To the problem of dynamic modeling and management in an integrated environment of the industrial cluster

Smirnova, G. S.; Sabitov, Rustem A.; Morozov, Boris; Sabitov, Shamil R.; Sirazetdinov, Bulat; Elizarova, N. Yu.

doi:10.1016/j.ifacol.2015.06.252

Cited by 14 publications

(12 citation statements)

References 2 publications

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“…Thus, composing and solving of the model examples is one of the most important stages for development and implementation of the economic and mathematical models. (11)(12)(13)(14)(15)(16)(17)(18) The work presents setting and model example for the problem for selection of the investment sources and objects. The basic provisions of the economic and mathematical model are given bellow.…”

Section: Methodsmentioning

confidence: 99%

Mathematical modeling in economics for selection of optimum investment solution

Alukhanyan

Panfilova

2021

E3S Web Conf.

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This work is devoted to development of economic and mathematical models for selection of the optimum investment solution. Moreover, it states the basis for development of model examples and correction of the model considering the results obtained in the examples. In the work the problem is set for selection of the investment sources and objects, which is limited to the linear programming problem. The controlled variable and basic limitations simulating real credit and monetary relations are distinguished in the provided model. The discounted profit obtained from implementation of the optimum investment portfolio is considered as a target function. The economic and mathematical model presented in the article allows finding the optimum investment solution within the limits of the credit and monetary relations taking place both at the micro- and macroeconomic level.

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Section: Methodsmentioning

confidence: 99%

Mathematical modeling in economics for selection of optimum investment solution

Alukhanyan

Panfilova

2021

E3S Web Conf.

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“…The development of regional clusters is a line of current research that is sustained as an engine of economic development and a source of industrial competitiveness (Felzensztein et al 2018;Sosnovskikh 2017). Government action has promoted the formation of these competitiveness schemes, through the formulation of policies that facilitate their conformation and institutionality (Asheim et al 2003;Gancarczyk and Gancarczyk 2018;Smirnova et al 2015), the maturity of the signatures (Lee 2018;Sosnovskikh 2017), foreign investment (Lorenzo et al 2018;Alefari et al 2018), international cooperation (Delgado et al 2015;Feser et al 2008), and formation of knowledge networks (Felzensztein et al 2018;Gancarczyk and Gancarczyk 2018;Alefari et al 2018).…”

Section: Cluster Competitivenessmentioning

confidence: 99%

“…Investigations (Felzensztein et al 2018;Gancarczyk and Gancarczyk 2018;Cano-Kollmann et al 2016;Alefari et al 2018;Smirnova et al 2015;Delgado et al 2015;Carbonara and Giannoccaro 2011;Lin et al 2006) analyze clusters as a complex system that responds dynamically to technology and competition. Complexity is associated with the number of actors in the system and bilateral relations in operational transactions, market operations, knowledge transfer, and dissemination of innovation (Gancarczyk and Gancarczyk 2018;Menzel and Fornahl 2009;Huggins and Johnston 2010;Jack et al 2008).…”

Section: Cluster Competitivenessmentioning

confidence: 99%

“…Porter 1990;Carayannis et al 2012). The latter are characterized by a systemic vision that brings knowledge to the complex structure of the system and identifies the dynamic relationships between endogenous and exogenous variables (Castellacci 2018;Alefari et al 2018;Smirnova et al 2015). Furthermore, it allows the system simulation, evaluating strategies and policies, analyzing scenarios and making decisions based on systemic analysis (Wiley and Schaffernicht 2007;Sterman et al 2015;J.…”

Section: Introductionmentioning

confidence: 99%

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Cluster Competitiveness Modeling: An Approach with Systems Dynamics

2020

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This study makes a systemic review to cluster and create a competitiveness relationship considering a systems dynamics approach. A dynamic hypothesis was constructed to validate what factors increase a cluster’s level of competitiveness, through causal analysis. Then, the causal diagram that validates the dynamic H0 hypothesis was constructed in Vensim PLE systems®. Literature review shows the evolution of the cluster system according to the current needs of the market, and emphasizes the need for new approaches and models that capture the complexity and dynamics of this system, allowing the understanding of its structure and the evaluation of the contribution of factors and capabilities to cluster competitiveness. It highlights the usefulness of systems dynamics as a simulation methodology for dynamic and complex systems, and establishes itself as a growing line of research applied to various systems of study. Dynamic hypothesis H0 was validated using the causal diagram, reaching the conclusion that innovation, productive management, financial management, organizational management, commercial management, and cluster management factors positively increase the cluster competitiveness level. From structure analysis, the behavior is associated to the archetype “Path Dependence”, usual in growing industrial markets.

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“…All source (input) information for the model can be divided into two groups of data: data on the fleet of machines and data on the technological process. [8,[12][13][14] The following managed variables can be distinguished in the model: (6)number of new j-type machines purchased in the m-th year, pcs. ; (6)the number of purchased machines of the j-th type of the new modification used on the i-th operation on the k-th day (agricultural term (6)); 7 (6, )the number of j-machines of age t years, sold to the side in the m-th year at their residual (liquidation) value 8 (6, ); (6, )the number of machines of the j-th type (old modification) of age t years remaining in operation in the m-th year used on the i-th operation on the k-th day.…”

Section: A Dynamic Model Of the Perspective Replacement Of The Mtf (System Approach)mentioning

confidence: 99%

Modelling the process of technical re-equipment of the MTF with the simultaneous selection of the optimal plan for its operation on the farm

Alukhanyan

Panfilova

2020

E3S Web Conf.

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The article deals with the issues of renovation (renewal) of the agricultural machinery and tractor fleet by developing models for developing an optimal strategy for its technical re-equipment. The optimal service life of machines in operation is considered as the most important prerequisite for the scientific management of such an update. The main criterion for the optimal strategy is the effectiveness of replacing a particular machine with a new one. The relevance of solving this problem is determined by the urgent need to plan the pace of technical re-equipment of the MTF, taking into account the presence of various economic restrictions.

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To the problem of dynamic modeling and management in an integrated environment of the industrial cluster

Cited by 14 publications

References 2 publications

Mathematical modeling in economics for selection of optimum investment solution

Mathematical modeling in economics for selection of optimum investment solution

Cluster Competitiveness Modeling: An Approach with Systems Dynamics

Modelling the process of technical re-equipment of the MTF with the simultaneous selection of the optimal plan for its operation on the farm

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