scholarly journals Rapid-cycle systems modeling to support evidence-informed decision-making during system-wide implementation

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
R. Christopher Sheldrick ◽  
Gracelyn Cruden ◽  
Ana J. Schaefer ◽  
Thomas I. Mackie
Keyword(s):  
Simulation Model ◽  
Simulation Modeling ◽  
Implementation Strategy ◽  
Simulation Models ◽  
Illustrative Case ◽  
Systems Modeling ◽  
Cycle Systems ◽  
Analytic Strategy ◽  
Group Interviews

Abstract Background To “model and simulate change” is an accepted strategy to support implementation at scale. Much like a power analysis can inform decisions about study design, simulation models offer an analytic strategy to synthesize evidence that informs decisions regarding implementation of evidence-based interventions. However, simulation modeling is under-utilized in implementation science. To realize the potential of simulation modeling as an implementation strategy, additional methods are required to assist stakeholders to use models to examine underlying assumptions, consider alternative strategies, and anticipate downstream consequences of implementation. To this end, we propose Rapid-cycle Systems Modeling (RCSM)—a form of group modeling designed to promote engagement with evidence to support implementation. To demonstrate its utility, we provide an illustrative case study with mid-level administrators developing system-wide interventions that aim to identify and treat trauma among children entering foster care. Methods RCSM is an iterative method that includes three steps per cycle: (1) identify and prioritize stakeholder questions, (2) develop or refine a simulation model, and (3) engage in dialogue regarding model relevance, insights, and utility for implementation. For the case study, 31 key informants were engaged in step 1, a prior simulation model was adapted for step 2, and six member-checking group interviews (n = 16) were conducted for step 3. Results Step 1 engaged qualitative methods to identify and prioritize stakeholder questions, specifically identifying a set of inter-related decisions to promote implementing trauma-informed screening. In step 2, the research team created a presentation to communicate key findings from the simulation model that addressed decisions about programmatic reach, optimal screening thresholds to balance demand for treatment with supply, capacity to start-up and sustain screening, and availability of downstream capacity to provide treatment for those with indicated need. In step 3, member-checking group interviews with stakeholders documented the relevance of the model results to implementation decisions, insight regarding opportunities to improve system performance, and potential to inform conversations regarding anticipated implications of implementation choices. Conclusions By embedding simulation modeling in a process of stakeholder engagement, RCSM offers guidance to realize the potential of modeling not only as an analytic strategy, but also as an implementation strategy.

scholarly journals Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study

2021 ◽  
Vol 11 (8) ◽  
pp. 3487
Author(s):  
Helge Nordal ◽  
Idriss El-Thalji
Keyword(s):  
Simulation Model ◽  
Simulation Modeling ◽  
Study Data ◽  
Opportunistic Maintenance ◽  
Maintenance Strategies ◽  
Modeling Approach ◽  
Intelligent Maintenance ◽  
Diagnosis And Prognosis ◽  
Case Study Data

The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times.

scholarly journals Simulation Modeling of Aerial Work Completed by Helicopters in the Construction Industry Focused on Weather Conditions

2021 ◽  
Vol 13 (24) ◽  
pp. 13671
Author(s):  
Andrej Bisták ◽  
Zdenka Hulínová ◽  
Michal Neštiak ◽  
Barbara Chamulová
Keyword(s):  
Simulation Model ◽  
National Parks ◽  
Simulation Modeling ◽  
Simulation Models ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Natural Conditions ◽  
Dominant Feature ◽  
Numerical Weather Forecast ◽  
The Given

