teams can benefit from knowledge exchange, enhanced recognition and scrutiny of their work, and long-term archiving of models.

Hospital selection decision in emergency medical services: a simulation-based assessment of assignment criteria

ABSTRACT Emergency Medical Services play a fundamental societal role since they provide pre-hospital care to patients in critical and time-constrained conditions. In this context, the hospital selection decision is vital to respond in a timely, effective and efficient way. By means of simulation experiments, the present study compares the effects of different assignment logics against a set of performance dimensions. To this end, we develop a Discrete Event Simulation model that covers the entire Emergency Medical Service process, from the ambulance transportation to final departure of the patient from the Emergency Department. Results demonstrate that the use of effectiveness-related criteria (e.g. expected waiting time, resource saturation) significantly enhances the overall performance and, at the same time, guarantees higher quality of care. Thus, the study contributes in enhancing hospital selection decisions by supporting decision-makers through a comprehensive decisional model, distancing from prior researches that predominantly considered efficiency-driven criteria (i.e. proximity).

Self-optimizing digital factory twin: an industrial use case

AbstractDigital Twins (DTs) are intended to be utilized for a wide range of applications, promising benefits like visualization, monitoring, simulation and control of a physical system. Not only the development of a DT for a production facility is a time-consuming task, but also to keep the virtual counterpart up to date in the use phase. In this work, the implementation of an industrial-scale DT of an automotive supplier production site based on a Discrete-Event Simulation (DES) model with self-optimization capabilities for easier maintainability and increased simulation accuracy is presented.

Evaluating the supply chain resilience strategies using discrete event simulation and hybrid multi-criteria decision-making (case study: natural stone industry)

ABSTRACT One of the challenges that organisations face is the risk reduction through creating resilient supply chains. There are different strategies to reach the resiliency. In this paper, we analysed five important strategies: redundancy, backup supplier, reserved capacity, reserved inventory, and increasing work-shifts. These supply chain resilience (SCR) strategies are evaluated using discrete event simulation and hybrid multi-criteria decision-making methods. For this end, Bayesian best-worst method (BWM) and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were used for weighting the SCR evaluation criteria and ranking the SCR strategies, respectively. Six discrete event simulation models were generated and run for evaluating each strategy in order to achieve quantitative criteria. On the other hand, the ideas of experts were used for weighting qualitative criteria and also ranking the alternatives. Results showed that the increasing work-shift strategy has the first rank and the reserved capacity strategy has the lowest rank.

Cost/effectiveness of elivaldogene autotemcel vs. hematopoietic stem cells transplantation in patients with cerebral adrenoleukodystrophy (CALD)

Cerebral adrenoleukodystrophy (CALD) is a severe, rare neurological disease with a progressive course and poor prognosis. Allogeneic hematopoietic stem cell transplantation (HSCT) is still considered the standard of care for CALD. Elivaldogene autotemcel is a novel gene therapy for the treatment of childhood-onset CALD. Since many novel gene therapies are associated with high costs, our aim was to compare costs and effectiveness of elivaldogene autotemcel with the costs and effectiveness of allogeneic HSCT treatment of childhood-onset CALD. The analysis used an 80-year horizon and the perspective of the Serbian Republic Health Insurance Fund. Quality-adjusted life years (QALYs) gained with elivaldogene autotemcel and HSCT, as well as direct treatment costs, were our major outcomes. For the purposes of the economic analysis, we constructed and simulated a discrete-event simulation model to calculate the outcomes. We obtained the results of the model using a Monte Carlo simulation, which we conducted on 1000 virtual CALD patients. The analysis indicated that elivaldogene autotemcel was not cost-effective when compared to HSCT, and the net monetary benefit was negative. The base case incremental cost/effectiveness ratio was 259,848,512 RSD/QALY. Elivaldogene autotemcel is not more cost-effective than HSCT for the treatment of CALD from the perspective of the Serbian Republic Health Insurance Fund, according to classic pharmacoeconomic analysis and criteria. To ensure that the treatment is affordable for all patients for whom it is prescribed, a differentiated pricing strategy with lower drug prices in developing nations seems to be required.

