Complex modeling of complicated processes: analytical model parameters adaptation in solving qualimetric problems

Purpose of the study.The aim is to create a technique targeted at the development of simulation skills in higher education environment, where students are competent in applying information technologies in economics. The relevance of the research lies in the fact that the existing methodological developments often focus on a specific software tool or methodology that cannot respond to all economic problems. A specialist in simulation modeling should possess integrative interdisciplinary knowledge from related scientific fields, for example, probability theory and mathematical statistics, higher mathematics, be familiar with other methods of solving economic problems: linear, nonlinear, dynamic programming, optimization; show proficiency in structural and functional analysis; be able to explore complex processes and systems comprehensively.Materials and methods.The following pedagogical approaches and teaching methods were implemented in this research: ● a systematic approach to solving complex problems based on the modelling economic objects as systems operating in a certain environment, ● activity approach to develop students’ professional competences in the process of creation, debugging and optimization of computer models of economic systems; ● problem teaching method in the framework of research and analysis of educational problems of the subject area; ● implemented interactive teaching methods; ● multimedia methods in the content of teaching materials of the discipline, including electronic manuals, educational videos, as well as multimedia presentations. The research also utilized information technologies, in which computers, communication equipment and software environments are: ● means to provide educational material to students for the transfer of knowledge; ● tools for designing, developing and conducting simulation experiments. In addition, we used the following special professional technologies, methods and tools in the process of teaching students: ● structural and functional modeling methodology; ● discrete-event approach to simulation methodology; ● special software for development and research of simulation models of economic processes and systems: Arena 15.0, AnyLogic 8.3.2.According to the requirements of the new educational standards, the student must master a sufficiently large amount of general cultural, professional and specialized competencies included in the curriculum. The application of the proposed approaches and methods allows to provide effective development of skills of simulation modeling of educational programs for bachelors of «Applied Informatics» and «Business Informatics».Results.The study created a method of teaching students the skills of simulation modeling. The research also established the model of formation of readiness of the IT specialist to the development of simulation models of economic processes and systems in higher school. We also identified important methodological conditions for the formation of professional competencies of students in the field of modeling, such as: ● application of a systematic approach to the analysis of domain problems, as well as for the synthesis of mathematical simulation models of business processes and economic systems; ● practical orientation of the content of training (selection and research in the educational process of the most characteristic, typical problems of the economy); ● integration of interdisciplinary knowledge, methods and approaches to solve complex problems.Conclusion.The method can equip students with skills of simulation modeling with various areas of practical application. First, this technique can be used by university students who are engaged in pursuing practical skills and basic system knowledge in the field of simulation. Secondly, teachers can use it, conducting courses: “Computer modeling”, “Mathematical and simulation modeling”, “Modeling of processes and systems” in the educational process of the University to improve the professional competence of students training simulation modeling. Third, the outcomes may be of interest to managers of educational programs in the areas of: “Applied Informatics”, “Business Informatics”, etc. to improve the structure and sequence of disciplines of competence-oriented curricula. Finally, the application of the proposed methodology in the educational process of the University will enhance professional expertise of young specialists and undoubtedly address the needs of potential employers.

Background and Purpose: Business process simulation (BPS) model is based on real-life data form sources like databases, observations and interviews. It acts as “as-is” business scenario can used for reengineering. The main challenge is to gather relevant data and to develop simulation model. Research aims to elaborate BPS model and to systematically assess how close to reality it is. Design/Methodology/Approach: The research has been performed in Polish telecommunications company. Authors investigate technical process of expanding cellular network. After elaborating “as-is” model, authors use ADONIS simulation tool to run a series simulations and confront simulation results with actual historical events. After this, assessment whether computer simulation model can precisely map real-life business process - and consequently act as credible basis for process improvement - is made. Results: The simulation model has been constructed with data from the WfMS database, observations, staff knowledge and their experience. Fully equipped simulation model is found to allow reconstructing the historical execution of business activity with low margin for error. Some limitation were identified and discussed. Conclusion: BPS is not a popular approach for process reengineering and improvement yet. Data collection issues for BPS that require adopting process mining techniques and additional information sources are among the reasons for that. In our study, computer simulation outputs are compatible with historical events. Hence the model reflects the business reality and can be taken as a reference model while redesigning the process.

