Pub Date : 2022-12-26DOI: 10.1177/00375497221143988
Yilin Huang, Xu Xie, Yubin Cho, A. Verbraeck
Data assimilation (DA) is a methodology widely used by different disciplines of science and engineering. It is typically applied to continuous systems with numerical models. The application of DA to discrete-event and discrete-time systems including agent-based models is relatively new. Because of its non-linearity and non-Gaussianity, the particle filter (PF) method is often a good option for stochastic simulation models of discrete systems. The probability distributions of model runs, however, make it computationally intensive. The experimental conditions therein are understudied. This paper studied three critical conditions of PF-based DA in a discrete event model: (1) the time interval between two consecutive DA iterations, (2) the number of particles, and (3) the actual level and perceived level of measurement errors (or noises). The study conducted identical-twin experiments of an M/M/1 single server queuing system. The ground truth is imitated in a stand-alone simulation model. The measurement errors are superimposed so that the effect of the three conditions can be quantitatively evaluated in a controlled manner. The results show that the estimation accuracy of such a system using PF is more constrained by the choice of time intervals than the number of particles. An under estimation of measurement errors produces worse state estimates than an over estimation of errors. A correct perception of the measurement errors does not guarantee better state estimates. Moreover, a slight over estimation of errors results in better state estimates, and it is more responsive to abrupt system changes than an accurate perception of measurement errors.
{"title":"Particle filter–based data assimilation in dynamic data-driven simulation: sensitivity analysis of three critical experimental conditions","authors":"Yilin Huang, Xu Xie, Yubin Cho, A. Verbraeck","doi":"10.1177/00375497221143988","DOIUrl":"https://doi.org/10.1177/00375497221143988","url":null,"abstract":"Data assimilation (DA) is a methodology widely used by different disciplines of science and engineering. It is typically applied to continuous systems with numerical models. The application of DA to discrete-event and discrete-time systems including agent-based models is relatively new. Because of its non-linearity and non-Gaussianity, the particle filter (PF) method is often a good option for stochastic simulation models of discrete systems. The probability distributions of model runs, however, make it computationally intensive. The experimental conditions therein are understudied. This paper studied three critical conditions of PF-based DA in a discrete event model: (1) the time interval between two consecutive DA iterations, (2) the number of particles, and (3) the actual level and perceived level of measurement errors (or noises). The study conducted identical-twin experiments of an M/M/1 single server queuing system. The ground truth is imitated in a stand-alone simulation model. The measurement errors are superimposed so that the effect of the three conditions can be quantitatively evaluated in a controlled manner. The results show that the estimation accuracy of such a system using PF is more constrained by the choice of time intervals than the number of particles. An under estimation of measurement errors produces worse state estimates than an over estimation of errors. A correct perception of the measurement errors does not guarantee better state estimates. Moreover, a slight over estimation of errors results in better state estimates, and it is more responsive to abrupt system changes than an accurate perception of measurement errors.","PeriodicalId":49516,"journal":{"name":"Simulation-Transactions of the Society for Modeling and Simulation International","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74823464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-22DOI: 10.1177/00375497221139282
Aby M Philip, S. Prasannavenkatesan, N. Mustafee
The increase in demand for outpatient departments (OPDs) has contributed to overcrowded clinics and patient dissatisfaction. Computer simulation can help decision-makers meet the operational challenge of balancing the demand for outpatient services with considerations of available capacity. The paper presents a synthesis of the literature on simulation modeling in OPD using two approaches: a bibliometric analysis (employing keyword co-occurrence network) and a literature classification focusing on OPD strategy, OPD performance measures, and simulation techniques. Our review is based on 161 papers, published between 2006 and 2020, identified through a methodological search of the literature. The objective of the review is threefold: (1) to identify the major and emerging research issues in general and specialized OPD, (2) to find the commonly used performance measures in OPD and how it is associated with the strategies used to improve the performance, and (3) to identify the commonly used simulation methods for OPD modeling. A key finding from the bibliometric analysis is that most OPD research can be classified under one of the four clusters—“organization and management,”“patient satisfaction,”“overbooking,” and “performance.” We also find that patient waiting time has received much attention among the performance measures reported in the literature, followed by server idle time/overtime (server here is the OPD consultant or other healthcare resource). Our review serves as a key reference point for scholars, practitioners, students, and healthcare stakeholders, and those who use quantitative tools to aid operational decision-making.
