Vol.27, No.1, 2021, pp.191-203, doi:10.32604/iasc.2021.014142
OPEN ACCESS
ARTICLE
A Negotiated Pricing Model for Innovation Services Based on the Multiobjective Genetic Algorithm
  • Yan Zhou1,*, Yue Li1, Yunxing Zhang2
1 Harbin University of Science and Technology, Harbin, 150080, China
2 Harbin Engineering University, Harbin, 150001, China
* Corresponding Author: Yan Zhou. Email:
Received 01 September 2020; Accepted 25 September 2020; Issue published 07 January 2021
Abstract
Service pricing is a bottleneck in the development of innovation services, as it is the issue of most concern to the suppliers and demanders. In this paper, a negotiated pricing model that is based on the multiobjective genetic algorithm is developed for innovation services. Regarding the process of service pricing as a multiobjective problem, the objective functions which include the service price, service efficiency, and service quality for the suppliers and the demanders are constructed. Because the solution of a multiobjective problem is typically a series of alternatives, an additional negotiation process is necessary in determining the final decision. A learning strategy is adopted during the negotiation process to simulate reality. Finally, the model is implemented in an innovation service transaction, the objective of which is to select the optimal price plan. The results demonstrate that the model is able to provide quantitative decision support for the pricing of an innovation service with a win-win result obtained. Furthermore, the influence of the parameters during the negotiation process is analyzed in detail. The effects of the learning strategy in accelerating the negotiation process and the set of reasonable parameters are discussed.
Keywords
Innovation service; negotiated pricing model; multiobjective problem; genetic algorithm; learning strategy
Cite This Article
Y. Zhou, Y. Li and Y. Zhang, "A negotiated pricing model for innovation services based on the multiobjective genetic algorithm," Intelligent Automation & Soft Computing, vol. 27, no.1, pp. 191–203, 2021.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.