The aim of the research is to develop a modeling methodology for solving problems associated with the creation of simulation models and means of information and technological support for the tasks of allocating water resources on drainage systems for double regulation of the water regime of soils. The methodological and informational basis of the work was formed by the experience of creating and operating existing means of information technology support for the design and operation of double regulation drainage systems, the provisions of normative and technical documents on the automation of systems, as well as the design features of drainage and humidification and drainage systems. irrigation systems. As a result of studies of the problem of distribution of water resources on a specific system, the main stages were determined: the allocation of its blocks for which a system of water resources regulation is being developed, the allocation of subsystems in the structure, classification of tasks in subsystems, construction of an information model of the system, construction of a complex of models. To solve the problem associated with the development of simulation models and means of information technology support for the tasks of water resources distribution on dual control systems, it is proposed to use the method of contour modeling of water resources management regimes. The essence of the methodology consists in identifying characteristic contours in the structure of the water distribution network, for which you can use standard modeling algorithms and tools (quickly adjusted to the parameters of these contours) and sequentially calculating the parameters of the distribution of water resources for each of them. In this connection, the main control loops have been determined: the control loop «Water consumer № 1, 2 ... n», «Group of water consumers № 1, 2 ... n», «Section of the system № 1, 2 ... n» and «System». The proposed technique allows you to select typical control loops and then, in the selected standard loop, select characteristic control loops, along which simulation modeling of the processes of regulating the distribution of water resources over all control loops is carried out. The carried out simulation modeling of regulation processes throughout the «system» can be used by the maintenance service of the double regulation drainage system as variations in solutions when controlling the operation of regulatory structures, pumping stations and other structures of the system used to control water distribution.

Scientific article is devoted to researching the possibility of developing a simulation model of an investment project for a travel agency. In recent years, the tourism industry in the world is experiencing rapid development. The development of the tourism industry requires new investments. The investor must assess the degree of risks, the likelihood of profit or loss. It is noted that the most important advantage of simulation modeling is that it makes it possible to study economic systems at the design stage. Due to this, simulation models can be used as a universal tool in making appropriate decisions under conditions of uncertainty and taking into account those factors that are difficult to predict and take into account, which is why simulation is so often used in the development of investment projects. When assessing the risks of investment projects, the collection of information requires significant financial expenses, is quite time-consuming, and sometimes impossible. The subject of the study is a set of the theoretical, methodological and organizational problems related to the management decisions regarding the investment of the projects in the field of tourism. The methodology is based on the stochastic simulation modeling of economic processes. The aim of the study is to develop a simulation model for an investment project in the tourism sector, which will allow the investor to appreciate the degree of the risk and the likelihood of return on the investment. The article reveals the features of computer simulation in the MathCAD system. It is proved that it is convenient to develop simulation models of investment projects in the MathCAD system. It has a powerful mathematical support, remains one of the systems in which the description of the solution of mathematical problems is given with the use of conventional mathematical formulas and symbols and does not require special training in programming. The simulation model of the investment project of the travel agency was developed in the MathCAD system. Statistical processing of the results of the experiments with the model has been carried out. The necessity of the detailed study of critical intervals of the histogram at the transition from loss-making to profitable NPV values has been substantiated. Based on the results of simulation experiments with the model, the errors of the model were estimated, the law of NPV random variables distribution was established. The developed simulation model allows the investor to estimate the risk factor, the probability of profit or loss from the investment, to assess the possible uncertainty of the results of their own decision to invest in the project. Conclusion: the simulation model of the investment project built and investigated in MathCAD system allows investor to estimate the risk factor, probability of profit or loss for choosing the optimal pricing policy and optimization of economic strategy of tourist agency.

We review the state of the art of process modeling for discrete event simulation, make a number of observations and identify a number of issues that have to be tackled for promoting the use of process modeling in simulation. Process models are of particular interest in model-based simulation engineering approaches where the executable simulation model (code) is obtained with the help of textual or visual models. We present an illustrative example of model-based simulation development.