{"title":"Simulation modelling of hospital outpatient department: a review of the literature and bibliometric analysis","authors":"Aby M Philip, S. Prasannavenkatesan, N. Mustafee","doi":"10.1177/00375497221139282","DOIUrl":"https://doi.org/10.1177/00375497221139282","url":null,"abstract":"The increase in demand for outpatient departments (OPDs) has contributed to overcrowded clinics and patient dissatisfaction. Computer simulation can help decision-makers meet the operational challenge of balancing the demand for outpatient services with considerations of available capacity. The paper presents a synthesis of the literature on simulation modeling in OPD using two approaches: a bibliometric analysis (employing keyword co-occurrence network) and a literature classification focusing on OPD strategy, OPD performance measures, and simulation techniques. Our review is based on 161 papers, published between 2006 and 2020, identified through a methodological search of the literature. The objective of the review is threefold: (1) to identify the major and emerging research issues in general and specialized OPD, (2) to find the commonly used performance measures in OPD and how it is associated with the strategies used to improve the performance, and (3) to identify the commonly used simulation methods for OPD modeling. A key finding from the bibliometric analysis is that most OPD research can be classified under one of the four clusters—“organization and management,”“patient satisfaction,”“overbooking,” and “performance.” We also find that patient waiting time has received much attention among the performance measures reported in the literature, followed by server idle time/overtime (server here is the OPD consultant or other healthcare resource). Our review serves as a key reference point for scholars, practitioners, students, and healthcare stakeholders, and those who use quantitative tools to aid operational decision-making.","PeriodicalId":49516,"journal":{"name":"Simulation-Transactions of the Society for Modeling and Simulation International","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79430263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-13DOI: 10.1177/00375497221138923
F. Cordoni, Caterina Giannetti, F. Lillo, G. Bottazzi
A crucial aspect of every experiment is the formulation of hypotheses prior to data collection. In this paper, we use a simulation-based approach to generate synthetic data and formulate the hypotheses for our market experiment and calibrate its laboratory design. In this experiment, we extend well-established laboratory market models to the two-asset case, accounting at the same time for heterogeneous artificial traders with multi-asset strategies. Our main objective is to identify the role played in the price-bubble formation by both self-impact (i.e., how trading orders affect the price dynamics) and cross-impact (i.e., the price changes in one asset caused by the trading activity on other assets). To this end, we vary across treatments the possibility of traders of diverting their capital from one asset to the other, thereby artificially changing the amount of liquidity in the market. To simulate different scenarios for the synthetic data generation, we vary along with the liquidity the type of trading strategies of our artificial traders. Our results suggest that an increase in liquidity increases the cross-impact, especially when agents are market-neutral. Self-impact, however, remains significant and constant for all model specifications.
{"title":"Simulation-driven experimental hypotheses and design: a study of price impact and bubbles","authors":"F. Cordoni, Caterina Giannetti, F. Lillo, G. Bottazzi","doi":"10.1177/00375497221138923","DOIUrl":"https://doi.org/10.1177/00375497221138923","url":null,"abstract":"A crucial aspect of every experiment is the formulation of hypotheses prior to data collection. In this paper, we use a simulation-based approach to generate synthetic data and formulate the hypotheses for our market experiment and calibrate its laboratory design. In this experiment, we extend well-established laboratory market models to the two-asset case, accounting at the same time for heterogeneous artificial traders with multi-asset strategies. Our main objective is to identify the role played in the price-bubble formation by both self-impact (i.e., how trading orders affect the price dynamics) and cross-impact (i.e., the price changes in one asset caused by the trading activity on other assets). To this end, we vary across treatments the possibility of traders of diverting their capital from one asset to the other, thereby artificially changing the amount of liquidity in the market. To simulate different scenarios for the synthetic data generation, we vary along with the liquidity the type of trading strategies of our artificial traders. Our results suggest that an increase in liquidity increases the cross-impact, especially when agents are market-neutral. Self-impact, however, remains significant and constant for all model specifications.","PeriodicalId":49516,"journal":{"name":"Simulation-Transactions of the Society for Modeling and Simulation International","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76361579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-10DOI: 10.1177/00375497221140918
Fatemeh Jahedinia, M. Bagheri, A. Naderan, Zahra Bahramian
Passengers’ safety against unexpected incidents such as rail stations’ fire accidents is essential in the safety field. The presence of luggage with passengers occupies extra space, diminishes passenger’s velocity in high densities, and consequently increases the evacuation time. Therefore, studying the mixture of luggage-laden passengers with non-luggage-laden passengers during the emergency evacuation time of a rail station is vital. In this paper, a simulation of a metro-rail transfer station using an extended cellular automata (CA) model is used to illustrate the importance of this consideration. In this model, luggage-laden passengers and non-luggage-laden passengers are defined as two-cell and one-cell groups, respectively. Specific parameters for luggage-laden passengers in minimum wall prevention and velocity are used. Also, the volume of each passenger group is extracted from the Wi-Fi scanners during the busiest time of the normal station operational hours due to metro and railway schedules. The simulation is carried out using the Python programming language. Fourteen scenarios that vary in their impact on the three classifications of station infrastructure, station equipment, and management’s approach are presented. The analysis indicates that approximately 28% of passengers, or 236 passengers, will not be evacuated in the time period predicted by the simulation if the luggage is not considered. Interestingly, resizing retail stores in the corridor reduced emergency evacuation times by 6.3%, the equivalent of removing them. Failures in the two escalators affect an 8% and 9.4% increase in emergency evacuation time and cause 28 and 46 more passengers to be trapped, respectively. Although the construction of the second railway entrance corridor has been suspended, results indicate that it will save 67 passengers and reduce evacuation time by 9.5%.
