Discrete Event Simulation Research Articles

Design of experiment and simulation approach for analyzing automated guided vehicle performance indicators in a production line

Several manufacturing industries try to reduce transportation waste using automated material handling systems, which can enhance the transportation of raw materials from one location to another in the production line of a manufacturing area. The issue with transportation and job flow is a critical factor in a production line because some production stations need to wait for the work-in-progress to be delivered. Automated guided vehicle (AGV) transportation needs a setup of traffic control over a factory’s physical infrastructure and simulation. Doing so can help showcase and evaluate possible deficiencies that can be improved in the real job flow scenario of the production line. The design of experiment plays a huge role in finding and explaining variations of information under conditions that are regularly put as a hypothesis to reflect or describe the variation. A simulation model is implemented by adopting simplified AGV parameters. The model development follows the structure of system specification → machine specification → AGV specification → discrete-event simulation model → experimental design → analysis of performance indicators (PIs). To precisely reflect an alternative for evaluating aforementioned issues, this study proposes the model stated above and an analysis that is based on the PIs. Analysis of variance (ANOVA) results are chosen to analyze different factors affecting the PIs. Using the factorial ANOVA test results, this study uses one-way and two-way interactions to compare the relationship between job flow time, AGVs, AGV utilization, number of AGVs, and average waiting time.

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

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

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

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

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

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

HSD118 Patient Safety in Oncology: Quantifying the Hospital-Associated Risks with a Discrete-Events Simulation

HSD118 Patient Safety in Oncology: Quantifying the Hospital-Associated Risks with a Discrete-Events Simulation

Optimizing Undergraduate Pilot Training 2.5 at Vance Air Force Base

With a continued increase in demand for qualified pilots, in an environment of continual budget constraints, the Air Education Training Command has been searching for ways to streamline Undergraduate Pilot Training (UPT). Leadership has recently implemented UPT 2.5 to train pilots more effectively. However, the 8th and 33rd Flight Training Squadrons at Vance Air Force Base have had issues efficiently cycling students through. Although UPT 2.5 specifies that it takes no more than 90 days for students to complete the post-preflight portion of the syllabus, it currently takes 126 days on average. We developed a discrete event simulation model of pilot training to identify bottlenecks in the current system, find an optimal class size given current resources, and optimize resources to graduate 32 students per class, the number required by AETC. The results will inform recommendations to increase efficiency within UPT and reduce the pilot shortage.

A discrete event simulation model for airport runway operations optimisation: A case study of Cairo International Airport

Runways are the major crucial infrastructure for all airports around the world. The efficient handling of runway operations is the key to ensuring that airports are running smoothly with minimal delays and reduced queuing time on both approaches and departures. Many factors affect the efficiency of runway operations, such as the aircraft wake separation, runway system configuration, the number of runways and the fleet mix. This paper aims to address how to maximise runways operations by using the discrete event simulation model (DESM) at Cairo International Airport (HECA). The study has applied the model to evaluate the operational performance of an airport with three parallel runway setups to compare the result of the DESM with the actual runway performance in different operational scenarios. As HECA has provided its results from historical operations, the proposed scenarios will help to set the new benchmark to explore the opportunities for runways performance improvements at the airport.

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

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

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

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

Development and application of simulation modelling for orthopaedic elective resource planning in England

ObjectivesThis study aimed to develop a simulation model to support orthopaedic elective capacity planning.MethodsAn open-source, generalisable discrete-event simulation was developed, including a web-based application. The model used anonymised patient records...

Comparison study of dispatching rules and heuristics for online scheduling of single machine scheduling problem with predicted release time jobs

In real-life dynamic scheduling systems, the release times of some important and/or urgent jobs are usually known deterministically or predictably in advance. When making dynamic scheduling decisions, we are expected to get better scheduling solutions if these jobs are considered before they actually arrive. However, to the best of our knowledge, we have not yet found corresponding research to date. This paper addresses a dynamic single machine scheduling problem with predicted arriving jobs (DSMSP/PRJs). We give a detailed description and the corresponding mathematical programming model with the objective of minimizing total weighted tardiness (TWT). With respect to the inclusion strategy of PRJs, which is one of the key technologies affecting the solution quality, we propose a job-based inclusion (JBI) procedure that focuses on the influence on the tardiness of PRJs rather than on a certain length of lookahead window. The novel JBI procedure shows obvious superior performance to the time-based method in the literature with respect to TWT. The discrete event simulation based on dispatching rules/heuristics (RHs) is adopted to address the DSMSP/PRJs. Six weighted dispatching rules and two improved heuristics based on the DT-TD (Decision Theory-Tactically Delayed) are proposed. Large-scale and systematic experiments show that the proposed RHs perform well. Especially, the DT-TD2 heuristic has overwhelming superior performance to the others. For the huge volumes of experimental raw data produced by 1350 simulation runs under 135 problem scenarios, we innovatively put forward the overall procedure and methods of data processing and analysis oriented to the different purposes. The systematic data analyses show that, under the various problem scenarios, considering PRJs in advance can significantly improve the scheduling solutions as long as appropriate RHs are used. The analysis results also reveal that, in the scenarios with lower shop utilization, lower percentage of PRJ, higher weight of PRJ, and looser due date of normal job (NJ), it is more obvious in the improvement of solutions, implying that it is more necessary to consider PRJs in advance.