The increasing demand for software project managers in industry requires strategies for the development of the management-related knowledge and skills of the current and future software workforce. Although several approaches help teach the required skills in a university setting, few empirical studies are currently available to characterize and compare their effects. This paper presents results of an externally replicated controlled experiment that evaluates the learning effectiveness of using a process simulation model for educating computer science students in software project management. While the experimental group applies a system dynamics (SD) simulation model, the control group uses the well-known COCOMO model as a predictive tool for project planning. The results of the empirical study indicate that students using the simulation model gain a better understanding about typical behavior patterns of software development projects. The combination of the results from the initial experiment and the replication corroborates this finding. Additional analysis shows that the observed effect can mainly be attributed to the use of the simulation model in combination with a web-based role-play scenario. This finding is strongly supported by information gathered from the debriefing questionnaires of subjects in the experimental group. They consistently rated the simulation-based role-play scenario as a very useful approach for learning about issues in software project management.

Purpose. When solving practical problems that require the creation and further analysis of the model, an important criterion is the labour intensity of modeling. In this regard, the article is aimed at formalizing the modeling process and using the method of step-by-step modeling for the design of technological processes. This approach allows you to design processes and tasks according to the following stages: physical modeling, mathematical modeling, discrete computer modeling and simulation. Methodology. To solve the problem, a methodology of step-by-step modeling is used. The simulation involves 3 stages and uses the decomposition algorithm, i. e. considers the problem from global to detailed. At the first stage of this implementation, the necessary information is collected for the experiment. This information is presented in the form of statistics. In the second stage, further processing takes place, which is performed by checking the compliance of the input data and the process with the question of how this process should be performed. The last stage is the simulation of passages of this fragment, which is represented by a chain of transitions, obtaining statistics of time efficiency of this process, weaknesses of the process and the ability to compare the results obtained during modeling and in the real process, as well as the ability to predict future results and actions. Findings. The technique can be used to study complex technological processes in the enterprise. It allows modeling of complex processes to obtain information about the time efficiency of the technological operation, finding weaknesses in it and patterns in the occurrence of random events that may affect the operation. Using this approach can be very effective in the systems that require constant real-time monitoring, as this tool can be modified by adding sensor kits that will constantly send information to the system or equip an additional system that will provide ready-made information packets. Originality. The method of step-by-step modeling of representation has been improved, which consists in the simultaneous use of physical, mathematical and simulation modeling of complex processes with a set of stages of their implementation. Practical value. The proposed technique is designed for step-by-step modeling of the technological process with the subsequent construction of simulation programming.

Errors of calibrator-assigned values lead to errors in the testing of patient samples. The ability to estimate the uncertainties of calibrator-assigned values and other variables minimizes errors in testing processes. International Organization of Standardization guidelines provide simple equations for the estimation of calibrator uncertainty with simple value-assignment processes, but other methods are needed to estimate uncertainty in complex processes. We estimated the assigned-value uncertainty with a Monte Carlo computer simulation of a complex value-assignment process, based on a formalized description of the process, with measurement parameters estimated experimentally. This method was applied to study uncertainty of a multilevel calibrator value assignment for a prealbumin immunoassay. The simulation results showed that the component of the uncertainty added by the process of value transfer from the reference material CRM470 to the calibrator is smaller than that of the reference material itself (<0.8% vs 3.7%). Varying the process parameters in the simulation model allowed for optimizing the process, while keeping the added uncertainty small. The patient result uncertainty caused by the calibrator uncertainty was also found to be small. This method of estimating uncertainty is a powerful tool that allows for estimation of calibrator uncertainty for optimization of various value assignment processes, with a reduced number of measurements and reagent costs, while satisfying the requirements to uncertainty. The new method expands and augments existing methods to allow estimation of uncertainty in complex processes.

