Implement Simulation Models Research Articles

Research of methods of modeling of mass service enterprise

Objective. The purpose of the study is to use artificial intelligence tools to optimize and model the automated organization of work at a mass service enterprise. Method. The research is based on the use of a Petri net as a modeling tool, as well as mathematical hardware that makes it possible to implement simulation models of subsystems and conduct additional research on GPSS artificial intelligence models. Result. To process the results of the modeling process, the Petri net modeling method was used and a network activity model graph was developed. The modeling process used with the Petri Net application allows such functions to be described more clearly. The principles of modeling systems based on the GPSS language and Petri nets are considered. Conclusion. It is advisable to use the language devices GPSS and SP, which provide information support for the activities of a mass service enterprise, modeling the structural organization and information flows.

Impact analysis of cooperative perception on the performance of automated driving in unsignalized roundabouts.

This paper reports the implementation and results of a simulation-based analysis of the impact of cloud/edge-enabled cooperative perception on the performance of automated driving in unsignalized roundabouts. This is achieved by comparing the performance of automated driving assisted by cooperative perception to that of a baseline system, where the automated vehicle relies only on its onboard sensing and perception for motion planning and control. The paper first provides the descriptions of the implemented simulation model, which integrates the SUMO road traffic generator and CARLA simulator. This includes descriptions of both the baseline and cooperative perception-assisted automated driving systems. We then define a set of relevant key performance indicators for traffic efficiency, safety, and ride comfort, as well as simulation scenarios to collect relevant data for our analysis. This is followed by the description of simulation scenarios, presentation of the results, and discussions of the insights learned from the results.

Use of a Software Application to Generate a Sequence for Simulation Model Creation

The use of simulation models is currently a necessity, considering the complexity of the problems solved in many areas of engineering and scientific work (including logistics). This fact places high demands on their creation, use, and possible modification. When implementing simulation models, we often encounter limitations (depending on the software used), for which an effective solution is the application of the additional programming method. However, the method’s implementation is often associated with problems, the solution to which is, for example, the use of third-party services. Ultimately, this is an effective but lengthy process. The paper presents a new approach to the method of additional programming, which is based on using an addressable software application as a tool for generating sequences according to defined input parameters. The research was carried out using the simulation software Tecnomatix Plant Simulation and the software tool Simtalk, which is part of it.

Adaptive traffic signal control using deep Q-learning: case study on optimal implementations

Deep reinforcement learning has found great successes in addressing many challenging control problems; however, real-world implementations are still scarce if not non-existent. This is primarily due to three main challenges pertaining to the implementation, the stability, the optimal settings, and a lack of knowledge on methods that can be applied to field settings. This research attempts to address these issues with an adaptive simulation-based control framework proposed specifically for the training and evaluation. The control framework has implemented simulation models to conduct an extensive sensitivity analysis on the effects of key design variables, including rewarding schemes, state spaces, and model training parameters. The feasibility of transfer learning as a training strategy is also studied on scenarios with different layouts and different driver behavior models. Complex scenarios are also evaluated and used as test cases, including multiphase ring-and-barrier control and multi-intersection control. The research has contributed a significant amount of evidence on several critical design and implementation-related questions such as input representation, data (technology) requirements, training methods, and model transferability.

Разработка имитационной дискретно-событийной модели транспортной инфраструктуры с использованием инструментов оптимизации

The paper describes a discrete-event simulation model of transport and logistics infrastructure on the example of a road intersection at Profsoyuznaya Street, 64 k.2 / Nametkina Street, 6. The model allows the studying of traffic processes at the intersection with a high degree of accuracy, drawing analytical conclusions and making correct management decisions to improve the traffic situation on the basis of these conclusions. The developed simulation model consists of road network elements, agent generation system, process diagram functional blocks, control elements and infographics, etc. A number of simple experiments were performed with the model, including optimization, as a result of which the most optimal duration of the traffic light phases was determined, which ensures the minimum average travel time of vehicles. In addition, the model includes ground crossings that allow the study of the behavior of pedestrian masses when crossing the carriageway. The proposed solutions to the organization of traffic and pedestrian flows can be used to implement simulation models for a wide range of issues of modeling transport and logistics infrastructure, including railway transport, for example, when developing simulation models of transport interchange nodes. Software that was used in the study: the simulation environment AnyLogic 8 and the object-oriented programming language Java. The conducted research and preparation of the publication were carried out as part of the implementation of a grant from Russian Railways for the development of scientific and pedagogical schools in the field of railway transport.

