Simulation-based Case Study Research Articles

Building Frontline Capability for Shared Decision-Making (SDM) in a Major Academic Oncology Center Caring for People With Non-Small Cell Lung Cancer: Performance Outcomes of a SDM Simulation Training Program.

There is a growing body of evidence on shared decision-making (SDM) training programs worldwide. However, there is wide variation in program design, duration, effectiveness, and evaluation in both academia (ie, medical school) and the practice setting. SDM training has been slow to integrate in practice settings. A pilot study of 6 multidisciplinary clinicians was conducted using quantitative and qualitative methods to evaluate changes in participant understanding and implementation of SDM in the practice setting. A 2-rater criterion-based evaluation method was used to assess a simulation-based case study role-play program using 7 domains of SDM pre and post training. The authors assessed whether clinicians addressed each of the 7 domains or what fraction of each domain was addressed as part of their simulation case study role-play performance. Focus groups were conducted pre- and postintervention to provide feedback to participants and to understand the clinician experience in greater detail. The increase in improvement in SDM ranged from 17% to 37%, and 7 of 8 domains for which participants were rated showed significant improvement. The areas of greatest improvement were seen in determining a patient's goals/preferences, including risk tolerance regarding treatments (+37%) and values and self-efficacy (+37%). The results of this study reveal a significant shift in clinician awareness of a patient's goals, preferences, and values. Postintervention, clinicians began to understand the value of building a partnership with their patients whereby the patient becomes an active participant in their clinical care.

Imposter Phenomenon in Pediatric Residency: An Empirical Simulation-Based Educational Study.

The imposter phenomenon (IP) may have a negative impact on the ability of healthcare providers to make difficult and accurate decisions. This study presents an empirical approach, segregating the real imposters based on a simulation, and aims to investigate the prevalence and severity of IP and evaluate the attitude in the decision-making process of pediatric residents with impostorism. A simulation-based case study with the 113 pediatric residents was performed with the Clance Imposter Phenomenon Scale to identify IP scores and appropriate management skills for the case. The collected data were divided and combined into different categories based on the IP scale scores and the success of case management to further detect how real imposters were affected. Our study revealed that 24 (21.2%) of residents have moderate, 33 (25.7%) of residents have frequent, and 29 (29.2%) of residents have intense impostorism feelings. The imposter scores were found to be higher among female participants (P = .006). However, when considering the prevalence of significant impostorism (defined as both frequent and intense), there was no statistically significant difference between females and males (P = .088). The data indicated that an increase in IP scores was associated with a higher likelihood of delayed pressing the help button for both the overall and post-exclusion groups (P < .001). The analysis also revealed a significant correlation and a monotonic-linear trend between IP scores and the decision-making process, even after excluding the unsuccessful participants (P < .001). This is the first study to demonstrate the prevalence of IP among pediatric residents, potentially leading to challenges in patient care and resulting in delayed decisionmaking and self-doubt with feelings of inadequacy. The findings support the claim that higher imposter scores are associated with a greater tendency to seek help from more experienced individuals, even when fully capable of managing cases independently. This emphasizes the importance of awareness about the IP, as these factors can directly impact both the well-being of trainees and patient care outcomes.

Port Access Fluidity Management during a Major Extension Project: A Simulation-Based Case Study

The increasing demand for freight services and the use of larger vessels to meet this demand has led to challenges related to storage space and logistics activities, highlighting the need for improvements in port infrastructure for better logistics management. At a crucial phase in its growth, the Port of Trois-Rivières in Canada is planning a major expansion, including the construction of a new terminal to enhance its hosting capacities and freight services. This expansion faces potential access congestion problems during the planned construction, exacerbated by the port’s urban setting. In response to the needs identified by the port authorities for this event, the study’s objective is to assess the implications of increased construction and freight truck flows on access gate fluidity and the impact of additional access infrastructure investment to mitigate potential congestion. These evaluations aim to define effective access management strategies throughout the construction period of the new terminal. To address these complexities, our approach is based on scenario analysis in variants co-constructed with the partner. These scenarios are evaluated using simulation models, configured according to parameters calibrated with a granularity that allows congestion detection. The results enabled an evaluation of the capability of existing and potential gates to manage access. Subsequently, recommendations were shaped in accordance with the expected objectives to manage access traffic effectively. These recommendations concern the optimization of construction activity planning, the layout and planning of access, and the importance of enhanced collaboration between municipal and port authorities for more controlled road traffic management. Recognizing the importance of synchromodality, road network centrality management, and the outsourcing of capacity through inter-port cooperation and with dry ports to manage congestion, these tools will be discussed in this work. The study proposes an approach that reconciles scientific rigor with the implementation constraints of the proposed solutions, allowing this study wider applicability in various port contexts facing challenges in this field of study.

