Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control

Beklaryan, Gayane; Akopov, Andranik S.; Khachatryan, Nerses

doi:10.2478/cait-2019-0017

Cited by 13 publications

(12 citation statements)

References 28 publications

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“…optimized the new real-code GA with fuzzy control for real-coded genetic algorithms (F-RCGA). The algorithm aggregated with the system dynamics (SD-model) model and resulted in greater time efficiency of F-RCGA than RCGA others and the Monte-Carlo method [21].…”

Section: Related Workmentioning

confidence: 99%

Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house

Kushadiani

Buono²,

Nugroho

2022

Comput. Sci. Inf. Technol.

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As a scientific publishing house, Indonesian Institute of Sciences (LIPI) Press' encountered some problems in publication planning, mainly predicting the optimum production of publications. This study aimed to enhance a fuzzy inference system (FIS) parameters using the genetic algorithm (GA). The enhancements led to optimally predict the number of LIPI Press publications for the following year. The predictors used were the number of work units, the number of workers, and the publishing process duration. The dataset covered a five years range of total production of LIPI Press. Firstly, an expert set up the parameters of the fuzzy inference system denoted as a FIS expert. Next, we performed a FIS GA by applying the genetic algorithm and K-fold validation in splitting the training data and testing data. The FIS GA revealed optimum prediction with parameters that were composed of both population size (30), the probability of crossover (0.75), the probability of mutation (0.01), and the number of generations (150). The experiment results show that our enhanced FIS GA outperformed FIS expert approach.

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Section: Related Workmentioning

confidence: 99%

Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house

Kushadiani

Buono²,

Nugroho

2022

Comput. Sci. Inf. Technol.

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“…At the same time, if the number of simultaneously evaluated raw materials assets is large (for example, several thousand), then genetic optimization algorithms [23][24][25], aggregated with the simulation model of a production company, should be used to identify and "turn off" such fields.…”

Section: Automation Of Management and Production Processesmentioning

confidence: 99%

“…For example, simulation models developed in AnyLogic and written in the programming Java can be integrated with the applications designed with the use of C++ and MPI (message passing interface) for parallelizing the respective computational procedures. As a result, the simulation model can be aggregated with genetic optimizing algorithms through objective functions, providing the ability to optimize characteristics of the simulated object in real time [23][24][25]. To ensure the software and management pool of available digital twins, it is possible to use an approach based on a service-oriented architecture (SOA).…”

Section: Introductionmentioning

confidence: 99%

Developing digital twins for production enterprises

Makarov¹,

Bakhtizin²,

Beklaryan³

2019

Bus. Inform.

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This article presents a new approach to developing digital twins of production companies with the use of simulation methods. It describes the concept of digital twins as an integrated system that aggregates simulation models, databases and intelligent software modules of the class of genetic optimization algorithms, subsystems of data mining, etc. The article presents examples of simulation models of different production companies, in particular, a typical assembly plant and a typical oil production enterprise. The first company carries out activities to assembly products from individual components with its own individual characteristics. To describe the behavior of such an enterprise, methods of agent and discrete-event modeling are used. The second enterprise produces raw carbohydrate materials at existing fields with individual characteristics. The integrated simulation models thus developed are integrated with a subject-oriented database and optimization modules that facilitate providing a control of the technological and resource characteristics of the respective production enterprises. The development of these models was performed using AnyLogic and Powersim simulation systems that support agent-based modeling and system dynamics methods. We demonstrate here the possibility of creating 'digital twins' for production companies using modern simulation tools.

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“…систем следует выделить методы системной динамики [3,5,8,11,12,31], методы агентного [3,4,7,15,18,40] и дискретно-событийного [3,24,26,28] имитационного моделирования, а также генетические оптимизационные алгоритмы [9,10,30,32,33,37], обеспечивающие возможность поиска наиболее предпочтительных альтернатив при решении многокритериальных оптимизационных задач региональных социально-экономических систем.…”

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Intelligent multi-level decision-making system at the regional level: a conceptual approach

Beklaryan¹

2022

Herald of CEMI

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This article presents a new conceptual approach to the development of an intelligent multi-level decision-making system (DMS) at the regional level. Examples of the developed simulation models are given and the possibilities of their practical application for the search for rational management decisions at the regional and sectoral levels are considered. An approach to the design of DMS is based on simulation modelling for economic planning at the level of the whole country, industries and sub-sectors of the economy, federal districts, separate regions of the Russian Federation, households and individuals involved in socio-economic processes considering the complex mechanism of their interaction is proposed.

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Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control

Cited by 13 publications

References 28 publications

Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house

Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house

Developing digital twins for production enterprises

Intelligent multi-level decision-making system at the regional level: a conceptual approach

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