Privacy Preservation in Patient Information Exchange Systems Based on Blockchain: System Design Study

BackgroundWith the increasing sophistication of the medical industry, various advanced medical services such as medical artificial intelligence, telemedicine, and personalized health care services have emerged. The demand for medical data is also rapidly increasing today because advanced medical services use medical data such as user data and electronic medical records (EMRs) to provide services. As a result, health care institutions and medical practitioners are researching various mechanisms and tools to feed medical data into their systems seamlessly. However, medical data contain sensitive personal information of patients. Therefore, ensuring security while meeting the demand for medical data is a very important problem in the information age for which a solution is required.ObjectiveOur goal is to design a blockchain-based decentralized patient information exchange (PIE) system that can safely and efficiently share EMRs. The proposed system preserves patients’ privacy in the EMRs through a medical information exchange process that includes data encryption and access control.MethodsWe propose a blockchain-based EMR-sharing system that allows patients to manage their EMRs scattered across multiple hospitals and share them with other users. Our PIE system protects the patient’s EMR from security threats such as counterfeiting and privacy attacks during data sharing. In addition, it provides scalability by using distributed data-sharing methods to quickly share an EMR, regardless of its size or type. We implemented simulation models using Hyperledger Fabric, an open source blockchain framework.ResultsWe performed a simulation of the EMR-sharing process and compared it with previous works on blockchain-based medical systems to check the proposed system’s performance. During the simulation, we found that it takes an average of 0.01014 (SD 0.0028) seconds to download 1 MB of EMR in our proposed PIE system. Moreover, it has been confirmed that data can be freely shared with other users regardless of the size or format of the data to be transmitted through the distributed data-sharing technique using the InterPlanetary File System. We conducted a security analysis to check whether the proposed security mechanism can effectively protect users of the EMR-sharing system from security threats such as data forgery or unauthorized access, and we found that the distributed ledger structure and re-encryption–based data encryption method can effectively protect users’ EMRs from forgery and privacy leak threats and provide data integrity.ConclusionsBlockchain is a distributed ledger technology that provides data integrity to enable patient-centered health information exchange and access control. PIE systems integrate and manage fragmented patient EMRs through blockchain and protect users from security threats during the data exchange process among users. To increase safety and efficiency in the EMR-sharing process, we used access control using security levels, data encryption based on re-encryption, and a distributed data-sharing scheme.

Evaluation of simulation models in neurosurgical training according to face, content, and construct validity: a systematic review.

BackgroundNeurosurgical training has been traditionally based on an apprenticeship model. However, restrictions on clinical exposure reduce trainees’ operative experience. Simulation models may allow for a more efficient, feasible, and time-effective acquisition of skills. Our objectives were to use face, content, and construct validity to review the use of simulation models in neurosurgical education.MethodsPubMed, Web of Science, and Scopus were queried for eligible studies. After excluding duplicates, 1204 studies were screened. Eighteen studies were included in the final review.ResultsNeurosurgical skills assessed included aneurysm clipping (n = 6), craniotomy and burr hole drilling (n = 2), tumour resection (n = 4), and vessel suturing (n = 3). All studies assessed face validity, 11 assessed content, and 6 assessed construct validity. Animal models (n = 5), synthetic models (n = 7), and VR models (n = 6) were assessed. In face validation, all studies rated visual realism favourably, but haptic realism was key limitation. The synthetic models ranked a high median tactile realism (4 out of 5) compared to other models. Assessment of content validity showed positive findings for anatomical and procedural education, but the models provided more benefit to the novice than the experienced group. The cadaver models were perceived to be the most anatomically realistic by study participants. Construct validity showed a statistically significant proficiency increase among the junior group compared to the senior group across all modalities.ConclusionOur review highlights evidence on the feasibility of implementing simulation models in neurosurgical training. Studies should include predictive validity to assess future skill on an individual on whom the same procedure will be administered. This study shows that future neurosurgical training systems call for surgical simulation and objectively validated models.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00701-021-05003-x.