Confidence and consistency in discrimination: A new family of evaluation metrics for potential distribution models

Potential distribution models (PDMs) are widely applied to understand and predict biogeographic patterns. PDM evaluation, however, presents major challenges, including (1) matches of predictions with observed absences and presences being treated similarly and (2) treatment of predicted presences falling outside the observations as errors, while a major motivation of PDMs is to identify such locations. Our aim was to construct a family of model performance metrics to measure the reliability and transferability of PDMs while providing solutions to the problems mentioned above.Instead of binarisation, predictions are reclassified into three categories for model evaluation: certain negative, uncertain and certain positive predictions. Model performance is tested solely within known presences to reduce the effect of unoccupied but suitable sites registered as observed absences. Metrics were developed for both cases: (1) when the target of the modelling is the identification of potential presence locations and (2) when the target is the evaluation of potential presences and absences equally.The new measures offer evaluation optimised for PDM models. On the one hand, (1) the proposed metrics concentrate on matches within observed presences. Thus, matches within the typically large amount of observed absences do not inflate the metric values. On the other hand, (2) the proposed metrics do not treat all mismatches within observed presences as errors and thus allow exploitation of information in the mismatches, too. Besides the theoretical background, we also provide a new R package for calculating the measures.We tested the metrics both on field and simulation data. Both field-based and simulation-based case studies underlined that the new metrics capture a different aspect of model performance than the traditional metrics, such as AUC, TSS, sensitivity and specificity.We conclude that our metrics help to identify whether the modelling process could capture the preferences of the object well enough to reliably find further suitable sites or stay reliable when transferred in space and time.

Human-Centered Task Allocation: A Simulation-Based Case Study

This study investigates human-centered task allocation, focusing on factors such as cognitive load, physical demand, and ergonomics. A discrete event simulator was developed to validate the task allocation results of the Q-learning optimization. The simulation evaluates the process and resulting task allocation plan based on predefined human-centered objectives. Through a case study on remanufacturing, we demonstrate how to optimize the coordination of a robotic arm and two human operators to reduce overall workforce requirements. The simulation allows for the analysis of operators’ cognitive and physical workloads over time, enabling exploration of not only typical time balances but also cognitive and physical burdens.

Ride-Hailing Assignment Problem under Waiting Time Uncertainty using Interval-Valued Fuzzy Quadratic

Ride-hailing is a creative idea created by transportation supported by science and technology. Ride-hailing services can help daily community activities. The issue with ride-hailing is that traffic conditions are unpredictable, implying that waiting times are uncertain. The time passengers spend waiting from when they book a ride service until the driver arrives at the pick-up location is called waiting time. This study suggests a quadratic programming technique for minimizing waiting time while accounting for the unpredictability of pick-up travel time. The interval-valued fuzzy quadratic programming method handles the uncertainty and imprecision of the anticipated journey time. When allocating drivers to pick up passengers, interval-valued fuzzy numbers can provide a more realistic representation of waiting time uncertainty. As a result, the interval-valued fuzzy quadratic programming model can handle the uncertainty in waiting time for ride-hailing assignment problems. The model's performance is evaluated using waiting time and the number of people served. The model's performance is demonstrated numerically using the simulation-based case study. This study shows how to utilize a mathematical method to solve real-world problems with uncertainty and improve user welfare.

Impact of real-time pricing and residential load automation on distribution systems

Price-responsive electricity consumption can facilitate the efficient operations of power systems. In this paper, we study the impact of real-time pricing (RTP) and the automation of a major residential load – Heating, Ventilation, and Air Conditioning (HVAC) systems – on residential distribution systems. First, we deploy a simulation-based case study of 437 houses in Austin, Texas, which we calibrate based on real-world data. Second, we study the implications of switching from a fixed retail rate to RTP. We estimate aggregate welfare gains of 39 USD per customer and year. All customers contribute to the welfare gain, but not equally. Moreover, all customers benefit and benefits are higher for customers with electric heating, lower comfort preference, and lower thermal insulation. However, RTP and load automation can critically increase peak system load by more than 30%. Third, we study the introduction of a market-based demand management system under which the local RTP increases if congestion is present. In this case, for reasonable specifications, another 12% of welfare gains can be realized during operations. Moreover, optimal grid investment can be reduced. Our work informs policy makers by illustrating under which conditions load automation under RTP can be particularly beneficial.