An integrated simulation and optimization tool for short-term mining planning problems with different prioritization among competing plant targets

This work proposes a tool that integrates a hierarchical mixed-integer linear programming (MILP) model with a discrete event simulation (DES) model to simulate an annual mining plan discretized in shift-by-shift periods. The hierarchical MILP model has ten objectives and optimizes the shift schedule according to the available materials in the free faces at each simulation moment. The DES model simulates the realization of this schedule considering the uncertainties and interactions between the mine’s equipment. Four scenarios from a Brazilian mining company were analyzed. These differ regarding the prioritization order of the MILP goals and the plants’ grade tolerance. The results report that prioritizing the plant with the highest value-added product results in a 7.0% production gain. Additionally, prioritizing the particle size target of the plants leads to a 2.5% production gain compared to prioritizing the element grade targets.

An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study

BackgroundShortages of mechanical ventilation have become a constant problem in Emergency Departments (EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES) to design effective interventions ensuring the high availability of ventilators for patients needing these devices.MethodsFirst, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European hospital group was used to validate the proposed methodology.ResultsThe number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity of the AI model was 93.08% (95% confidence interval, [88.46 − 96.26%]), whilst the specificity was 85.45% [77.45 − 91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 − 95.13%) and 87.85% (80.12 − 93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 − 100%). Finally, the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource capacity strategy.ConclusionsCombining AI and DES helps healthcare decision-makers to elucidate interventions shortening the waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics.

Evaluating Reception Center Models for Radiation Response Screening Capacity and Throughput Predictions.

Introduction: The current fleet of nuclear reactors in the United States is mandated to provide evidence that surrounding jurisdictions can screen their populations should an incident occur. Capacity can be measured as throughput in reception centers used for screening. Due to the significant staffing and resources required to exercise screening capacity, most jurisdictions typically perform smaller exercises and use models to estimate their overall throughput. Objective: To evaluate the applicability and realism of current throughput models and practices. Methods: Throughput capacity for radiation screening is estimated with a mathematical model derived by the Federal Emergency Management Agency (FEMA). The Centers for Disease Control and Prevention developed a discrete event simulation model as a tool, SimPLER, to evaluate capacity and make throughput predictions. Model estimates will be compared and evaluated using timing data collected at a large-scale exercise. Results: The FEMA model estimated a throughput 41.2% higher than the actual radiation screening throughput, while the SimPLER model provided identical values. The FEMA and SimPLER models' predicted throughputs were 50% and 3.8%, respectively, higher than total exercise throughput. Applying each model to the throughput projections for a 12-hour shift, the FEMA model estimates ranged from 665 to 6,646 people and the SimPLER model yielded an estimated throughput of 1,809 people with a standard deviation of 74.6. Conclusion: Discrete event simulation models, such as SimPLER, may provide more realistic and accurate predictions of radiation screening and throughput capacity of reception centers than mathematical models such as the FEMA model.

A material handling system modeling framework: a data-driven approach for the generation of discrete-event simulation models

A material handling system modeling framework: a data-driven approach for the generation of discrete-event simulation models

Enabling massively parallel, ad hoc exploration of the design space for simulation models within a serverless environment

This paper presents a massively parallel, cloud-computing framework for the ad hoc evaluation of discrete-event simulation (DES) models to enable broad exploration of the design space for model parameters. Parallel evaluation is enabled through use of a serverless computing environment allowing thousands of simultaneous experiments, on demand, without the need to explicitly provision or manages hardware. A standard Simulation Evaluation application programming interface (API) was designed for evaluating simulation functions that enables language independence between client application and simulation model, encouraging reuse of simulation models for multiple purposes (what-if analysis, ranking and selection, sensitivity analysis, or optimization). Extensions to the Java Simulation Library (JSL)27 enable rapid deployment of models built with the JSL as parameterized serverless functions implementing the Simulation Evaluation API. New Java packages facilitate the calling of any serverless functions that implement the Simulation Evaluation API.

Estimated reductions in type 2 diabetes burden through nutrition policies in AZAR cohort population: A PRIME microsimulation study for primary health care.