A Buckling Instability Prediction Model for the Reliable Design of Sheet Metal Panels Based on an Artificial Intelligent Self-Learning Algorithm

Sheets’ buckling instability, also known as oil canning, is an issue that characterizes the resistance to denting in thin metal panels. The oil canning phenomenon is characterized by a depression in the metal sheet, caused by a local buckling, which is a critical design issue for aesthetic parts, such as automotive outer panels. Predicting the buckling instability during the design stage is not straightforward since the shape of the component might change several times before the part is sent to production and can actually be tested. To overcome this issue, this research presents a robust prediction model based on the convolutional neural network (CNN) to estimate the buckling instability of automotive sheet metal panels, based on the major, minor, and Gaussian surface curvatures. The training dataset for the CNN model was generated by implementing finite element analysis (FEA) of the outer panels of various commercial vehicles, for a total of twenty panels, and by considering different indentation locations on each panel. From the implemented simulation models the load-stroke curves were exported and utilized to determine the presence, or absence, of buckling instability and to determine its magnitude. Moreover, from the computer aided design (CAD) files of the relevant panels, the three considered curvatures on the tested indentation points were acquired as well. All the positions considered in the FEA analyses were backed up by industrial experiments on the relevant panels in their assembled position, allowing to validate their reliability. The combined correlation of curvatures and load-displacement curves allowed correlating the geometrical features that create the conditions for buckling instability to arise and was utilized to train the CNN algorithm, defined considering 13 convolution layers and 5 pooling layers. The trained CNN model was applied to another automotive frame, not used in the training process, and the prediction results were compared with experimental indentation tests. The overall accuracy of the CNN model was calculated to be 90.1%, representing the reliability of the proposed algorithm of predicting the severity of the buckling instability for automotive sheet metal panels.

An order-splitting model for supplier selection and order allocation in a multi-echelon supply chain

This paper considers an integrated supplier selection and inventory control problem for a multi-echelon inventory system with an order-splitting policy. A buyer firm consisting of one warehouse and N identical retailers procures a type of product from a group of potential suppliers; the acquisition of the warehouse takes place when the inventory level depletes to a reorder point R, and the order Q is simultaneously split among m selected suppliers. We develop an exact analytical model for the order-splitting problem in a multi-echelon system, and formulate the supplier selection problem in a Mixed Integer Nonlinear Programming (MINLP) model. This model determines the optimal inventory policy that coordinates stock levels between each echelon of the systems while properly allocating orders among selected suppliers to maximize the expected profit. For verification and validation of the proposed mathematical model, we conduct several numerical analyses and implement simulation models which helps us demonstrate the model’s solvability and effectiveness.

THE CONCEPT OF THE SIMULATION MODEL OF THE DECISION SUPPORT SYSTEM OF THE COAL ENTERPRISE

The article considers the General approach and elements of implementation of the simulation model of the decision support system as one of the most effective ways to improve the management system of a mining enterprise. The paper gives the description of the technological process and the main functional blocks of the control system. The author identifies the key links and requirements for the implementation of the simulation model of production process and enterprise management. The subsystems of the model allow us to consider the dynamics of the production enterprise in all the variety of its activities, for different variants of the technology and organization of production of the existing control system, the planned system and promising management solutions. In addition, the article presents the structure and description of the implemented simulation model of the main production process of the experimental coal mine. The paper indicates the role of the user-manager in the decision-making process based on the results of modeling. The author gives a control example of the simulation results.

Modeling the electrical power and energy consumption of automated guided vehicles to improve the energy efficiency of production systems

Due to the advancing energy system transformation and the increasingly complex and dynamic environment in which factories have to operate, the energy efficiency and flexible design of production systems are becoming more important. Since the use of automated guided vehicles is a promising approach to enhance the flexibility of intralogistics, their electrical power requirements are analyzed. Based on the measurement data obtained, different movement modules of an automated guided vehicle are identified and then modeled using physical laws. These movement modules are the translatory movement, rotary movement, and the lifting and lowering of the load-carrying platform. The measurement data show, for example, that the energy requirement for translational and rotational movement increases in relation to the payload weight. At the same time, however, it is also shown that an increase in speed of the automated guided vehicle leads to a lower energy requirement, although the power requirement grows. Consequently, one result is that an automated guided vehicle works most efficiently when the maximum payload is transported at the highest speed. With regard to the lifting and lowering of loads, the result is an increasing energy requirement depending on the payload weight. The comparison between the collected measurement data and the outcomes of the implemented simulation model shows only minor deviations. Thus, the implemented simulation model of automated guided vehicles can be used with regard to their electrical power consumption, for example, in production planning to comprehensively raise the energy efficiency of production systems.