Simulation software Tecnomatix Plant Simulation was originally created for a modelling and subsequent simulation of production and logistics processes. Its variability, however, opens its use also in other areas such as transport in urban agglomerations. Based on that, research was implemented to verify the program’s application in urban transport, specifically to visualize and simulate traffic processes at the traffic node. The paper describes a methodology which made it possible to create a simulation program for traffic light intersections, and presents examples of the simulation model application. The proposed methodology will enable the application of Tecnomatix Plant Simulation to create a complex simulation model of the logistics process. It will mainly enable to simulate the field of production logistics and city logistics within one simulation model.

В індустріально розвинених країнах імітаційне моделювання логістичних ланцюгів використовується досить широко і рахується звичайною складовою частиною проектів по створенню нових або реконструкції існуючих логістичних ланцюгів. В даній статті були розглянуті три підходи до імітаційного моделювання логістичних систем. Розглядалося застосування різних підходів імітаційного моделювання для оцінки ефективності логістичних процесів. Представлена узагальнена структура імітаційної моделі функціонування логістичної системи. Для її реалізації запропоновано використання трирівневого комплексу моделей, що використовують різні парадигми імітаційного моделювання. Були розглянуті та окреслені переваги кожного із підходів імітаційного моделювання логістичних процесів, зокрема: системний динаміка, дискретно-ситуативний підхід, агентний підхід. Визначено призначення моделей кожного підходу: системна динаміка – для проектування загальної структури логістичної системи; дискретно-ситуативний підхід – для поліпшення параметрів окремих логістичних процесів; агентний підхід – для незалежного територіально-розподіленого ланцюга поставок. Для прикладу було взято узагальнену імітаційну модель логістичної системи у вигляді «чорного ящика». Запропоновано приклад реалізації рівня дискретно-ситуативних моделей у вигляді трьох типових видів моделей. У середовищі імітаційного моделювання Arena розроблена мультипродуктова модель для оцінки базових показників ефективності логістичної системи: рівень обслуговування, середній рівень запасів, середній рівень затриманих замовлень, загальна кількість втрачених або затриманих замовлень. В ході подальшої роботи планується наповнення запропонованого комплексу моделей з використанням всіх трьох підходів імітаційного моделювання. В цілому можна зробити висновок, що застосування переваг кожного із підходів імітаційного моделювання є перспективним і може бути використаний для широкого кола підприємств різних галузей діяльності.

The article deals with continuous personal and professional self-improvement of the specialists in the field of information technologies and pedagogical conditions as the development and formation of versatile aspects of professional training of the future specialists. The specificity of pedagogical activity arises from the fact that it combines organizational, research and creative components, therefore, in modern pedagogical conditions, it is important to be able to actively perceive new things, ensuring the continuity of personal and professional self-improvement. For higher pedagogical educational institutions, the primary task is the formation of the future teacher personality who is able to be constantly engaged in the personal and professional self-improvement, ready for continuous education, that is, for regular self-development. The development of readiness for self-improvement of future specialists in the field of information technologies is a rather complex process, the effectiveness of which depends on the implementation and observance of the necessary pedagogical conditions. Usually, pedagogical conditions are interpreted as those that are specially created in the educational process to increase its efficiency or implement innovations. The issue of qualification of pedagogical conditions has become the subject of attention of many researchers. We consider the analysis of a number of scientific works in which the pedagogical conditions of development and formation of versatile aspects of professional training of future specialists are considered important for our exploration of the features of the development of self-improvement skills of future specialists by means of information technologies. The personal self-improvement, which begins at school, should continue life-long. The professional self-development is a complex, multifaceted, continuous, conscious process aimed at the formation, integration and implementation of professionally significant personal traits, professional knowledge, abilities and skills in the professional activities. The future specialists are creative individuals who constantly have many new ideas and are ready to accept innovations in education. After all, the teacher must regularly prepare new, interesting lectures, be able to adapt to changes in the educational process. Modern student teachers have to master a variety of educational technologies. In the professional activity of the teacher, a structural component is the professionalism of self-improvement.

