Дискретно-событийное моделирование морского грузового фронта экспортного угольного терминала в среде Anylogic

The value of modeling and simulation for education, training, and testing in information security has been documented in several studies. In this paper, we suggest that it is important not only to include the general use of simulation in various courses of the security curriculum, but also to include the theory and development of simulation models. We describe briefly the general features of simulation models and tools for model development that we are using in computing education.A collection of educational simulation tools have been created in the OOPsim project, for developing discrete-event simulation models. The principal goal of this project is to develop newer simulation tools and approaches for education in computing. The Object Oriented Simulation Language, OOSimL, was recently developed with partial support from an NSF CPATH grant.Two object-oriented simulation models are discussed as typical examples discussed in a simulation course on security: a model of a distributed denial of service (DDoS) and a model of simple firewall system. These models were developed with educational simulation tools created in OOPsim project. We have also developed a course that emphasizes an approach to early introduction to object-oriented discrete-event simulation.The DDoS simulation model is implemented using the OOSimL simulation language. The Firewall simulation model was implemented in Java with the PsimJ2 object oriented simulation package; other models have been implemented in C++ using the Psim3 object oriented simulation package. The simulation tools and model development are very useful for educating and training students and professionals in information security, computer science, software engineering, information technology, and in other related disciplines.

The authors have considered possibilities of organizing the training of students majoring in engineering specialty “131. Applied Mechanics” in the course “CAD of packaging production” using the specialized software of the AnyLogic Company. The paper presents software products for automated calculation and modeling of the technological process of packaging. As a result of studying the software product, the students will be able to master the techniques of automated calculation and design of packaging components and implement projects using modern CAD and CAM systems. The purpose of modeling the technological processes of packaging is to increase their productivity by determining and justifying the optimal parameters. The study of technological processes based on simulation modeling allows the students to identify interconnections, to highlight technical and economic indicators, to evaluate the impact on profitability, to determine the directions of development and ways to improve the technical and technological facilities. The methodology for developing a flexible automated line simulation model, for example, for the production of macaroni products, is described in this work. Authors highlighted the stages of modelling a flexible automated packaging system. The criteria that influence the technological process of bulk products packing are determined. The parameters for constructing the simulation model are selected. A formal description of the packing line has been made for a more detailed understanding of the process and the adequate construction of the model. With the help of the software of The AnyLogic Company, students create an acting part of the model, simulate processing at workshops and the work of a warehouse. As a result, in the developed simulation model, the authors researched the load of the production elements, determined problematic structural elements and calculated the coefficient of the technological line readiness. The virtual model of the technological line allowed testing the future technological line for adequacy, process ability and reliability. As a result of studying the discipline "CAD of packaging production" and AnyLogic software, students develop holistic understanding of the automated process and acquire the skills of modeling, analysis and optimization of the technological line.

Passenger Terminal Safety: Simulation Modelling as Decision Support Tool

This paper presents an approach to the modelling and forecasting of migration and demographic trends in science cities, using system dynamics and agent-based methods. An original simulation model was developed in the AnyLogic software, which considers the "gravity effect" on the redistribution of migration flows between Moscow as the centre and nearby science city such as Korolev, Obninsk, Troitsk, Dubna, Protvino, and Pushchino. Using the developed simulation model, we predict the dynamics of population (including those formed by migration) in these science cities and in Moscow under two scenarios. The first is a baseline scenario, assuming that current imbalances between Moscow and science cities will persist. The second is a more advanced scenario, in which economic growth in these scientific hubs is accelerated. As a result, it has been shown that science cities may potentially be more attractive for people to live in than a large neighboring metropolitan area, if there is an increased availability of socially important infrastructure (e.g., housing, kindergartens, schools, hospitals), as well as high-tech jobs with wages that exceed the inflation rate. This can ensure an efficient redistribution of population from the mega-city to the scientific cities, with a significant improvement in the demographic situation.

The development of simulation models can be time consuming and highly dependant on system data being widely available. When using simulation modeling to analyze future systems, system data may not be available for the system under study and simulation results are often needed within a short time frame to support early system design efforts. This paper presents a parametric estimation/generic simulation integrated environment developed to facilitate the rapid development of valid simulation models for the Orbital Space Vehicle ground processing operations.