The aim of this research was to develop a simulation model of the works carried out by helicopters, which are used in the construction of buildings under harsh natural conditions. This work identified that even technologies that we do not normally encounter, such as aerial work using helicopters, can have a major impact on ensuring the requirement of sustainability within the overall environmental and economic context. In the environment of protected landscape areas and national parks, in particular, where all sites are sensitive to human intervention, the use of helicopters in construction functions is an irreplaceable aid. Preparations for aerial work are very demanding and require the use of more sophisticated tools to achieve optimal results consistent within the paradigm of long-term sustainability. Simulation modeling is one such option, thanks to the considerable advancements made in information technology. A simulation model of aerial work was compiled within the presented work, and its functionality was verified using specific examples that confirmed in full the suitability of using simulations in the preparation of aerial work within construction. A detailed analysis of helicopter operations showed that an algorithm that accounts for future weather conditions at the construction site, and specifically focused on the conditions at the given altitude above the ground, should be a dominant feature of simulation models. It is exceptionally important that such data be known within the preparations for aerial work as accurately as possible, and, as such, this article describes the process of obtaining meteorological information for simulation models in detail using a numerical weather forecast and the reliability of data obtained in this manner. Based on the results obtained during this research, the proposed simulation model can be recommended as a suitable tool in the preparation of buildings. Its use is especially important if construction takes place under difficult natural conditions, where work cannot be carried out without the use of helicopters. We perceive the simulation model as a potential tool for digitizing construction preparations in the age of Industry 4.0.

Method to Control Manufacturing Cell by Driving Simulation Model

2014 ◽  
Vol 8 (4) ◽  
pp. 539-549 ◽  
Author(s):  
Hironori Hibino ◽  
Keyword(s):  
Simulation Model ◽  
Manufacturing System ◽  
Simulation Models ◽  
Driving Simulation ◽  
Manufacturing Cell ◽  
Manufacturing Management ◽  
Execution Phase ◽  
Manufacturing Engineering ◽  
Wiring System

In this paper, a method to Control a Manufacturing Cell by Driving Simulation Models (CMC-DSM) is proposed. The purposes of CMC-DSM is not only to directly operate the manufacturing cell while controlling and monitoring the manufacturing cell based on a simulation model in the manufacturing system execution phase, but also to support the manufacturing engineering processes based on the simulation model. In the manufacturing engineering processes, the simulation model is mixed and synchronized with real equipment and management applications in the case where parts of equipment and manufacturing management applications are not provided in the manufacturing cell. In the manufacturing system execution phase, when the simulation model acts in response to manufacturing system behaviors, the manufacturing system is controlled by synchronizing the simulation model behaviors. In this paper, the Environment required to Control a Manufacturing Cell by Driving Simulation Models (E-CMC-DSM) is proposed. The necessary functions for E-CMC-DSM are defined and developed. E-CMC-DSM consists of a simulator developed to drive simulation models (EMU), a soft-wiring system developed in this study, and a semi-standard industrial network middleware. The validation of ECMC-DSM was carried out through a case study.

Application of an Interactive Water Simulation Model in urban water management: a case study in Amsterdam

2014 ◽  
Vol 70 (11) ◽  
pp. 1729-1739 ◽  
Author(s):  
J. G. Leskens ◽  
M. Brugnach ◽  
A. Y. Hoekstra
Keyword(s):  
Water Management ◽  
Simulation Model ◽  
Simulation Models ◽  
Decision Makers ◽  
Urban Water ◽  
Urban Water Management ◽  
Current Water ◽  
Water Simulation ◽  
Computation Algorithms

Water simulation models are available to support decision-makers in urban water management. To use current water simulation models, special expertise is required. Therefore, model information is prepared prior to work sessions, in which decision-makers weigh different solutions. However, this model information quickly becomes outdated when new suggestions for solutions arise and are therefore limited in use. We suggest that new model techniques, i.e. fast and flexible computation algorithms and realistic visualizations, allow this problem to be solved by using simulation models during work sessions. A new Interactive Water Simulation Model was applied for two case study areas in Amsterdam and was used in two workshops. In these workshops, the Interactive Water Simulation Model was positively received. It included non-specialist participants in the process of suggesting and selecting possible solutions and made them part of the accompanying discussions and negotiations. It also provided the opportunity to evaluate and enhance possible solutions more often within the time horizon of a decision-making process. Several preconditions proved to be important for successfully applying the Interactive Water Simulation Model, such as the willingness of the stakeholders to participate and the preparation of different general main solutions that can be used for further iterations during a work session.