{"title":"Simulation of luggage-laden passengers’ behavior in the evacuation process based on a floor field CA model case study: Tehran metro-rail transfer corridor","authors":"Fatemeh Jahedinia, M. Bagheri, A. Naderan, Zahra Bahramian","doi":"10.1177/00375497221140918","DOIUrl":"https://doi.org/10.1177/00375497221140918","url":null,"abstract":"Passengers’ safety against unexpected incidents such as rail stations’ fire accidents is essential in the safety field. The presence of luggage with passengers occupies extra space, diminishes passenger’s velocity in high densities, and consequently increases the evacuation time. Therefore, studying the mixture of luggage-laden passengers with non-luggage-laden passengers during the emergency evacuation time of a rail station is vital. In this paper, a simulation of a metro-rail transfer station using an extended cellular automata (CA) model is used to illustrate the importance of this consideration. In this model, luggage-laden passengers and non-luggage-laden passengers are defined as two-cell and one-cell groups, respectively. Specific parameters for luggage-laden passengers in minimum wall prevention and velocity are used. Also, the volume of each passenger group is extracted from the Wi-Fi scanners during the busiest time of the normal station operational hours due to metro and railway schedules. The simulation is carried out using the Python programming language. Fourteen scenarios that vary in their impact on the three classifications of station infrastructure, station equipment, and management’s approach are presented. The analysis indicates that approximately 28% of passengers, or 236 passengers, will not be evacuated in the time period predicted by the simulation if the luggage is not considered. Interestingly, resizing retail stores in the corridor reduced emergency evacuation times by 6.3%, the equivalent of removing them. Failures in the two escalators affect an 8% and 9.4% increase in emergency evacuation time and cause 28 and 46 more passengers to be trapped, respectively. Although the construction of the second railway entrance corridor has been suspended, results indicate that it will save 67 passengers and reduce evacuation time by 9.5%.","PeriodicalId":49516,"journal":{"name":"Simulation-Transactions of the Society for Modeling and Simulation International","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80901389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.1177/00375497221139870
Lina Aboueljinane, E. Sahin, Z. Jemai
Emergency Medical Service (EMS) managers continuously strive to improve the coverage performance, i.e., the percentage of calls responded to within a specific target time, to save lives in case of life-threatening emergencies. This goal can be achieved by dynamically adjusting the location of rescue teams during a day in response to some temporal or geographical fluctuations such as demand patterns, traffic conditions, or the number of teams on duty. This relocation is known as the multi-period redeployment problem. In this study, we propose a discrete simulation-based optimization model to adress the multi-period redeployment problem in the French EMS of the Val-de-Marne department (France), named SAMU 94. The proposed model uses an iterative method that combines the use of a mathematical model to find the optimal locations of rescue teams with the use of the SAMU 94 simulation model implemented in Arena software, to evaluate the busy fraction parameters required to solve the mathematical model. The model performance was compared with that of the simulation-based optimization software, OptQuest. The experimental results demonstrated that the iterative method could produce solutions with better coverage performance, 20 times faster than OptQuest.