In an increasingly competitive and global marketplace, simulation has become a very powerful tool for the design and analysis of new or existing systems. Simulation can be particularly beneficial when dealing with very large and complex systems. However, current simulation modelling methodologies are not geared towards model development for these types of systems. In this paper a modelling framework-called the Integrated Modelling Methodology-that makes the task of simulation model development for large and complex systems simpler and efficient is described. The proposed paradigm that uses a hierarchical and modular framework allows: a) demarcation of a complex system into manageable modelling units; b) development of simulation models for these units and; c) development of the final simulation model for the complete system. Specifications and the steps required for the development of simulation models using this approach are described in detail. The framework and its specifications are also illustrated with the help of modelling examples.

The development of simulation models can be time consuming and highly dependant on system data being widely available. When using simulation modeling to analyze future systems, system data may not be available for the system under study and simulation results are often needed within a short time frame to support early system design efforts. This paper presents a parametric estimation/generic simulation integrated environment developed to facilitate the rapid development of valid simulation models for the Orbital Space Vehicle ground processing operations.

The paper describes the pluming, design, and construction of an artificial island transshipping terminal in the Atlantic Ocean, 8 miles rom the coast of Brazil. At a connecting open-sea berth built along a 55-ft-deep natural hannel, salt and other granular materials can be loaded into 100,000 dead-weight ton (DWT) bulk carriers. Feeder vessels of 2,000 DWT can be unloaded on the sheltered side of the island. Up to 150,000 tons of salt can be accumulated in the storage area. Soros Associates established the feasibility of building and maintaining an artificial island in this equatorial part. of the Atlantic Ocean on the basis of extensive investigations of wind and wave data, tidal movements, currents, and bottom conditions. Seismic surveys and soil borings were carried out before the final design of the island and of the pile-supported structures connected with it. The solutions of technical problems encountered during the design and construction provide useful information for the planning of future artificial islands and offshore terminals. INTRODUCTION An artificial island was recently built 8 miles off the coast of Brazil, near Areia Branca, for TERMISA (Terminais Salineiros do Rio do Norte, S.A.) (Fig. 1). The island was designed for the transshipment of solar salt destined for markets located several thousand miles away. The decision to build this island was based on a detailed study of the region's salt producing industry, its markets, and the traditional methods of handling and transporting the product. The new terminal with its sophisticated bulk handling system replaces the antiquated method of hoisting hand-filled buckets from lighters on board of ships riding at anchor. Before selecting the artificial island as the most desirable solution, Soros Asso. investigated several alternate systems, including self unloading barges, floating storage facilities, long-distance conveyors, and aerial cableways. These investigations established that an offshore transshipping terminal would be the most desirable arrangement, provided that a barge-unloading wharf, a storage area, and a ship loading pier can be built at the right location, approximately latitude 040 49' South, longitude 370 02' West (Fig. 2). WIND AND WAVE ANALYSIS Special studies carried out by Sorts Assad., based on available statistical data and special surveys by the Hydrographic Dept. of the Brazilian Navy, indicated that in the open sea off AreiaBranca the frequency of wave height exceeding 5 ft is 10 percent, 7 ft is 2 percent, and the average wave is 3 ft high with a period of 4 to 5 seconds (Fig. 3). The area is under the general influence of the southeasterly trade winds, subject to seasonal variations in accordance with the movements of the Inter-Tropical Convergence Zone, located immediately to the north. Winds and waves are generally from the easterly quarter (Fig. 4). Prevailing wind velocity is 15 Impost. On the basis of statistical data, it was concluded that the ships can be loaded at an open-sea berth, but the barge unloading operation requires a wharf sheltered from prevailing waves.

Determining how to reasonably evaluate the production performance of bulk cargo terminals has always been a concern of the terminal managers. Aiming at the problem of production performance evaluation of the coal export terminals, this paper presents an evaluation method of production performance based on MAS (multi-agent system). A global simulation model of a typical export coal terminal is built in the paper, and the changes of production performance indicators under different conditions are discussed in detail. For the terminal studied, the experimental results show that the terminal production efficiency can be improved based on a series of measures, such as reducing the coal storage time, reducing the ship inter-arrival time, increasing ship loading capacity within a certain range. This method is helpful for the coal terminal managers to better understand the relationship between the terminal production performance indicators and related input factors.

