Mathematical model of operational management technological complex continuous type

Ladanyuk, Anatolii; Прокопенко, Тетяна Олександрівна; Reshetiuk, Volodymyr

doi:10.22630/aafe.2017.12

Cited by 2 publications

(3 citation statements)

References 1 publication

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“…This approach is also quite traditional and does not make it possible to respond quickly to changes. The cognitive approach applied in [10] provides opportunities to establish a causal relationship between risk factors and risky events, as well as risk control measures. However, this does not take into account the possibility of situations with the absence of statistical information about the possibility of a risky event.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Our analysis [3][4][5][6][7][8][9][10] showed that existing models and methods of project risk management in various industries do not provide opportunities for generating management decisions according to the current situation. Crisis situations in projects that arise under conditions of uncertainty are characterized by losses, overruns of resources, and affect the efficiency of the project.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The integrated application of situational management methods, intelligent and expert methods, as well as Big Data technologies for project risk management, in contrast to [6][7][8][9][10], has a number of the following advantages:…”

Section: Strategy Adoptionmentioning

confidence: 99%

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Development of the comprehensive method of situation management of project risks based on big data technology

Прокопенко

Lanskykh

Prokopenko

et al. 2023

EEJET

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Project implementation is often carried out under the influence of negative changes in the environment and circumstances characterized as crisis. Therefore, the processes associated with risk management, which is the object of this study, are becoming important. The topic of this study is to increase the efficiency of projects by adapting the project to crisis conditions, promptly developing and making effective management decisions. For projects, it is necessary not only to identify the current situation as risky but also to determine rational ways to achieve the project goals under crisis conditions. Therefore, a comprehensive method of situational project risk management based on the combined application of situational analysis methods, intelligent and expert methods, as well as Big Data technology, is proposed. Within the framework of the method, a project risk management model has been built in the form of a fuzzy situational graph, which would provide a choice of strategies that could contribute to overcoming a risky situation, as well as reduce the time to make effective management decisions in crisis circumstances. The result of this method is compliance with time constraints, reducing resource overruns and losses in the project, as well as adapting to rapidly changing circumstances and adequate response. A comprehensive method of situational project risk management is characterized by solving the task toformalize management decision-making procedures and their information support, taking into account the availability of both structured and unstructured data. The proposed procedure for situational project risk management based on the use of Big Data technology can also be the basis of project management information technology and the corresponding decision support system

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Development of the comprehensive method of situation management of project risks based on big data technology

Прокопенко

Lanskykh

Prokopenko

et al. 2023

EEJET

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Methodology of Development of Intellectual Energy Efficient System of Control of Temperature-Humidity Regime in Industrial Heat

Yakymenko¹,

Lysenko²,

WITASZEK³

2021

Tehnìka ta energetika

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Methods of improving the quality of regulation of technological parameters by combining various intelligent control algorithms in one automation system, which helps to reduce energy costs by 10-13%, are analyzed. It has been established that heating and ventilation systems have the highest energy consumption for indoor buildings (on average, more than 4,000 m3 of natural gas and almost 1,000 kWh of electricity are consumed per day for heating and ventilation in an industrial greenhouse. Correlation analysis of links between external disturbances and energy costs that ensure compliance with the technology of plant production, confirmed the hypothesis of conditions of uncertainty in the operation of industrial greenhouses are formed by random disturbances, incomplete information about the biological component, with linear correlation coefficients not exceeding r<0.35. both for forecasting energy costs and for the formation of energy efficient management strategies. Based on the use of fuzzy logic methods and genetic algorithm, models for finding and using optimal parameters of PI controller settings adapted to changes in the operating conditions of the automation system have been developed and studied. This provides better regulation in conditions of uncertainty, the time of regulation, over-regulation is reduced by two to three times. To create an energy-efficient microclimate management system in industrial greenhouses, operating in conditions of uncertainty, a neural network model for predicting the energy consumption of natural gas and electricity has been developed. The input parameters of the neural network forecasting model are: the value of external and internal air temperatures of the greenhouse, the value of relative humidity, the solar radiation absorbed by the greenhouse and the level of carbon dioxide in the greenhouse. The outputs of the forecasting model are the values of natural gas and electricity costs. The structural and functional scheme of the temperature and humidity control automation system in industrial greenhouses has been improved by combining intelligent algorithms for stabilizing the operation of technological equipment at the lower management level and optimizing energy costs by forecasting them at the upper level. The introduction of such a system saves up to 13% on natural gas for heating and up to 10% on electricity.

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Mathematical model of operational management technological complex continuous type

Cited by 2 publications

References 1 publication

Development of the comprehensive method of situation management of project risks based on big data technology

Development of the comprehensive method of situation management of project risks based on big data technology

Methodology of Development of Intellectual Energy Efficient System of Control of Temperature-Humidity Regime in Industrial Heat

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