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Rapid Control of Government Economic Environment Management Cost Based on Balanced Score Card
EMC (economic management cost) of the government has become a hot topic of concern from all walks of life. Controlling and reducing government EMC is the requirement for building a conservation-oriented society and deepening the reform of public budget. Dynamic cost accounting calculates the cost of...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363228/ https://www.ncbi.nlm.nih.gov/pubmed/35958389 http://dx.doi.org/10.1155/2022/8506676 |
Sumario: | EMC (economic management cost) of the government has become a hot topic of concern from all walks of life. Controlling and reducing government EMC is the requirement for building a conservation-oriented society and deepening the reform of public budget. Dynamic cost accounting calculates the cost of cost objects through the confirmation, measurement, and distribution of production costs. In this article, it is innovatively proposed to integrate BSC (balanced score card) into EMC control, and analyze the logical relationship between system objectives and functions by using the mathematical model. Based on GA (genetic algorithm) cost optimization control concept and specific control ideas, individuals who meet the evolutionary characteristics are stored in the crossover database and participate in crossover operation as parents when crossing. In order to improve the local optimization ability of the algorithm, parallel mutation operation mechanism is introduced, which can execute multiple mutation rules at the same time. The results show that the average convergence time of this algorithm is 0.186s and the variance of population fitness is 288.19. The conclusion shows that the algorithm proposed in this article can overcome the problems of slow convergence speed, low accuracy, and local convergence of GA, and effectively improve the overall performance of the algorithm. |
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