Introduction. Personnel of the highest scientific qualification are the basis of staffing the competitive development all sectors of the national economy of the Russian Federation. At the same time, there is no reliable statistical information about the total number of Candidates and Doctors of Sciences and their age structure. Scientific publications do not present approaches to carry out the estimate of the number of persons with scientific degrees. Materials and Methods. The article proposes a model for calculating the age-specific number of Candidates and Doctors of Sciences on the basis of annual statistical data on the number of defenses of candidate and doctoral theses and survival rates. Since the detailed data are presented only for the period starting from 2008, and the aggregated data are not known for all years, the corresponding interpolation methods are proposed to restore the missing data. According to the proposed models, calculations of estimates of the number of Candidates and Doctors of Sciences are made, approximation functions are constructed. The results of the calculations were verified on the basis of statistical data on the number of researchers with a degree of Candidate and Doctor of Sciences in different sectors of the economy and the number of Candidates and Doctors of Sciences among the teaching staff of universities. Results. The results of the study are 6 models for calculating the number of Candidates and Doctors of Sciences, approximation function of the multiplicative survival coefficient for persons with scientific degrees, approximation functions of the age distribution of the applicants of degrees of Candidate and Doctor of Science, approximation functions of agerelated and cumulative distribution of the number of Candidates and Doctors of Sciences, quantitative estimates of the number of Candidates and Doctors of Sciences in 2019. Discussion and Conclusion. The proposed models have shown their adequacy. As a result of the calculations, it was found that the number of Candidates of Sciences in the age category of 20–80 years is 468,000 people, and the number of Doctors of Sciences in the age category of 30–90 years is 72,000 people. According to the obtained numerical values, approximating functions were constructed to get the number of Candidates and Doctors of Sciences in any age range. Since the system of personnel of the highest scientific qualification is sufficiently inert, the data obtained of age structure of the personnel of the highest scientific qualification will retain its character for at least another 10 years. The obtained estimates of the number of persons with scientific degrees allow us to characterize the personnel potential for decision-making in the management of high-tech sectors of the economy. The constructed models can be used to solve the problems of forecasting in the field of development of science-intensive technologies and in higher education for the training of highly qualified scientific personnel and replenishment of the scientific community.

Introduction. The study examines the problem of building an effective quality management system for education to improve its quality by changing the mechanisms of quality assurance and surveillance. The purpose of this study is to evaluate the impact of the transition to a risk-based regulation in higher education institutions. Materials and Methods. The study was conducted using the methodology of mathematical modeling, namely, simulation models of certain types of state quality management and surveillance in the field of higher education: licensing, licensing management, quality assurance mechanisms and state supervision. Modeling was carried out using the Anylogic software. The models were calibrated on the basis of statistical data from the Federal State Information System ‘Unified Register of Inspections’: a total of 1,542 inspections for four types of assessment from 2014 to 2019. The main indicators to be evaluated were the duration of assessment and supervisory measures for each type of control, as well as expenses for their implementation. Results. The study reveals that the transition to risk-based regulation in the field of higher education is accompanied by significant positive changes - a reduction in the load both in terms of the assessment duration and costs of its implementation. It is shown that for a more accurate assessment of these effects, it is necessary to take into account the differences in the structure and quantitative characteristics of processes of different types of control. The quantity of the effects is determined by the choice of characteristics of the risk-based approach model: the proportion of high-risk objects and the probability of detecting violations as a result of checking them. The proposed methodology developed by the authors can be used to make management decisions by comparing models of quality management and surveillance of universities. Conclusions. The research findings empirically confirmed the need to move from the current system of quality management in the field of higher education to a risk-based regulation. Adopting this approach will reduce the burden both on the subject of control - the Federal Service for Supervision in Education and Science, and on the objects - higher education institutions.

Performance analysis of manufacturing systems : queueing approximations and algorithms