Given the impact of high intake of sugar-sweetened beverages on type 2 diabetes, intervention to reduce their consumption can be a top priority for any health system. Thus, the purpose of the present study is to simulate the impact of policy options related to reduce consumption of sugar-sweetened beverages (SSBs) on the prevalence and mortality of type 2 diabetes in Iranian men and women. A discrete event simulation (DES) model was used to predict the effect of several policy options on the prevalence and death from type 2 diabetes in Azar Cohort Databases. Population age- and sex-specific prevalence and incidence rate of diagnosed diabetes were derived from the national health data. The Preventable Risk Integrated Model (PRIME) model was used for coding the input parameters of simulation using R and Python software. The prevalence and mortality rate of type 2 diabetes under the scenario of reduced consumption of SSBs indicated that the highest and the lowest prevalence and mortality rates of type 2 diabetes for men and women were related to no policy condition and replacing SSBs with healthy drinks, like water, respectively. Also, the maximum "number of deaths postponed/ prevented" from type 2 diabetes was related to replacing SSBs with water (n=2015), and an integration of reformulation and applying 10% tax on SSBs (n=1872), respectively. Simulating the effect of different policy options on reducing the consumption of SSBs showed "replacing of SSBs with water" as the most effective policy option in Iranian setting.

CAR T-cell Therapy for Diffuse Large B-cell Lymphoma in Canada: A Cost-Utility Analysis.

Chimeric antigen receptor (CAR) T-cell therapy is a novel cell therapy for treating non-Hodgkin lymphoma. The development of CAR T-cell therapy has transformed oncology treatment by offering a potential cure. However, due to the high cost of these therapies, and the large number of eligible patients, decision makers are faced with difficult funding decisions. Our objective was to assess the cost-effectiveness of tisagenlecleucel for adults with relapsed/refractory diffuse large B-cell lymphoma in Canada using updated survival data from the recent JULIET trial. We developed an individual-simulated discrete event simulation model to assess the costs and quality-adjusted life-years (QALY) of tisagenlecleucel compared with salvage chemotherapy. Survival estimates were obtained from a published clinical trial and retrospective analysis. If patients remained progression free for 5 y, they were assumed to be in long-term remission. Costing and utility data were obtained from reports and published sources. A Canadian health care payer perspective was used, and outcomes were modeled over a lifetime horizon. Costs and outcomes were discounted at 1.5% annually, with costs reported in 2021 Canadian dollars. A probabilistic analysis was used, and model parameters were varied in 1-way sensitivity analyses and scenario analyses. After we incorporated the latest clinical evidence, tisagenlecleucel led to an additional cost of $503,417 and additional effectiveness of 2.48 QALYs, with an incremental cost-effectiveness ratio of $202,991 compared with salvage chemotherapy. At a willingness-to-pay threshold of $100,000/QALY, tisagenlecleucel had a 0% likelihood of being cost-effective. At the current drug price, tisagenlecleucel was not found to be a cost-effective option. These results heavily depend on assumptions regarding long-term survival and the price of CAR T. Real-world evidence is needed to reduce uncertainty. For patients with diffuse large B-cell lymphoma who failed 2 or more lines of systemic therapy, CAR T was not found to be a cost-effective treatment option at a willingness-to-pay threshold of $100,000.These results heavily depend on the expected long-term survival. The uncertainty in the model may be improved using real-world evidence reported in the future.

Mechanism Analysis of the Impact of COVID-19 on the Whole Process of Aircraft Turnaround Operations

Efficient aircraft turnaround operations at airports are vital to ensure overall air traffic network performance. After the outbreak of COVID-19, the traditional aircraft ground handling process has changed significantly due to new requirements put forward by the pandemic prevention and control policy. To better understand how COVID-19 has affected ground handling operations, a discrete-event simulation model of turnaround is established to analyze the change in the whole turnaround process before and after the pandemic. The critical path of turnaround operations was used to identify the significantly affected subprocesses to which airports should pay attention. For a case study on the two busiest airports in China, the aircraft turnaround time increased by about 18% after COVID-19. Cabin cleaning, catering, and passenger embarking were the main processes in causing this increase. By evaluating the impact mechanism of COVID-19 on turnaround operations, the study sheds light on strategic, tactical, and operational approaches for relevant authorities.