Distributed model for the drill-string system with multiple regenerative effects in the bit-rock interaction

In this paper, an integrated model is presented to study the axial–torsional dynamics of a distributed drill string. The coupled axial and torsional drill string dynamics typically lead to severe phenomena such as bit-bounce and stick–slip, which are the main causes of failure and efficiency reduction. The top-drive dynamics and the Bottom Hole Assembly (BHA) dynamics are interconnected through the drill pipes. In order to preserve the infinite-dimensional feature of the drill pipes in the mathematical modeling, a distributed model in terms of Delay Differential Algebraic Equations (DDAE) is proposed to represent the dynamics of the drill string. The bit-rock interaction, which consists of the cutting and the frictional components, is responsible for the coupling between the axial and torsional dynamics. A rate-independent bit-rock interaction law is employed for both cutting and frictional components. In order to capture the global bit motion, including multiple regenerative effects, the well-bottom depth function is used to determine the depth of cut. The well-bottom pattern evolution is formulated through algebraic equations rather than Partial Differential Equations (PDEs). A new formulation for the linearized drill string system in terms of Neutral-type Delay Differential Integral Equations (NDDIEs) is proposed to investigate the initiation of the oscillations around the nominal operation. By this formulation, the local stability of the distributed system is studied by determining the right-most eigenvalue of the linearized dynamics. A simulation-based case study reflecting real-life scenarios is presented to investigate the dynamical behavior of the system under different operating conditions.

Assessment methodology for dynamic occupancy adaptive HVAC control in subway stations integrating passenger flow simulation into building energy modeling

Heating, ventilation, and air conditioning (HVAC) systems in subway stations are often pre-scheduled to non-discriminatorily control large areas, ignoring uneven and dynamic thermal demand. Although building energy modeling (BEM) contributes to optimizing HVAC design and operation, it is limited in investigating dynamic occupancy adaptive HVAC control strategies in large open subway stations. This research formalized an integrated thermal control methodology (ITCM) that integrates passenger flow simulation and BEM to assess the dynamic occupancy adaptive HVAC control strategies for subway stations. To evaluate the applicability and effectiveness of ITCM, we developed a prototype coupling AnyLogic and EnergyPlus and carried out a simulation-based case study using a Hong Kong subway station. Based on the case study, we identified that ITCM enables subway station development and management teams to assess the comprehensive dynamic occupancy adaptive HVAC control strategies (e.g., decomposed thermal zones considering the layouts of diffusers and variable air volume boxes) in a timely and consistent manner. In addition, the case study results show that adaptive control with ceiling diffusers could save between 1.8% and 24.4% of cooling energy without considerably sacrificing thermal comfort.

Effectiveness of simulation-based case studies on knowledge acquisition and clinical judgment in undergraduate nursing students

BackgroundThe development of clinical judgment and nursing knowledge are essential in nursing. However, new graduate nurses do not possess the knowledge and clinical judgment needed to provide safe nursing care. Therefore, nurse educators must find ways to promote the development of clinical judgment and knowledge in nursing students. ObjectivesTo examine the effectiveness of simulation-based case studies as a classroom teaching strategy versus traditional lecture on the development of knowledge acquisition and clinical judgment in undergraduate nursing students. DesignQuasi-experimental two-group, pretest-posttest study. SettingThe study was conducted at a public university in the South-Central region of the United States. ParticipantsA total of 120 students enrolled in a medical-surgical course over two semesters. MethodsStudents in the control group (n = 54) attended a traditional lecture while students in the experimental group (n = 66) participated in simulation-based case studies. A knowledge quiz and the Lasater Clinical Judgment Rubric were completed before and after exposure to the teaching strategy. Data were analyzed using independent samples t-tests. ResultsThere was a significant increase in the knowledge quiz scores found in the experimental group. However, there was not a significant difference found in clinical judgment scores in the experimental group. There was a significant increase in knowledge and clinical judgment scores in both groups. ConclusionBoth attendance in traditional lecture and participation in simulation-based case studies in the classroom are effective in increasing knowledge and clinical judgment scores in undergraduate nursing students.

A Novel Multi Level Dynamic Decomposition Based Coordinated Control of Electric Vehicles in Multimicrogrids

This paper presents a novel tetra-level dynamic decomposition-based control approach for coordinated operation of electric vehicles in multimicrogrids, which is comprehensive, generic, modular, and secure in nature, to maximize the utilization of renewable energy sources, while meeting the load demands with the resources available. There are a number of microgrids that are connected to the grid. Each microgrid consists of a number of renewable energy sources, energy storage systems, non-renewable energy sources, electric vehicles, and loads. Each distributed energy source or load is controlled by a microsource controller. All microsource controllers with a similar nature are controlled by a unit controller, and all the unit controllers in a microgrid are controlled by a microgrid controller. There is a single multimicrogrid controller at the top. The proposed control scheme was verified through simulation-based case studies.