When links build networks: brief history about the Antropocaos Group

The Antropocaos Group of the Universidad de Buenos Aires has been working on topics related to Social Network Analysis and other complexity algorithms for more than 20 years. This article reviews some of the team’s productions lines of work and the more frequent methodological choices. To this end, we outlined a brief history of our formation in the university cloisters, from the beginning of the 1990s to the present day. We emphasize the empirical topics addressed by the group, in order to highlight both the conceptual variability and the methodological unity regarding different areas of Social Network Analysis application. A section is dedicated specifically to those studies where we implemented simulation models and the possibility to observe reticular phenomena in a controlled computational environment. The next section shows different areas in which the members of Antropocaos currently carry out their activities and apply reticular methods, including academic research, management, and public administration, and different sorts of consultancies. Finally, we point toward some guidelines about the future of the group, as well as the lines of research that are just being started, related to algorithms of Artificial Intelligence, as well as our long-standing commitment to disseminate the Social Networks Analysis methodology.

An integrated and parametric simulation model to improve production and maintenance processes: Towards a digital factory performance

In recent years with the emergence of the concept of Industry 4.0 digitalization is transforming the industrial scenario. A great attention is focused on integrated simulation system, an emerging enabling technology, as a particular decision support tool, to represent the increasingly complex modern production systems. The advantage is that it is possible to perform simulations and reconfigure models to evaluate modified or alternative scenarios to achieve current demands of increasingly competitive markets. The aim of the present research is the definition of an integrated parametric simulation model for integration management enhancing the production and maintenance process. Thus, a framework for implementing simulation models that help production and maintenance managers to make cost-effective decisions as well as optimize the use of resources is developed. System Dynamics approach is used to simulate, through PowerSim Software®, the nonlinear behavior of the analyzed complex systems over time. The results revealed that the proposed simulation optimization procedure can be used to solve and to manage complexity in a ‘‘real-time’’. Furthermore, the output of this simulation optimization procedure gives the decision maker the most probable scenario.

MULTIPLE OBJECTIVE MANAGEMENT STRATEGIES FOR COASTAL AQUIFERS UTILIZING NEW SURROGATE MODELS

Coastal aquifers are hydraulically connected to the sea and therefore susceptible to saltwater intrusion problems. This study proposes the utilization of a new surrogate model within coupled simulation-optimization (S/O) model for the management of coastal aquifers subjected to density-dependent saltwater intrusion processes. The simulation of the transient 3-dimensional density-dependent flow and transport model is based on the solution of an implemented numerical simulation model. Direct coupling of the numerical simulation model into the multi-objective genetic algorithm (MOGA) is computationally expensive. Hence, the solution of the numerical simulation model with random input variables are used to train and test the support vector machine regression (SVMR) surrogate models for approximately simulating the flow and transport processes. The performances of the new surrogate models are evaluated using various performance evaluation criteria. The resulting validated SVMR surrogate models are coupled to the MOGA and implemented for an illustrative coastal aquifer with an aim to develop efficient coastal aquifer management strategies. Based on the objective functions, execution of S/O model presented a set of optimal groundwater withdrawal rates from the simulated aquifer. It also ensured salinity levels at the designated monitoring wells are constrained within specified limits. The efficiency of the new SVMR surrogate models is also demonstrated. Evaluation results suggested that the projected S/O model is an effective way of developing feasible and reliable coastal aquifer management strategies. It also enhances the possibility of solving more realistic large-scale problems and developing regional-scale coastal aquifer management methodologies.

Simulation Model of Bacterial Resistance to Antibiotics Using Individual-Based Modeling.

We designed and implemented simulation models of bacterial growth and antibiotic resistance to determine the appropriate antibiotics to use against antibiotic-resistant bacteria. Simulation models were designed using individual-based modeling, and a simulation tool, ARSim, was developed to conduct experiments using the models. Simulations of bacterial growth were conducted by virtually growing Klebsiella pneumoniae bacteria in a virtual environment with predefined parameters. Other experiments included predicting the effects of antibiotics when added to two different groups, one group of nonresistant bacteria and another group of both resistant and nonresistant bacteria. Carbapenem class antibiotics such as Imipenem were used for the simulation. The simulation results showed that the biological principles of bacteria and their antibiotic resistance mechanisms were correctly designed and implemented. Using the computational approaches developed in this study, we hope to provide researchers with a more effective method for finding new ways to fight antibiotic resistance.