Cost-effectiveness of Prefusion F Protein-based Vaccines Against Respiratory Syncytial Virus Disease for Older Adults in the United States.

Two prefusion F protein-based vaccines, Arexvy and Abrysvo, have been authorized by the US Food and Drug Administration for protecting older adults against respiratory syncytial virus (RSV)-associated lower respiratory tract illness. We evaluated the health benefits and cost-effectiveness of these vaccines. We developed a discrete-event simulation model, parameterized with the burden of RSV disease including outpatient care, hospitalization, and death for adults aged 60 years or older in the United States. Taking into account the costs associated with these RSV-related outcomes, we calculated the net monetary benefit using quality-adjusted life-year (QALY) gained as a measure of effectiveness and determined the range of price-per-dose (PPD) for Arexvy and Abrysvo vaccination programs to be cost-effective from a societal perspective. Using a willingness-to-pay of $95 000 per QALY gained, we found that vaccination programs could be cost-effective for a PPD up to $127 with Arexvy and $118 with Abrysvo over the first RSV season. Achieving an influenza-like vaccination coverage of 66% for the population of older adults in the United States, the budget impact of these programs at the maximum PPD ranged from $6.48 to $6.78 billion. If the benefits of vaccination extend to a second RSV season as reported in clinical trials, we estimated a maximum PPD of $235 for Arexvy and $245 for Abrysvo, with 2-year budget impacts of $11.78 and $12.25 billion, respectively. Vaccination of older adults would provide substantial direct health benefits by reducing outcomes associated with RSV-related illness in this population.

The inclusion of uncertainty in circularity transition modeling: A case study on wind turbine blade end-of-life management

The transition to a more circular economy (CE) is complex and hard to predict, including in sustainable energy technologies. Many sources of uncertainty make it challenging to model CE scenarios and their potential benefits. As an example, the high variability in costs and revenues of different recycling options makes future wind turbine blade recycling highly uncertain. To better understand this challenge, the circular economy, life cycle assessment and visualization (CELAVI) framework – a discrete event simulation and life cycle assessment framework – is modified to incorporate uncertainty analysis capabilities. Moreover, a 3-step procedure that covers different aspects of uncertainty in CE studies and includes a Monte-Carlo analysis is proposed. The procedure is tested in a case study on wind turbine blade recycling using CELAVI. Results highlight that grinding and landfilling costs are the most influential parameters for wind power circularity. The model’s output coefficients of variation (when input parameter uncertainties are propagated) are between 92% and 384% depending on the indicator. The approach developed in this study may help researchers and decision-makers who study circularity prioritize their data collection effort. Finally, our method contributes to a mounting yet critical body of research: the measurement of uncertainty in circularity transitions.

Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes

ABSTRACT Throughput is an important parameter to evaluate production system performance. It is typically constrained by one or more resources referred to as ‘throughput bottlenecks’. To start improvement actions, the first step is to identify throughput bottlenecks. Consequently, several bottleneck detection methods were developed in the literature. But this literature remains largely unstructured, which makes it difficult for practitioners to select an appropriate method. To generate clarity and to consolidate the field, a systematic literature review was conducted. The review identified 14 different bottleneck detection methods that are classified according to the information used: queue states, process states, or combined queue and process states. It further identified three different modes used to operationalize the different bottleneck detection methods: gemba walk, discrete event simulation, and data science. This study further presents important research issues, identifies contingency factors for method application, and discusses important guidelines for the choice of operationalization mode in practice.

A method for developing and validating simulation models for automated storage and retrieval system digital twins

Automated storage and retrieval systems have become increasingly popular in modern supply chains due to their significant advantages over traditional warehousing systems. Due to the high complexity of these systems, simulation approaches can be used to generate accurate performance measures for a specific system configuration. Simulation models are also the cornerstone of digital twins, one of the latest technological innovations that can further improve warehouse operations. Therefore, the aim of this research is to describe an approach for the development of a discrete event simulation model of an automated storage and retrieval system with a perspective towards the implementation of a digital twin. To be consistent with the objectives of the digital twin, the proposed model represents both the physical system and the overarching information technology architecture, such as the warehouse management system and the warehouse control system. In addition, this paper describes a methodology to validate such a simulation model by setting up an experimental campaign based on the principles of design of experiment. The experiments conducted in a logistics laboratory were used to iteratively calibrate the model until its performance accurately reflected the functioning of the real system. The results obtained demonstrate the effectiveness of the proposed method. Finally, this work contributes to the literature on warehouse digital twins by highlighting new variables to be considered when defining travel time models and their stochastic nature.