QUANTIFYING UNCERTAINTY IN SIMULATION MODELING

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Jace Thibault ◽  
Simaan AbouRizk
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Simulation Modeling ◽  
Simulation Models ◽  
Light Rail ◽  
Rail Transit ◽  
Engineering Systems ◽  
Quantifying Uncertainty ◽  
Monte Carlo Simulation Model

Uncertainty can be defined as a state of either incomplete or otherwise bounded knowledge. Simulation models, and the engineering systems that they represent, often contain various types of uncertainty. Different approaches and theories can be applied to model these various types of uncertainty with a range of degrees in difficulty and accuracy. The objective of this paper is to explain the various types of uncertainty found in simulation models and to examine where uncertainty can be better represented or potentially reduced. To achieve this objective, a Monte Carlo Simulation model called the As-Planned Model is developed to estimate both cost and schedule using a risk-based approach for a simplified, Light Rail Transit construction project. After the project is complete, the As-Planned model is then compared to the project’s actual results. The resulting conclusions about various types of uncertainty are derived through both output comparison as well as uncertainty analysis.

scholarly journals Simulation modeling in assessing the agricultural enterprise state in an emergency

2021 ◽  
Vol 285 ◽  
pp. 01010
Author(s):  
Kirill Zhichkin ◽  
Vladimir Nosov ◽  
Lyudmila Zhichkina ◽  
Natalia Fomenko
Keyword(s):  
Initial Data ◽  
Simulation Model ◽  
Cash Flow ◽  
Simulation Modeling ◽  
Statistical Evaluation ◽  
Simulation Models ◽  
Financial Resources ◽  
Financial Flows ◽  
Software Complex ◽  
Agricultural Enterprise

The article proposes a methodology for assessing the sufficiency of financial resources in an emergency. The purpose of the study is to develop a methodology based on the method of simulation modeling to assess the sufficiency of resources and the sustainability of an agricultural enterprise in the event of an emergency. This set of methods for assessing the availability of enterprise financial resources for overcoming emergencies was implemented using algorithms for simulation of enterprise financial flows and their assessment in the program for investment calculations Project Expert 7.19. The program allows you to build simulation models of an enterprise, regardless of their industry and specificity. With the help of this software complex, it is possible not only to build a simulation model of an enterprise, but also to carry out its statistical evaluation. Together with the proposed method of detailing the initial data of annual financial and economic documents, this set of methods is a powerful tool for building and evaluating simulation models of agricultural and other enterprises, taking into account fluctuations in cash flow values during the year. Thus, the accuracy of the estimates obtained is significantly increased in comparison with methods based on the analysis of relative indicators or coefficients.

scholarly journals Some Issues in the Testing of Computer Simulation Models

2016 ◽  
Vol 5 (1) ◽  
pp. 1-10
Author(s):  
David Murray-Smith
Keyword(s):  
Simulation Model ◽  
Model Validation ◽  
Simulation Models ◽  
Model Testing ◽  
Model Verification ◽  
Verification And Validation ◽  
Quality Data ◽  
Model Quality ◽  
Face Validation

The testing of simulation models has much in common with testing processes in other types of application involving software development. However, there are also important differences associated with the fact that simulation model testing involves two distinct aspects, which are known as verification and validation. Model validation is concerned with investigation of modelling errors and model limitations while verification involves checking that the simulation program is an accurate representation of the mathematical and logical structure of the underlying model. Success in model validation depends upon the availability of detailed information about all aspects of the system being modelled. It also may depend on the availability of high quality data from the system which can be used to compare its behaviour with that of the corresponding simulation model. Transparency, high standards of documentation and good management of simulation models and data sets are basic requirements in simulation model testing. Unlike most other areas of software testing, model validation often has subjective elements, with potentially important contributions from face- validation procedures in which experts give a subjective assessment of the fidelity of the model. Verification and validation processes are not simply applied once but must be used repeatedly throughout the model development process, with regressive testing principles being applied. Decisions about when a model is acceptable for the intended application inevitably involve some form of risk assessment. A case study concerned with the development and application of a simulation model of a hydro-turbine and electrical generator system is used to illustrate some of the issues arising in a typical control engineering application. Results from the case study suggest that it is important to bring together objective aspects of simulation model testing and the more subjective face- validation aspects in a coherent fashion. Suggestions are also made about the need for changes in approach in the teaching of simulation techniques to engineering students to give more emphasis to issues of model quality, testing and validation.