Changing the Patient Flow Process for the Green Area of Emergency Department: A Discrete-Event Simulation Approach

The emergency department is important as a fundamental element of the healthcare system. Patient flow processes in emergency departments are a critical factor that determines effective and rapid medical intervention as well as the overall performance of the healthcare system.
 Aim: This study aimed to analyze patient waiting times and hospital stay times by developing a different approach to the current patient flow processes in the green area of a hospital emergency department.
 Methods: Numerical and statistical results were obtained using a discrete event simulation model for the current and new situation of patient flow processes.
 Results: The waiting time decreased from 12.91 minutes in the current simulation model to 12.81 minutes in the proposed simulation model. The length of stay time decreased from 54.72 minutes in the current simulation model to 53.90 minutes in the proposed simulation model. In this case, it is seen that the proposed model provides a reduction in the length of stay and patient waiting time. Conclusion: The result shows that the proposed model provides some improvement in both waiting and length of stay time. This study offers a different approach to patient flow, allowing emergency department personnel to use their resources effectively and minimizing the time spent on patients' treatment or waiting periods. Thus, this study concluded that the effective management of recommended patient flow processes increases the capacity of emergency departments to deal with emergencies and accelerates access to emergency medical services.

Performance Evaluation of Spintronic-Based Spiking Neural Networks Using Parallel Discrete-Event Simulation

Spintronics devices that use the spin of electrons as the information state variable have the potential to emulate neuro-synaptic dynamics and can be realized within a compact form-factor, while operating at ultra-low energy-delay point. In this paper, we benchmark the performance of a spintronics hardware platform designed for handling neuromorphic tasks. To explore the benefits of spintronics-based hardware on realistic neuromorphic workloads, we developed a Parallel Discrete-Event Simulation model called Doryta, which is further integrated with a materials-to-systems benchmarking framework. The benchmarking framework allows us to obtain quantitative metrics on the throughput and energy of spintronics-based neuromorphic computing and compare these against standard CMOS-based approaches. Although spintronics hardware offers significant energy and latency advantages, we find that for larger neuromorphic circuits, the performance is limited by the interconnection networks rather than the spintronics-based neurons and synapses. This limitation can be overcome by architectural changes to the network. Through Doryta we are also able to show the power of neuromorphic computing by simulating Conway’s Game of Life (GoL), thus showing that it is Turing complete. We show that Doryta obtains over 300 × speedup using 1,024 CPU cores when tested on a convolutional, sparse, neural architecture. When scaled-up 64 times, to a 200 million neuron model, the simulation ran in 3:42 minutes for a total of 2000 virtual clock steps. The conservative approach of execution was found to be faster in most cases than the optimistic approach, even when a tie-breaking mechanism to guarantee deterministic execution, was deactivated.

A discrete-event simulation model for assessing operating room efficiency of thoracic, gastrointestinal, and orthopedic surgeries.

In hospital management, pinpointing steps that most enhance operating room (OR) throughput is challenging. While prior literature has utilized discrete event simulation (DES) to study specific strategies such as scheduling and resource allocation, our study examines an earlier planning phase, assessing all workflow stages to determine the most impactful steps for subsequent strategy development. DES models real-world systems by simulating sequential events. We constructed a DES model for thoracic, gastrointestinal, and orthopedic surgeries summarized from a tertiary Chinese hospital. The model covers preoperative preparations, OR occupation, and OR preparation. Parameters were sourced from patient data and staff experience. Model outcome is OR throughput. Post-validation, scenario analyses were conducted for each department, including: (1) improving preoperative patient preparation time; (2) increasing PACU beds; (3) improving OR preparation time; (4) use of new equipment to reduce the operative time of a selected surgery type; three levels of improvement (slight, moderate, large) were investigated. The first three improvement scenarios resulted in a 1%-5% increase in OR throughput across the three departments. Large reductions in operative time of the selected surgery types led to approximately 12%, 33%, and 38% increases in gastrointestinal, thoracic, and orthopedic surgery throughput, respectively. Moderate reductions resulted in 6%-17% increases in throughput and slight reductions of 1%-7%. The model could reliably reflect OR workflows of the three departments. Among the options investigated, model simulations suggest that improving OR preparation time and operative time are the most effective.