Production Enhancement through Integration of Lean, Life Cycle Assessment &amp; Industry 4.0

Advancement in the manufacturing sector has attained a dominate interest from the researchers as well as the industrialists, for attaining the more products efficiencies. The concept of Lean Manufacturing set the cornerstone for excellence in manufacturing sector by improving the production times and reducing the non-value-added processes. In 2011, the concept of Industry 4.0 pivoted the concept of automation in factories to complement the production improvement processes. The under developing countries such as Pakistan, the manufacturing sector is run with the conventional manufacturing practices, which yields the products of lower quality and much time is being wasted resulting in overall poor efficiency. Moreover, those industries which want to improve their processes are not very much certain, about the methodologies they shall implement. In this research study, the authors have used the mathematical modelling approach of Analytical Hierarchy Processes (AHP) to recognise the pertinent Industry 4.0 technologies and lean perceptions – this technique empowers opportunity of organizing and analysing the intricate decisions for a strong understanding. By using Value Stream Mapping and Automation in a simulation-based case study, improvements of 44.70% in lead time, 17% in value added time and 45.25% in non-value-added time were witnessed. This research explores the avenue of Multi-Criteria Decision-Making (MCDM), based decision making in Industry 4.0 related environments. It will provide clarity to academicians regarding the integration of lean and Industry 4.0 through optimized and logical selection of relevant approaches, in addition to aiding practitioners in intelligent decision making.

Techno-economic assessment of sunflower husk pellets treated with waste glycerol for the Bio-Hydrogen production– A Simulation-based case study

Biomass gasification is a renewable and sustainable energy source with high product yield and economic viability. This study presents a conceptual model for producing biohydrogen from sunflower husks pellets and waste glycerol using the Aspen Plus® simulation tool. The study analyzes the effects of temperature, steam to biomass rate, and CaO rate on the product gas. The results show that increasing the gasifier temperature from 600 to 750 °C increases H2 volume percentage to 92%. Optimizing steam to biomass rate and CaO to biomass rate also affects the volume % of H2 gas in the product gas. The study concludes that a CaO rate of 1.8:1 and a steam-to-biomass ratio of 1.75:1 produce high H2 content with a high heating value of gas. The total capital cost incurred for the system is $2.93 × 106, with a total operating cost of $14.58 × 106/yr.

Fault impact analysis of ventilation systems in residential buildings: A simulation-based case study in Denmark

The spread of intelligent buildings and advanced HVAC systems controls emphasizes the necessity of evaluating the impact of malfunctions caused by errors in the design, installation, or maintenance of these systems. In this work, the effect of the most diffused faults in ventilation systems is studied for an air handling unit installed in a Danish residential apartment. For this purpose, a dynamic model was built in Modelica, representing the building and the ventilation system. Different faults with varying intensities were implemented in the model. The impacts were evaluated by comparing the results of the obtained “with-fault” models with the “fault-free” model used as a benchmark. The comparison involved thermal and electrical energy use, thermal comfort and indoor air quality. Results showed that, for the studied system, the increase in thermal energy use was significant for the offset of the sensor controlling the bypass (up to + 27%), the bypass damper leakage and stuck (up to + 90%) and ducts’ thermal losses (up to + 48%). In the latter case, an increase in thermal discomfort was observed (+7% of hours with operative temperature below 18.5 °C).

Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems

The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g. machine breakdown) occurs. Though multi-agent methods have been proposed to solve the problem in a flexible and agile manner, the agent internal decision-making process and resource uncertainties have rarely been studied. This work introduces a model-based resource agent (RA) architecture that enables effective agent coordination and dynamic agent decision-making. Based on the RA architecture, a rescheduling strategy that incorporates risk assessment via a clustering agent coordination strategy is also proposed. A simulation-based case study is implemented to demonstrate dynamic rescheduling using the proposed multi-agent framework. The results show that the proposed method reduces the computational efforts while losing some throughput optimality compared to the centralised method. Furthermore, the case study illustrates that incorporating risk assessment into rescheduling decision-making improves the throughput.