*† ‡ We develop an approach for estimating model parameters which result in the “best distribution fit” between experimental and simulation data. Best distribution fit means matching moments of experimental data to those of a simulation (and possibly matching a full probability distribution). This approach extends typical nonlinear least squares methods which identify parameters maximizing agreement between experimental points and computational simulation results. Several analytic formulations for the distribution matching problem are provided, along with results for solving test problems and comparisons of this parameter estimation technique with a deterministic least squares approach. I. Introduction Nonlinear models are frequently used to model physical phenomena, including engineering applications. In this paper, we refer to a nonlinear model very broadly: the output of the model is a nonlinear function of the parameters [Draper98]. Thus, nonlinear models can include systems of partial differential equations (PDEs). Some examples include CFD (computational fluid dynamics), groundwater flow, heat transport, etc. Nonlinear models also include functional approximations of uncertain data via regression or response surface models. In most cases, we have some type of simulation model which is a nonlinear model, so we use the term nonlinear model and simulation model interchangeably in this paper. In addition to a simulation model, we assume that we have experimental data which may be used to calibrate the model. The calibration often requires the solution of an optimization problem to determine the optimal parameter settings for the simulation model. In this paper, we are concerned with identifying model parameters which result in a “best fit” between experimental data and simulation results in a nondeterministic context. That is, instead of matching point estimates we are concerned with matching moments (e.g., mean or variance) between experimental and simulation data where the variability in the model output is due to parametric uncertainty. We develop an approach extending the typical nonlinear least squares formulation to allow for this distribution matching. Note that “parameters” may be parameters in an approximation model such as a regression model, or physics modeling parameters which are used in physical simulation models such as PDEs. We distinguish data from parameters: data are physical data which are input either to a regression or physical simulation. For example, in groundwater flow modeling, hydraulic conductivity is a parameter and data may include measured flow rates from well tests. In this paper, we denote parameters that will be calibrated as θ, and the independent input data (e.g., state variables, configuration data, boundary conditions) as x. We also assume that there are uncertain variables, denoted by u, that represent inherent variability or lack of exact knowledge influencing the simulation, but which we cannot observe. The effect of the uncertain variables is reflected in both the output variability of the nonlinear model and the experimental data, but we can only explicitly account for these uncertain variables in the simulation model. Thus, our simulation model, f,

For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/β-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models.

Fused deposition modeling (FDM) offers many advantages over conventional manufacturing methods, but it is limited by the number of materials available. Extending FDM technology to semicrystalline polymers has been challenging due to the crystallization that occurs during cooling which results in FDM part warpage. Previous work used process simulation models to study the effects of material parameters and FDM process variables on the part warpage seen using polypropylene (PP). In this work, the process simulation models were adapted to investigate warpage of FDM parts made with a high-performance semicrystalline polymer, polyphenylene sulfide (PPS). Material parameters in the PPS process simulation models were individually changed to the PP values to investigate which material parameters cause PP to exhibit higher warpage than PPS. Material parameters of interest included coefficient of thermal expansion (CTE), thermal conductivity, heat capacity, and Young’s modulus. Additional material parameters based on material property modification through the addition of fillers were investigated in order to establish the relationship between material parameters and warpage values. The simulation models suggested that the CTE has the largest impact on FDM part warpage. Decreasing the CTE in the simulation model resulted in a decrease in the FDM part warpage by the same factor.

Domain generalization for chiller fault diagnosis requires balanced data, but automatic shutdowns cause fault data scarcity and class imbalance. We propose a simulation-based method that integrates simulated and real data to address this issue. First, a chiller simulation model is designed, and its heat transfer parameters are fine-tuned using operational data to reduce the discrepancy between the simulation model’s response and the actual unit’s response. Then, a mapping model is developed, employing an encoder-decoder network to further improve the similarity between simulated and real data. These two refinement steps ensure that the simulation model’s response closely approximates the behavior of the real system. Finally, a domain-invariant feature extraction model for chiller is developed. This model uses domain adversarial training to extract domain-invariant representations and incorporates a local domain discriminator to perform class-level domain differentiation, enhancing the feature extraction capability. The proposed method is validated on a laboratory chiller unit system. Comparative analysis with other domain generalization methods demonstrates that the proposed approach achieves higher diagnostic accuracy, with an average diagnostic rate reaching 88.97% across three tasks, while offering enhanced data interpretability.