Cognitive Behaviors Modeling Using UML Profile: Design and Experience

To achieve model reuse in combat effectiveness simulation systems development, cognitive decision behaviors are usually implemented using a scripting language, which is separate from the programming language used to implement simulation models. Therefore, it is desirable to establish a much better grounding for cognitive behaviors modeling. In the context of domain specific modeling, metamodeling from scratch for designing such a scripting language poses some limitations, among which is the issue of integrating various models that are represented by various customized languages with different syntax and semantics, together with a large expenditure of designing, implementing, and maintaining these languages and their supporting resources. Instead, UML profile-based metamodeling is adopted, as a lightweight extension to capture the cognitive domain specific concepts, relationships, and constraints. Moreover, a unifying framework is proposed to guide the cognitive domain specific profiles design. Upon this framework, the development process is shown through constructing an anti-submarine tactical profile in combat effectiveness simulation systems domain and the feasibility of the domain specific language is illustrated with an armed escort scenario.

Support vector machine and ROC curves for modeling of aircraft fuel consumption

To estimate the fuel consumption of a civil aircraft, we propose to use the receiver operating characteristic (ROC) curve to optimize a support vector machine (SVM) model. The new method and procedure has been developed to build, train, validate, and apply an SVM model. A conceptual support vector network is proposed to model fuel consumption, and the flight data collected from routes are used as the inputs to train an SVM model. During the training phase, an ROC curve is defined to evaluate the performance of the model. To validate the applicability of the trained model, a case study is developed to compare the data from an aircraft performance manual and from the implemented simulation model. The investigated aircraft in the case study is a Boeing 737-800 powered by CFM-56 engines. The comparison has shown that the trained SVM model from the proposed procedure is capable of representing a complex fuel consumption function accurately for all phases during the flight. The proposed methodology is generic, and can be extended to reliably model the fuel consumption of other types of aircraft, such as piston engine aircraft or turboprop engine aircraft.

216 A process model of coolant hose factory for lean manufacturing simulation

Lean Manufacturing (LM) is a production practice to eliminate waste by considering the expenditure of resources for any goal other than the creation of value for the customer to be wasteful. For achieving lean production, typical lean principles are already established as a set of LM tools, such as Single minute exchange of die (SMED), Kanban, total productive maintenance, etc, and some of these tools are also available as computer software. Even though these tools could resolve many manufacturing issues, expected results by LM tools cannot be seen before their implementation. This study focuses on a process simulation approach to see the effectiveness of LM tools before their implementation. This study designed a process model of a coolant hose manufacturing (CHM) factory and implemented its simulation model. This paper presents the implemented simulation model and shows how it could be used to see the effect of LM tools using an example of SMED LM tool.

Supporting systems of systems hazard analysis using multi-agent simulation

When engineers create a safety-critical system, they need to perform an adequate hazard analysis. For Systems of Systems (SoSs), however, hazard analysis is difficult because of the complexity of SoS and the environments they inhabit. Traditional hazard analysis techniques often rely upon static models of component interaction and have difficulties exploring the effects of multiple coincident failures. They cannot be relied on, therefore, to provide adequate hazard analysis of SoS. This paper presents a hazard analysis technique (SimHAZAN) that uses multi-agent modelling and simulation to explore the effects of deviant node behaviour within a SoS. It defines a systematic process for developing multi-agent models of SoS, starting from existing models in the MODAF architecture framework and proceeding to implemented simulation models. It then describes a process for running these simulations in an exploratory way, bounded by estimated probability. This process generates extensive logs of simulated events; in order to extract the causes of accidents from these logs, this paper presents a tool-supported analysis technique that uses machine learning and agent behaviour tracing. The approach is evaluated by comparison to some explicit requirements for SoS hazard analysis, and by applying it to a case study. Based on the case study, it appears that SimHAZAN has the potential to reveal hazards that are difficult to discover when using traditional techniques.

Simulation Modeling of SAM Fuzzy Logic Controllers

This paper presents an approach to implementing simulation models for SAM fuzzy controllers without the use of external components. The approach represents a fuzzy controller as a composition of simple simulation models which involve only basic operations.