Verification and Validation of Pipeline Terminal Simulation Models

2014 ◽  
Author(s):  
Frank Lin ◽  
Allan Chegus ◽  
Dumitru Cernelev
Keyword(s):  
Simulation Model ◽  
Simulation Models ◽  
Difficult Problem ◽  
Verification And Validation ◽  
Optimization Software ◽  
Simulation And Optimization ◽  
Terminal Performance ◽  
Model Validity ◽  
Complex Simulation

The problem of validating a complex simulation model represents pipeline terminal performance with verifiable accuracy is a difficult problem requiring extensive testing and calibration. This paper discusses a case study of the verification and validation of a terminal simulation model. The approach to deciding model validity is presented as well as the process of verifying and validating the model including methodology and thresholds for acceptance. Ultimately the paper demonstrates the ability of commercial simulation and optimization software to work collaboratively to determine an optimal business solution.

A pattern-based validation method for the credibility evaluation of simulation models

SIMULATION ◽  
2019 ◽  
Vol 96 (2) ◽  
pp. 151-167
Author(s):  
Yuanjun Laili ◽  
Lin Zhang ◽  
Yongliang Luo
Keyword(s):  
Simulation Model ◽  
Dynamic Models ◽  
Reference Data ◽  
Model Performance ◽  
Simulation Models ◽  
Validation Method ◽  
Evaluation Procedures ◽  
Serial Data ◽  
Multi Agent

Measuring the credibility of a simulation model has always been challenging due to its growing uncertainty and complexity. During the past decades, plenty of metrics and evaluation procedures have been developed for evaluating different sorts of simulation models. Most of the existing research focuses on the direct comparison of numerical results with a group of reference data. However, it is sometimes unsuitable for evolving dynamic models such as the multi-agent models. With the same condition, both the practical system and the simulation model perform highly dynamic actions. The credibility of the model with insufficient information, non-stationary states and changing environment is unable to acquire through a direct pair comparison. This paper presents a pattern-based validation method to complementarily extract hidden patterns that exist in both a simulation model and its reference data, and assess the model performance in a different aspect. Firstly, multi-dimensional perceptually important points strategy is modified to find the patterns exist in time-serial data. Afterward, a pattern organizing topology is applied to automatically depict required pattern from reference data and assess the performance of the corresponding simulation model. The extensive case study on three simulation models shows the effectiveness of the proposed method.

scholarly journals Train Vehicle Structure Design from the Perspective of Evacuation

2019 ◽  
Vol 32 (1) ◽  
Author(s):  
Hanzhao Qiu ◽  
Weining Fang
Keyword(s):  
Simulation Model ◽  
Dynamic Process ◽  
Simulation Models ◽  
Structure Design ◽  
Small Sample ◽  
Time Warping ◽  
Evacuation Simulation ◽  
Vehicle Structure ◽  
Dynamic Time

Abstract The safety of trains, a highly efficient mode of transportation, has attracted significant attention. In the vehicle structure design of a train, the evaluation of the passenger evacuation time is necessary. The establishment of a simulation model is the fastest, most convenient, and practical way to achieve this goal. However, few scholars have focused on the reliability of a passenger train evacuation simulation model. This paper proposes a new validation method based on dynamic time warping and multidimensional scaling. The proposed method validates the dynamic process of a simulation model, provides statistical results, and can be used for small-sample scenarios such as a train evacuation scenario. The results of a case study indicate that the proposed method is an effective and quantitative approach to the validation of simulation models in a dynamic process. Thus, this paper describes the influence of the train structure size on an evacuation based on the results of simulation experiments. The structural size factors include the door width, aisle width, and seat pitch. The experiment results indicate that a wide aisle and reasonable seat pitch can promote a proper evacuation. In addition, a normal train door width has no effect on an evacuation.

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