Quantization-based simulation of spiking neurons: theoretical properties and performance analysis

ABSTRACT In this work we present an exhaustive analysis of the use of Quantized State Systems (QSS) algorithms for the discrete event simulation of Leaky Integrate and Fire models of spiking neurons. Making use of some properties of these algorithms, we first derive theoretical error bounds for the sub-threshold dynamics as well as estimates of the computational costs as a function of the accuracy settings. Then, we corroborate those results on different simulation experiments, where we also study how these algorithms scale with the size of the network and its connectivity. The results obtained show that the QSS algorithms, without any type of optimisation or specialisation, obtain accurate results with low computational costs even in large networks with a high level of connectivity.

Generation of material handling system alternatives: A constraints satisfaction problem approach

In the context of Industry 4.0, the design of efficient Material Handling Systems (MHS) plays a critical role in optimizing industrial operations and enhancing productivity. The integration of advanced technologies, automation, and complex systems has revolutionized industrial processes, emphasizing the need for defining coherent MHS alternatives prior to the deployment of a specific solution. This paper presents an integrated MHS alternative generation approach that combines MHE allocation, fleet sizing, and key performance indicator (KPI)-based evaluation to enhance MHS. The proposed approach deploys a Constraint Satisfaction Problem (CSP) framework, which defines the conditions that must be satisfied by the values assigned to the MHE allocation and fleet sizing variables. The constraints capture the physical and operational characteristics of MHE and MHS, ensuring that the generated alternatives adhere to the specific requirements and limitations of the system. Once a set of feasible MHS alternatives complying with various constraints is established, further assessments can be carried out to gauge the dynamic behavior of these systems using methods like Discrete Event Simulation. The paper emphasizes the importance of incorporating a broader range of data categories, such as layout constraints, product characteristics, and Industry 4.0 challenges, to enhance the effectiveness and accuracy of the MHS design process. To evaluate the generated alternatives, a set of KPIs is proposed, encompassing operational, economic, environmental, safety, and security aspects. These KPIs enable decision-makers to assess alternative performance across multiple metrics, aiding in the identification of relevant solutions aligned with company objectives. The incorporation of KPIs provides a pre-evaluation of MHS alternatives, streamlining subsequent simulation and multicriteria decision-making processes. This research acknowledges the importance of alternative generation as an integral part of the broader MHS design problem.

Using Discrete-Event Simulation to Balance Staff Allocation and Patient Flow between Clinic and Surgery

We consider the problem of system-level balanced scheduling in a pediatric hospital setting. A hospital clinic has a queue for patients needing care. After being seen in clinic, many require follow-up surgery, for which they also wait in a queue. The rate-limiting factor is physician availability for both clinic visits and surgical cases. Although much existing work has been done to optimize clinic appointments, as well as to optimize surgical appointments, this novel approach models the entire patient journey at the system level, through both clinic and surgery, to optimize the total patient experience. A discrete-event simulation model of the system was built based on historic patient encounter data and validated. The system model was then optimized to determine the best allocation of physician resources across the system to minimize total patient wait time using machine learning. The results were then compared to baseline.

ENCache: Improving content delivery with cooperative caching in Named Data Networking

Content centric networks offer better response time and resilience to failures by caching the content in the network routers. Owing to the limited capacity of caches and the quest for maximizing the utilization of available caches has lead to many interesting caching techniques. While on-path caching is a commonly adopted caching technique to reduce content access latency, it does not guarantee better cache hit ratio when considered across multiple receivers in the network. In this paper, we present eNCache which is a cooperative caching method that cooperates among the routers in the neighborhood to improve overall performance. It retains the simplicity of on-path caching and makes caching, routing (queries and replies) decisions by interacting with off-path routers in the neighborhood. We present the working of eNCache with algorithms for request handling and caching decisions at every router. We also formalize the interaction between a router and its neighbors by providing an extension to the Named Data Networking Interest packet structure. We evaluate and compare the performance of eNCache with other popular on-path caching strategies by performing simulations on standard RocketFuel Internet Service Provider (ISP) topologies using a discrete event simulator. We also compare the performance of a variant of enhanced NCache, i.e.,eNCache with hash based off-path caching methods. Our simulation results show that eNCache has reduced latency, better cache hit ratio and diversity.

Simulating the Software Development Lifecycle: The Waterfall Model

This study employs a simulation-based approach, adapting the waterfall model, to provide estimates for software project and individual phase completion times. Additionally, it pinpoints potential efficiency issues stemming from suboptimal resource levels. We implement our software development lifecycle simulation using SimPy, a Python discrete-event simulation framework. Our model is executed within the context of a software house on 100 projects of varying sizes examining two scenarios. The first provides insight based on an initial set of resources, which reveals the presence of resource bottlenecks, particularly a shortage of programmers for the implementation phase. The second scenario uses a level of resources that would achieve zero-wait time, identified using a stepwise algorithm. The findings illustrate the advantage of using simulations as a safe and effective way to experiment and plan for software development projects. Such simulations allow those managing software development projects to make accurate, evidence-based projections as to phase and project completion times as well as explore the interplay with resources.