Discrete Event Simulation Model Research Articles

Design complexity based flexible order dispatching for additive manufacturing production

Understanding design complexity for additive manufacturing (AM) is essential in AM production planning since conventional make-to-order production for individual AM orders of complex designs can amplify operational uncertainty in an entire AM production system. As a response, this study aims not only to demonstrate the impact of design complexity on AM production but also to propose a novel order dispatching approach based on design complexity that mitigates operational uncertainty in an AM production system. First, a design complexity measure was developed using an information theoretic approach. Next, a discrete-event simulation model to represent an AM production system consisting of parallel AM machines for jet-engine bracket designs was built to identify the impact of design complexity on average order lead time and total production cost through regressions. Finally, a flexible order dispatching rule that reflects operational attitudes toward design complexity was proposed to determine part-processing priorities by tracking both part- and system-level design complexity states in a centralized queue for AM production. The proposed dispatching rule was compared with relevant static dispatching rules to assess its performance in operational efficiency under varied attitudes toward design complexity. The findings from this study clearly showed the negative impact of design complexity on operational performance for AM production. Moreover, the proposed dispatching rule resulted in lead time reduction and balanced lead time performance in AM production against alternative static dispatching strategies. This study demonstrates the importance of design complexity-based flexible operations to properly handle latent uncertainties in an AM production system.

A quality improvement study on how a simulation model can help decision making on organization of ICU wards

BackgroundIntensive Care Unit (ICU) capacity management is essential to provide high-quality healthcare for critically ill patients. Yet, consensus on the most favorable ICU design is lacking, especially whether ICUs should deliver dedicated or non-dedicated care. The decision for dedicated or non-dedicated ICU design considers a trade-off in the degree of specialization for individual patient care and efficient use of resources for society. We aim to share insights of a model simulating capacity effects for different ICU designs. Upon request, this simulation model is available for other ICUs.MethodsA discrete event simulation model was developed and used, to study the hypothetical performance of a large University Hospital ICU on occupancy, rejection, and rescheduling rates for a dedicated and non-dedicated ICU design in four different scenarios. These scenarios either simulate the base-case situation of the local ICU, varying bed capacity levels, potential effects of reduced length of stay for a dedicated design and unexpected increased inflow of unplanned patients.ResultsThe simulation model provided insights to foresee effects of capacity choices that should be made. The non-dedicated ICU design outperformed the dedicated ICU design in terms of efficient use of scarce resources.ConclusionsThe choice to use dedicated ICUs does not only affect the clinical outcome, but also rejection- rescheduling and occupancy rates. Our analysis of a large university hospital demonstrates how such a model can support decision making on ICU design, in conjunction with other operation characteristics such as staffing and quality management.

The Spillover Effects of Extending Liver Transplantation to Patients with Colorectal Liver Metastases: A Discrete Event Simulation Analysis.

Liver transplantation is an alternative treatment for patients with nonresectable colorectal cancer liver-only metastases (CRLM); however, the potential effects on wait-list time and life expectancy to other patients on the transplant waiting list have not been considered. We explored the potential effects of expanding liver transplantation eligibility to include patients with CRLM on wait-list time and life expectancy in Norway. We developed a discrete event simulation model to reflect the Norwegian liver transplantation waiting list process and included 2 groups: 1) patients currently eligible for liver transplantation and 2) CRLM patients. Under 2 alternative CRLM-patient transplant eligibility criteria, we simulated 2 strategies: 1) inclusion of only currently eligible patients (CRLM patients received standard-of-care palliative chemotherapy) and 2) expanding waiting list eligibility to include CRLM patients under 2 eligibility criteria. Model outcomes included median waiting list time, life expectancy, and total life-years. For every additional CRLM patient listed per year, the overall median wait-list time, initially 52 d, increased by 8% to 11%. Adding 2 additional CRLM patients under the most restrictive eligibility criteria increased the CRLM patients' average life expectancy by 10.64 y and decreased the average life expectancy for currently eligible patients by 0.05 y. Under these assumptions, there was a net gain of 149.61 life-years over a 10-y programmatic period, which continued to increase under scenarios of adding 10 CRLM patients to the wait-list. Health gains were lower under less restrictive CRLM eligibility criteria. For example, adding 4 additional CRLM patients under the less restrictive eligibility criteria increased the CRLM patients' average life expectancy by 5.64 y and decreased the average life expectancy for currently eligible patients by 0.12 y. Under these assumptions, there was a net gain of 96.36 life-years over a 10-y programmatic period, which continued to increase up to 7 CRLM patients. Our model-based analysis enabled the consideration of the potential effects of enlisting Norwegian CRLM patients for liver transplantation on wait-list time and life expectancy. Enlisting CRLM patients is expected to increase the total health effects, which supports the implementation of liver transplantation for CRLM patients in Norway. Given the Norwegian donor liver availability, adding patients with nonresectable colorectal cancer liver-only metastases (CRLM) to the liver transplantation waiting list had an overall modest, but varying, impact on total waiting list time.Survival gains for selected CRLM patients treated with liver transplantation would likely outweigh the losses incurred to patients listed currently.To improve the total life-years gained in the population, Norway should consider expanding the treatment options for CRLM patients to include liver transplantation.Other countries may also have an opportunity to gain total life-years by extending the waiting list eligibility criteria; however, country-specific analyses are required.

