Abstract
Knowledge generation and diffusion in the modern digital economy as well as innovation process implying novelty technologies, products and services promotion on the market are considered. Production function included R&D or knowledge term regarded as moving force in the self-organizing process of network alliances composition. The model of the networks alliances composition based on the knowledge profile of the firms and measures their similarity or dissimilarity and quadratic programming with binary variables is proposed. Results of the modeling with genetic programming algorithm for partner selection are presented. In paper, we used quadratic methods of programming method as possible way for partner selection. Genetic algorithm and multi-valued logic (Lukasiewicz logic) were applied for these aims. The results of genetic algorithm are discussed in conclusion as possible way for including increment of production function due to new partner’s attraction.
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Notes
- 1.
This research was conducted in the framework of the basic part of the scientific research state task in the field of scientific activity of the Ministry of science and education of the Russian Federation, project no. 2.9577.2017.
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Novototskih, D., Romanov, V. (2017). Simulation of Alliance Networks Composition in Knowledge Economy. In: Pergl, R., Lock, R., Babkin, E., Molhanec, M. (eds) Enterprise and Organizational Modeling and Simulation. EOMAS 2017. Lecture Notes in Business Information Processing, vol 298. Springer, Cham. https://doi.org/10.1007/978-3-319-68185-6_1
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DOI: https://doi.org/10.1007/978-3-319-68185-6_1
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