Industrial cluster evolution based on path dependence combining with pso algorithm
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- Category: Information technologies, systems analysis and administration
- Last Updated on 02 October 2016
- Published on 02 October 2016
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Authors:
D.Krivosheev, School of Management, Harbin Institute of Technology, Harbin, Heilongjiang, China
Jiang Minghui, School of Management, Harbin Institute of Technology, Harbin, Heilongjiang, China
Abstract:
Purpose. Specific to the existing defects of the industrial cluster research studies at home and abroad, the paper analyzed the evolutionary process based on the PSO algorithm.
Methodology. An industrial cluster evolution mold was established based on path dependence combining with PSO algorithm. A simulation analysis was performed on the mold algorithm established in the paper.
Findings. The results have revealed that the mold presented in the paper can be applied for quantitative and qualitative analysis of the evolvement rule of the industrial cluster. In addition, it can be seen from the case simulation results that with appropriate governmental macro regulation and control, as well as equilibrium state of competition and cooperation between enterprises, the eventual industrial cluster will mainly tend to be an enterprise model maintaining the existing market share and expanding production. When the governmental macro regulation and control is over-sized or excessively small, or the competition and cooperation between enterprises are under non-equilibrium state, the final industrial cluster will tend to be an enterprise model to develop new products and new markets.
Originality. While studying the evolutionary process of the industrial cluster, the existing research studies still remain the qualitative discussion stage for the production process and evolution rules of the industrial cluster. The research studies are not sufficiently profound and can hardly describe the dynamic effects of the industrial cluster quantitatively. Consequently, the paper correlates the PSO algorithm and the self-organizing characteristics of the industrial cluster. Combined with the theory of path dependence, it successfully simulated the evolution of the industrial cluster by adopting the PSO algorithm.
Practical value. The simulation analysis result of the paper can effectively analyze the evolutionary process of the industrial cluster. It can qualitatively simulate the evolution characteristics and rules of the industrial cluster. Furthermore, it can provide a new analysis thought for the academic research of the industrial cluster.
References/Список літератури
1. Horváth, G á. and Harazin, P., 2015. A framework for an industrial ecological decision support system to foster partnerships between businesses and governments for sustainable development. Journal of Cleaner Production, Vol. 2015, No. 114, pp. 214–223.
2. Morandin, M., Hackl, R. and Harvey, S., 2014. Economic feasibility of district heating delivery from industrial excess heat: A case study of a Swedish petrochemical cluster. Energy, Vol. 65, No.1, pp. 209–220.
3. Hall, J., 2014. Innovation and entrepreneurial dynamics in the Base of the Pyramid. Technovation, Vol. 34, No. 5–6, pp. 265–269.
4. Wang, Y., Li, J. and Ning, L, 2014. Dynamic patterns of technology collaboration: A case study of the Chinese automobile industry. Scientometrics, Vol. 101, No. 1, pp. 663–683.
5. Kim, H.D., Lee, D.H. and Choe, H., 2014. The evolution of cluster network structure and firm growth: a study of industrial software clusters. Scientometrics, Vol. 99, No. 1, pp. 77–95.
6. Liu, M.C., 2015. Manufacturing servitization and revitalizing industrial clusters: a case study of Taiwan’s LIIEP. Journal of the Asia Pacific Economy, Vol. 20, No. 3, pp. 423–443.
7. Yang J, He L, Fu S., 2014. An improved PSO-based charging strategy of electric vehicles in electrical distribution grid. Applied Energy, Vol. 128, No. 3, pp. 82–92.
8. Mahmoodabadi, M.J., Mottaghi, Z.S. and Bagheri, A., 2014. HEPSO: High exploration particle swarm optimization. Information Sciences, Vol. 273, No. 18, pp. 101–111.
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