Discrete Event Simulation untuk Analisis Pelayanan Bisnis Kuliner (Studi Kasus : Gacoan Merr)

A queue is a group of people or goods in a line waiting to be served. It can occur when the number of visitors is greater than the number of serving resources. One of these queuing phenomena can be found at one of the fast food outlets, namely Gacoan Merr Surabaya. This study aims to identify and analyze the factors that affect queues and propose a more efficient strategy to solve queuing problems at Gacoan Merr Surabaya. The simulation model used is a discrete-event simulation model. The method in this study has stages that include problem analysis, literature study, observation, system modeling, and proposed solutions. The variables used in the research object include the time interval of arrival, the number of customers, the total number of servers in the ordering service process, and the total number of servers in the food manufacturing process, for a total of eight people. From the results of system modeling, it is found that the use of eight servers in the product processing phase can still be said to be less effective where the food distribution time also takes longer. Therefore, changes are needed in the product or food manufacturing phase by adding the number of servers used to process orders. The increase in the number of servers shows a decrease in the number of order queues in the product processing phase, and the food distribution time becomes faster.

A decision support framework to improve flow management in a supply chain subject to risks

ABSTRACT This research aims at developing an approach to assess the probability of meeting customer service level, while considering various risk factors present in a supply chain (supply disruptions, uncertainties in lead times, labour shortages, transport disruptions etc.). The work is conducted in tight collaboration with a leading actor of an aeronautical industry. Our model enables a better management of supply chain flows by improving two KPIs of interest for the company that operates in a MTO environment: on-time delivery (OTD) and depth of delay (DOD), by varying the repartition of flows between possible production and transportation modes. Change in transportation mode is indeed considered as a risk mitigation lever leading to sustainable operations. We first collect input data, which includes data related to the characteristics of the system under study (flow diagram, data pertaining to parameters such as lead time, cost, capacity…) and supply chain risks mapping. Then, we develop a discrete event simulation (DES) model considering supply chain risks to determine the best flow repartition enabling to satisfy KPIs considered. A performance evaluation module enables to calculate the probability distribution of the chosen KPIs. Finally, a multi-scenario analysis is conducted by varying different parameters that have an impact on OTD, DOD and the total cost.

Greening container terminals: An innovative and cost-effective solution for sustainable reefer container storage

This research addresses the critical necessity for energy-efficient solutions in port operations. The primary objective of this paper is to introduce and assess the viability of an innovative infrastructure termed Underground Reefer Container Storage (URCS) devised to mitigate the significant and increasing energy demand posed by reefer containers in ports. This study employs a discrete-event simulation (DES) model to evaluate the feasibility, environmental implications, and cost-effectiveness of three distinctive URCS infrastructure designs. These designs are compared against conventional storage methods to ascertain their capacity for minimizing energy usage and greenhouse gas emissions. The analysis reveals that the best scenario for a specific case study involves a multi-block layout comprising two underground yards, capable of accommodating 39% of the overall refrigerated container capacity at a port. This layout cuts CO2 emissions by 61% from the 2.704 GWh/year energy used by refrigerated containers, yielding around 1.4 million USD/year cost savings. Further analyses using weighted searches to prioritize URCS container slotting led to shorter container dwell times at ports, rendering energy savings 23% higher than in similar unplanned settings. Moreover, this study presents URCS as an eco-friendly alternative for port-based reefer container storage, offering practical alignment with sustainability goals and regulations. The innovative design and its successful application provide holistic insights, paving the way for enhanced port energy management and environmental care. In addition, the implementation of URCS has the potential to reshape port procedures, yielding economic and ecological benefits. This initiative contributes to diminishing the carbon footprint of ports, aligning with global endeavors towards greener and more responsible practices.