{"title":"A discrete simulation-based optimization approach for multi-period redeployment in emergency medical services","authors":"Lina Aboueljinane, E. Sahin, Z. Jemai","doi":"10.1177/00375497221139870","DOIUrl":"https://doi.org/10.1177/00375497221139870","url":null,"abstract":"Emergency Medical Service (EMS) managers continuously strive to improve the coverage performance, i.e., the percentage of calls responded to within a specific target time, to save lives in case of life-threatening emergencies. This goal can be achieved by dynamically adjusting the location of rescue teams during a day in response to some temporal or geographical fluctuations such as demand patterns, traffic conditions, or the number of teams on duty. This relocation is known as the multi-period redeployment problem. In this study, we propose a discrete simulation-based optimization model to adress the multi-period redeployment problem in the French EMS of the Val-de-Marne department (France), named SAMU 94. The proposed model uses an iterative method that combines the use of a mathematical model to find the optimal locations of rescue teams with the use of the SAMU 94 simulation model implemented in Arena software, to evaluate the busy fraction parameters required to solve the mathematical model. The model performance was compared with that of the simulation-based optimization software, OptQuest. The experimental results demonstrated that the iterative method could produce solutions with better coverage performance, 20 times faster than OptQuest.","PeriodicalId":49516,"journal":{"name":"Simulation-Transactions of the Society for Modeling and Simulation International","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78987757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1177/00375497221105527
Manman Li, Jian Lu, Jiahui Sun
This study models and analyzes the dynamic interaction between route choice and prescriptive information on a degradable network. The predictively prescriptive information is first explicitly modeled based on travelers’ behavior and historical data. Its accuracy is then dynamically measured to adjust the information compliance rate based on travelers’ expectations and experiences. Finally, a logit function is adopted to describe travelers’ route choice. Based on the proposed model, the dynamic interaction between route choice and prescriptive information on a degradable network is investigated by theoretical analyses and numerical experiments, which provides insights for the design and operation of advanced traveler information systems and traffic management.
{"title":"Interactive route choice and prescriptive information on degradable network","authors":"Manman Li, Jian Lu, Jiahui Sun","doi":"10.1177/00375497221105527","DOIUrl":"https://doi.org/10.1177/00375497221105527","url":null,"abstract":"This study models and analyzes the dynamic interaction between route choice and prescriptive information on a degradable network. The predictively prescriptive information is first explicitly modeled based on travelers’ behavior and historical data. Its accuracy is then dynamically measured to adjust the information compliance rate based on travelers’ expectations and experiences. Finally, a logit function is adopted to describe travelers’ route choice. Based on the proposed model, the dynamic interaction between route choice and prescriptive information on a degradable network is investigated by theoretical analyses and numerical experiments, which provides insights for the design and operation of advanced traveler information systems and traffic management.","PeriodicalId":49516,"journal":{"name":"Simulation-Transactions of the Society for Modeling and Simulation International","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85211016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1177/00375497221105290
Lei Si, Feng Xing, Zhongbin Wang, Chao Tan
The automatic control of top coal caving is of great significance to realize intelligent coal mining. In the process of top coal caving, a coal-gangue mixed area containing coal, gangue, and air is formed at the tail beam of the hydraulic support, which has different electromagnetic parameters, different volumes, and different shapes. To explore the transmission characteristics of electromagnetic wave in coal-gangue mixed model and the influence of different gangue ratios on electromagnetic wave propagation, the coal-gangue mixed model is established based on the random medium theory. Some electromagnetic forward modeling is carried out with different coal-gangue granularities, electromagnetic parameters, and gangue ratios based on finite-difference time-domain (FDTD) and finite-integration time-domain (FITD) methods. The results show that different granularities of coal and gangue will affect the amplitude of electromagnetic wave time-domain waveform. Under the same particle size, the equivalent electromagnetic parameters in the coal-gangue mixed medium will be larger with higher gangue ratio. Furthermore, the difference of transmitted wave signals between different gangue ratios will be larger with higher electromagnetic parameters difference of the coal and gangue. For higher refractive index, the propagation velocity of electromagnetic wave in the medium and the transmitted wave amplitude will be smaller. In addition, the comparison results illustrate that the rules of electromagnetic wave propagation obtained by FDTD and FITD methods are basically the same, which verifies the correctness of the simulations in this paper. The simulation results can lay a theoretical foundation for identifying the coal-gangue mixed degree in the process of top coal caving.