The Thermal Effect of Various Local Park Settings: A Simulation-Based Case Study of Sunshine Coast, Australia

Local parks are widely used to mitigate urban heat islands. However, the increase of dry weather periods in subtropical climates has caused natural grass failure, leading some city councils to replace it with artificial grass. Therefore, this study investigates various local park settings to assess the performance of different ground covers (i.e., irrigated and non-irrigated natural grass, bare soil, and artificial grass) and the influence that tree proximity has on them, as well as the total thermal performance of the investigated local parks. This study finds that non-irrigated parks could harm park users’ health due to the high concentration of mean radiant temperature (MRT) around trees. Although the surface temperature of the artificial grass was higher (2.60 °C) than that of asphalt during periods of peak temperature, the parks using artificial grass performed better at reducing air temperature and enhancing thermal comfort than the ones with dry grass or bare soil, where both tree proximity and soil water content played significant roles. However, artificial grass negatively affected night cooling, resulting in a poorer daily performance. This study discusses the influence of various park settings on air temperature, MRT, thermal comfort, surface temperature, soil temperature, and water content. In addition, various recommendations and settings are offered to direct decision makers and future studies.

Bi-level ramp merging coordination for dense mixed traffic conditions

Connected and Autonomous Vehicles (CAVs) hold great potential to improve traffic efficiency, emissions and safety in freeway on-ramp bottlenecks through coordination between mainstream and on-ramp vehicles. This study proposes a bi-level coordination strategy for freeway on-ramp merging of mixed traffic consisting of CAVs and human-driven vehicles (HDVs) to optimize the overall traffic efficiency and safety in congested traffic scenarios at the traffic flow level instead of platoon levels. The macro level employs an optimization model based on fundamental diagrams and shock wave theories to make optimal coordination decisions, including optimal minimum merging platoon size to trigger merging coordination and optimal coordination speed, based on macroscopic traffic state in mainline and ramp (i.e., traffic volume and penetration rates of CAVs). Furthermore, the micro level determines the real platoon size in each merging cycle as per random arrival patterns and designs the coordinated trajectories of the mainline facilitating vehicle and ramp platoon. A receding horizon scheme is implemented to accommodate human drivers’ stochastics as well. The developed bi-level strategy is tested in terms of improving efficiency and safety in a simulation-based case study under various traffic volumes and CAV penetration rates. The results show the proposed coordination addresses the uncertainties in mixed traffic as expected and substantially improves ramp merging operation in terms of merging efficiency and traffic robustness, and reducing collision risk and emissions, especially under high traffic volume conditions.

Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging

Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.

Mitigating peak load and heat stress under heatwaves by optimizing adjustments of fan speed and thermostat setpoint

ABSTRACT Heatwaves are becoming more frequent and severe, intensifying cooling demand and reducing air conditioner efficiencies. This causes peaks in electricity demand that pose operational challenges to power grids. This paper provides methods to mitigate demand peaks and heat stress under heatwaves by jointly adjusting fan speeds and thermostat setpoints in buildings. The methods involve (1) learning baseline models to predict load and thermal comfort, (2) fitting perturbation models that relate fan speed and thermostat setpoint adjustments to perturbations in load and thermal comfort, and (3) optimizing peak load and thermal comfort. The methods are implementable in real buildings, providing fast, accurately predicted optimized solutions that flatten demand peaks and mitigate personal heat stress. This paper demonstrates the methodology through simulation-based case studies of a single building and a six-building neighbourhood. In case studies, the methods reduce peak load by 8–10% while maintaining occupants' thermal comfort within safe and comfortable ranges. Highlights This paper develops data-driven methods to reduce peak demand and mitigate heat stress during heatwaves. The methods are designed for straightforward implementation in the field. In case studies, the methods reduce peak demand by 8–10% while maintaining thermal comfort within safe and comfortable ranges. To achieve the same level of peak load reduction, jointly adjusting fan speed, rather than solely thermostat setpoint, improves thermal comfort by 5% in the test case.

Grammatical-Evolution-based parameterized Model Predictive Control for urban traffic networks

While Model Predictive Control (MPC) is a promising approach for network-wide control of urban traffic, the computational complexity of the, often nonlinear, online optimization procedure is too high for real-time implementations. In order to make MPC computationally efficient, this paper introduces a parameterized MPC (PMPC) approach for urban traffic networks that uses Grammatical Evolution to construct continuous parameterized control laws using an effective simulation-based training framework. Furthermore, a projection-based method is proposed to remove the nonlinear constraints that are imposed on the parameters of the parameterized control laws and to guarantee the feasibility of the solution of the MPC optimization problem. The performance and computational efficiency of the constructed parameterized control laws are compared to those of a conventional MPC controller in an extensive simulation-based case study. The results show that the parameterized control laws, which are automatically constructed using Grammatical Evolution, decrease the computational complexity of the online optimization problem by more than 80% with a decrease in performance by less than 10%.