Cost-effectiveness of the McGill interactive pediatric oncogenetic guidelines in identifying Li-Fraumeni syndrome in female patients with osteosarcoma.

Li-Fraumeni syndrome (LFS) is a penetrant cancer predisposition syndrome (CPS) associated with the development of many tumor types in young people including osteosarcoma and breast cancer (BC). The McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) decision-support tool provides a standardized approach to identify patients at risk of CPSs. We conducted a cost-utility analysis, from the healthcare payer perspective, to compare MIPOGG-guided, physician-guided, and universal genetic testing strategies to detect LFS in female patients diagnosed at an age of less than 18years with osteosarcoma. We developed a decision tree and discrete-event simulation model to simulate the clinical and cost outcomes of the three genetic referral strategies on a cohort of female children diagnosed with osteosarcoma, especially focused on BC as subsequent cancer. Outcomes included BC incidence, quality-adjusted life-years (QALYs), healthcare costs, and incremental cost-utility ratios (ICURs). We conducted probabilistic and scenario analyses to assess the uncertainty surrounding model parameters. Compared to the physician-guided testing, the MIPOGG-guided strategy was marginally more expensive by $105 (-$516; $743), but slightly more effective by 0.003 (-0.04; 0.045) QALYs. Compared to MIPOGG, the universal testing strategy was $1333 ($732; $1953) more costly and associated with 0.011 (-0.043; 0.064) additional QALYs. The ICUR for the MIPOGG strategy was $33,947/QALY when compared to the physician strategy; the ICUR for universal testing strategy was $118,631/QALY when compared to the MIPOGG strategy. This study provides evidence for clinical and policy decision-making on the cost-effectiveness of genetic referral strategies to identify LFS in the setting of osteosarcoma. MIPOGG-guided strategy was most likely to be cost-effective at a willingness-to-pay threshold value of $50,000/QALY.

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.

Anti–Vascular Endothelial Growth Factor Drugs Compared With Panretinal Photocoagulation for the Treatment of Proliferative Diabetic Retinopathy: A Cost-Effectiveness Analysis

ObjectivesThis study aimed to evaluate the cost-effectiveness of anti–vascular endothelial growth factor drugs (anti-VEGFs) compared with panretinal photocoagulation (PRP) for treating proliferative diabetic retinopathy (PDR) in the United Kingdom. MethodsA discrete event simulation model was developed, informed by individual participant data meta-analysis. The model captures treatment effects on best corrected visual acuity in both eyes, and the occurrence of diabetic macular edema and vitreous hemorrhage. The model also estimates the value of undertaking further research to resolve decision uncertainty. ResultsAnti-VEGFs are unlikely to generate clinically meaningful benefits over PRP. The model predicted anti-VEGFs be more costly and similarly effective as PRP, generating 0.029 fewer quality-adjusted life-years at an additional cost of £3688, with a net health benefit of −0.214 at a £20 000 willingness-to-pay threshold. Scenario analysis results suggest that only under very select conditions may anti-VEGFs offer potential for cost-effective treatment of PDR. The consequences of loss to follow-up were an important driver of model outcomes. ConclusionsAnti-VEGFs are unlikely to be a cost-effective treatment for early PDR compared with PRP. Anti-VEGFs are generally associated with higher costs and similar health outcomes across various scenarios. Although anti-VEGFs were associated with lower diabetic macular edema rates, the number of cases avoided is insufficient to offset the additional treatment costs. Key uncertainties relate to the long-term comparative effectiveness of anti-VEGFs, particularly considering the real-world rates and consequences of treatment nonadherence. Further research on long-term visual acuity and rates of vision-threatening complications may be beneficial in resolving uncertainties.

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.