{"title":"Electromagnetic wave forward modeling of coal-gangue mixed model in top coal caving mining face","authors":"Lei Si, Feng Xing, Zhongbin Wang, Chao Tan","doi":"10.1177/00375497221105290","DOIUrl":"https://doi.org/10.1177/00375497221105290","url":null,"abstract":"The automatic control of top coal caving is of great significance to realize intelligent coal mining. In the process of top coal caving, a coal-gangue mixed area containing coal, gangue, and air is formed at the tail beam of the hydraulic support, which has different electromagnetic parameters, different volumes, and different shapes. To explore the transmission characteristics of electromagnetic wave in coal-gangue mixed model and the influence of different gangue ratios on electromagnetic wave propagation, the coal-gangue mixed model is established based on the random medium theory. Some electromagnetic forward modeling is carried out with different coal-gangue granularities, electromagnetic parameters, and gangue ratios based on finite-difference time-domain (FDTD) and finite-integration time-domain (FITD) methods. The results show that different granularities of coal and gangue will affect the amplitude of electromagnetic wave time-domain waveform. Under the same particle size, the equivalent electromagnetic parameters in the coal-gangue mixed medium will be larger with higher gangue ratio. Furthermore, the difference of transmitted wave signals between different gangue ratios will be larger with higher electromagnetic parameters difference of the coal and gangue. For higher refractive index, the propagation velocity of electromagnetic wave in the medium and the transmitted wave amplitude will be smaller. In addition, the comparison results illustrate that the rules of electromagnetic wave propagation obtained by FDTD and FITD methods are basically the same, which verifies the correctness of the simulations in this paper. The simulation results can lay a theoretical foundation for identifying the coal-gangue mixed degree in the process of top coal caving.","PeriodicalId":49516,"journal":{"name":"Simulation-Transactions of the Society for Modeling and Simulation International","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78657405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1177/00375497221138943
Yamika Patel, V. Rastogi, W. Borutzky
Dynamic interaction between train wheel and high-speed slab is an important issue when evaluating the contact force at the wheel–rail interface under the influence of an irregularity either on the train wheel or on the rail. In this paper, an influence of amplitude of irregularity along with the vehicle speed on the dynamics of wheel–rail interaction for high-speed railway tracks is being analyzed. For this purpose, single-wheel high-speed railway (HSR) track interaction models are developed using the bond graph modeling technique. The HSR track model consists of two layers of beam, i.e., rail and concrete slab. Both the rail and slab track is modeled using the Euler–Bernoulli beam theory. The Hertzian contact theory at the wheel–rail interface has been considered for this analysis. The vertical dynamic interaction between a train wheel and a high-speed slab track is compiled in the time domain using a bond graph approach coupled with a technique known as modal superposition. Irregularity present on the wheel is characterized as smooth, moderate, and severe depending upon the variation of irregularity amplitude. An expeditious increase of maximum contact force has been observed between the speed range of 200 and 250 km/h. Beyond the speed of 250 km/h, there is a gradual increment of contact force up to its peak value. When the train speed is beyond 288 km/h, there is a gradual decrease in maximum contact force. This kind of several other useful dynamic responses in terms of wheel acceleration and wheel–rail overlap are also evaluated.
{"title":"Simulation study on the influence of wheel irregularity on the vertical dynamics of wheel–rail interaction for high-speed railway track using bond graph","authors":"Yamika Patel, V. Rastogi, W. Borutzky","doi":"10.1177/00375497221138943","DOIUrl":"https://doi.org/10.1177/00375497221138943","url":null,"abstract":"Dynamic interaction between train wheel and high-speed slab is an important issue when evaluating the contact force at the wheel–rail interface under the influence of an irregularity either on the train wheel or on the rail. In this paper, an influence of amplitude of irregularity along with the vehicle speed on the dynamics of wheel–rail interaction for high-speed railway tracks is being analyzed. For this purpose, single-wheel high-speed railway (HSR) track interaction models are developed using the bond graph modeling technique. The HSR track model consists of two layers of beam, i.e., rail and concrete slab. Both the rail and slab track is modeled using the Euler–Bernoulli beam theory. The Hertzian contact theory at the wheel–rail interface has been considered for this analysis. The vertical dynamic interaction between a train wheel and a high-speed slab track is compiled in the time domain using a bond graph approach coupled with a technique known as modal superposition. Irregularity present on the wheel is characterized as smooth, moderate, and severe depending upon the variation of irregularity amplitude. An expeditious increase of maximum contact force has been observed between the speed range of 200 and 250 km/h. Beyond the speed of 250 km/h, there is a gradual increment of contact force up to its peak value. When the train speed is beyond 288 km/h, there is a gradual decrease in maximum contact force. This kind of several other useful dynamic responses in terms of wheel acceleration and wheel–rail overlap are also evaluated.","PeriodicalId":49516,"journal":{"name":"Simulation-Transactions of the Society for Modeling and Simulation International","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90695804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.1177/00375497221132574
Mohammad Dehghanimohammadabadi, Mandana Rezaeiahari, Javad Seif
Appointment scheduling is one of the critical factors for improving patient satisfaction with healthcare services. A practical and robust appointment scheduling solution allows clinics to efficiently utilize medical devices, equipment, and other resources. This study introduces a Multi-Objective Patient Appointment Scheduling (MO-PASS) framework to enhance clinic operations and quality of care. The proposed framework integrates three modules: (1) Optimization (using MATLAB), (2) Data-Exchange (MS Excel), and (3) Simulation (Simio). To implement MO-PASS, the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is coded in MATLAB, and a Simio API is developed, which exchanges simulated scenarios with MOPSO via Excel. The efficiency of the proposed framework is evaluated in a breast cancer clinic with multiple physicians and patient types. Two objective functions are defined for evaluating the solutions of the AS problem: (1) minimizing the total service time and (2) maximizing the number of (admitted) patients with zero overtime. Finally, the performance of MO-PASS is tested against three heuristic approaches with respect to objective functions. The computational experiment results show that the proposed MO-PASS outperforms the existing heuristic benchmarks. Also, the framework is accompanied by all the necessary details to make it practical and easy to implement.