Dear Editor, We read with great interest the recent manuscript by Mostafa Alavi-Moghaddam et al. (1) on “Application of Queuing Analytic Theory to Decrease Waiting Times in Emergency Department: Does it Make Sense?” published in the Archives of Trauma Research. In this article, the authors described a discrete events simulation (DES) model deployed on the ARENA simulation software to model the processes in an Emergency Department (ED). The simulation model was based on empirical data of actual ED operations for The Imam Hosein Hospital located in Tehran, Iran, with annual input of approximately 50,000 patients, 24 general emergency beds and 14 trauma emergency beds. The application of simulation models for improving EDs’ work processes started in the late 1980s (2) and have been gaining traction over the last decade (3-11). The objectives of simulation modeling for EDs have been varied, ranging from infrastructural designs (3), process improvements (4-6, 12), staffing decisions (10) and near-term forecasting for proactive operational management (11). Simulation models in general can be developed for different hospital processes under a variety of settings. However, differences in operating parameters, such as process designs and infrastructural configurations, prevent easy generalizability of simulation models, associated results and policy insights. Different objectives may also entail different modeling assumptions and data requirements. It should be noted that the ability for any simulation model to faithfully represent the actual system have to be justified through rigorous model validation and verification processes before they can be used for scenario analysis and policy testing (13). The authors described a set of scenarios related to manpower resource management (e.g. nurse scheduling) and capacity improvements (e.g. changes to diagnostic, laboratory, consultation and discharge capacities) using the simulation model. Forecasting accuracy is critical in such simulation models, since the quality of realized performance hinges upon the accuracy of forecasts. To a certain extent, scenario analytic techniques may be able to circumvent this issue by presenting a plethora of plausible scenarios with sufficiently wide coverage thereby facilitating the development of policies robust against the identified scenarios. Estimates of individual parameters describing the ED system are integrated within DES models that capture the interlocking internal dynamics within complex ED systems (11, 14). As demonstrated in the study, the internal dynamics of ED systems can be captured through the use of DES models to provide a viable decision support tool. The simulation model examined typical input, throughput and output factors influencing ED overcrowding and reported encouraging results. The mean waiting times at the various processes and average length of stay (LOS) within the ED have been reported as the primary outcome measures. Other outcome measures, such as utilization levels for beds or care units (11), probability of ambulance diversion (11, 15, 16), ED productivity (17) and the proportion of patients who leave without being seen (LWBS) (18), can also be of interest to decision makers. Empirical data is critical for the development of a robust simulation model that considers fluctuating demands and dynamic resource allocation decisions which characterizes ED operations. Characteristics of patient demands (e.g. triage categories, reneging characteristics and medical/ trauma categories) and the historical distributions of pertinent time intervals (e.g. patient inter-arrival times and service times of the various ED processes) are important parameters that have to be carefully modeled. Accurate and updated spatiotemporal patient flow data may not be readily captured via the data-collection scheme described in the paper. Real-time location systems (RTLS) for tracking the status and movement of various entities within the ED (e.g. patients, healthcare workers and physical assets) (19) may improve the quality of data, and hence, the accuracy of forecasts. Simulation modeling provides a rigorous and versatile platform for evidence-based decision making in ED process improvements, infrastructural designs and policy evaluations. Simulation models have been shown to provide for short term operational, tactical and strategic ED planning and management. Different study objectives necessitate different modeling assumptions, data resolution and data quality requirements. As ED overcrowding has been identified as a significant international crisis that is detrimental to patient safety, quality of care and patient satisfaction (20), there is an immediate need to re-examine and improve ED processes using rigorous evidence-based and quantitative modeling techniques. In this respect, it is encouraging to see the increasing adoption of simulation to quantitatively model the inherent complex system dynamics present in EDs around the world.

Coal terminals justify the effort and expense of simulation modeling because they are critical connectors between coal transport modes and have a strong influence on the end cost of coal. Three major factors to be considered by the modelers are: coal terminals have special characteristics in terms of queuing theory; they require modeling of commodity storage; and they have complex materials handling facilities. As a case study, Richards Bay Coal Terminals in South Africa illustrates a complex operational and corporate problem which presented a challenge to the simulation method of modeling.

The summary (statistics) data model described herein is an extension of the relational model. The concept of category (type or class) and the additivity property of some statistical functions form the basis of this model. In this approach category shields details of a database instance from users, and plays an important role in deriving new statistics data. Statistics data is a trinary tuple consisting of 〈statistical function, category, summary〉. The additivity property allows new statistics data to be generated without having to access the original database. Statistics data is meta-knowledge summarized by statistical functions of the detailed information typically stored in a conventional database. Unfortunately, deciding whether a category is derivable from a set of categories, in general, is NP-hard. The proposed generating category set can resolve the intractability problem of the category derivation. The derivation of new statistics data within a relation or on multi-relations is investigated, and the efficiency and correctness of the stored statistics data are guaranteed when the original database is updated or when new statistics data is obtained. Finally potential applications and security concerns applying to this model are also discussed.