Version 1
: Received: 18 August 2020 / Approved: 19 August 2020 / Online: 19 August 2020 (10:26:06 CEST)
How to cite:
Zhou, Y.; Li, Y.; Zhang, Y. A Negotiation Pricing Model for Innovation Services Based on the Multiobjective Genetic Algorithm. Preprints2020, 2020080409. https://doi.org/10.20944/preprints202008.0409.v1
Zhou, Y.; Li, Y.; Zhang, Y. A Negotiation Pricing Model for Innovation Services Based on the Multiobjective Genetic Algorithm. Preprints 2020, 2020080409. https://doi.org/10.20944/preprints202008.0409.v1
Zhou, Y.; Li, Y.; Zhang, Y. A Negotiation Pricing Model for Innovation Services Based on the Multiobjective Genetic Algorithm. Preprints2020, 2020080409. https://doi.org/10.20944/preprints202008.0409.v1
APA Style
Zhou, Y., Li, Y., & Zhang, Y. (2020). A Negotiation Pricing Model for Innovation Services Based on the Multiobjective Genetic Algorithm. Preprints. https://doi.org/10.20944/preprints202008.0409.v1
Chicago/Turabian Style
Zhou, Y., Yue Li and Yunxing Zhang. 2020 "A Negotiation Pricing Model for Innovation Services Based on the Multiobjective Genetic Algorithm" Preprints. https://doi.org/10.20944/preprints202008.0409.v1
Abstract
Service pricing is a bottleneck in the development of innovation services, as it is the issue of most concern between the suppliers and demanders. In this paper, a negotiation pricing model that is based on the multiobjective genetic algorithm is developed for innovation service pricing. Regarding the service pricing process as a multiobjective problem, the objective functions, which include the service price, service efficiency, and service quality, for suppliers and demanders are constructed. As the solution of a multiobjective problem is typically a series of alternatives, another negotiation process is necessary for determining the final decision. A learning strategy is adopted during the negotiation process to simulate reality. Finally, the model is implemented for an innovation service transaction, the objective of which is to identify the optimal price plan. The results demonstrate that the model can provide quantitative decision support for the pricing of an innovation service and ultimately yield a win-win result for both the supplier and demander of the innovation service. Furthermore, the influence of the parameters during the negotiation process is analyzed in detail. The effects of the learning strategy on accelerating the negotiation process, as well as the chosen of reasonable parameters are given.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