{"title":"Multi-Objective Patient Appointment Scheduling Framework (MO-PASS): a data-table input simulation–optimization approach","authors":"Mohammad Dehghanimohammadabadi, Mandana Rezaeiahari, Javad Seif","doi":"10.1177/00375497221132574","DOIUrl":"https://doi.org/10.1177/00375497221132574","url":null,"abstract":"Appointment scheduling is one of the critical factors for improving patient satisfaction with healthcare services. A practical and robust appointment scheduling solution allows clinics to efficiently utilize medical devices, equipment, and other resources. This study introduces a Multi-Objective Patient Appointment Scheduling (MO-PASS) framework to enhance clinic operations and quality of care. The proposed framework integrates three modules: (1) Optimization (using MATLAB), (2) Data-Exchange (MS Excel), and (3) Simulation (Simio). To implement MO-PASS, the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is coded in MATLAB, and a Simio API is developed, which exchanges simulated scenarios with MOPSO via Excel. The efficiency of the proposed framework is evaluated in a breast cancer clinic with multiple physicians and patient types. Two objective functions are defined for evaluating the solutions of the AS problem: (1) minimizing the total service time and (2) maximizing the number of (admitted) patients with zero overtime. Finally, the performance of MO-PASS is tested against three heuristic approaches with respect to objective functions. The computational experiment results show that the proposed MO-PASS outperforms the existing heuristic benchmarks. Also, the framework is accompanied by all the necessary details to make it practical and easy to implement.","PeriodicalId":49516,"journal":{"name":"Simulation-Transactions of the Society for Modeling and Simulation International","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84674956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-19DOI: 10.1177/00375497221133846
Sajad Shafiekhani, N. Gheibi, Azam Janati Esfahani
Blockade of programmed death-ligand-1 (PD-L1) as a new method of immunotherapy for cancers has shown limited efficacy in hepatocellular carcinoma (HCC). The combination of anti-PD-L1 and radiotherapy (RT) enhances the antitumor effect in HCC cancer. The efficacy and interactions of these treatments can be addressed by a mathematical model. We developed a mathematical model using a set of ordinary differential equations (ODEs). The variables include cancer cells, cytotoxic T lymphocytes (CTLs), programmed cell death-1 (PD-1), PD-L1, anti-PD-L1, and ionizing radiation. The model is parameterized with imprecise data set of murine HCC model and the effect of parametric uncertainty is assessed by the fuzzy theorem. The global sensitivity analysis (GSA) is performed to assess model robustness against perturbation in parameters and to identify the most influential parameters on the dynamics of cells and proteins. In silico predictions are consistent with experimental data. The model simulation shows that anti-PD-L1 and RT have a synergistic effect. In silico assessment of treatments’ efficacy in the fuzzy setting of parameters revealed that anti-PD-L1 therapy, RT, and combination treatment caused the uncertainty band of tumor cells to lead to lower populations. This model as a validated rigorous simulation framework can be used to deepen our understanding of tumor and immune cell interactions and helps clinicians to investigate the efficacy of different time schedules of anti-PD-L1, RT, and combination therapy. The fuzzy theorem in conjunction with the classical ODE model that is parameterized by imprecise data was used to predict reliable outcomes of treatment.
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