A utility-aware multi-task scheduling method in cloud manufacturing using extended NSGA-II embedded with game theory

ABSTRACT

As an emerging sharing and collaborative paradigm, the cloud manufacturing system should maximize the satisfaction of stakeholders to promote the long-term development of the system. This article proposes a new utility-aware cloud manufacturing multi-task scheduling model, which considers the utilities of both customers and manufacturers. To solve the proposed model, an extended non-dominated sorting genetic algorithm-II with three improvements is presented to find the approximate optimal Pareto solution set. Then, these non-dominated solutions are ranked by means of game theory, and the resulting optimal solution is recommended to the cloud manufacturing system. Simulation experiments are conducted to verify the effectiveness of the proposed algorithm by comparing it with three baseline multi-objective evolutionary algorithms.

KEYWORDS:

• cloud manufacturing
• multi-task scheduling
• utility-aware
• game theory
• non-dominated sorting genetic algorithm-II

Acknowledgments

This research was supported by the National Natural Science Foundation of China (No. 51875503, No. 51975512, and No.61973267), and Zhejiang Natural Science Foundation of China (No. LZ20E050001) and Zhejiang Key R & D Project of China (No.2021C03153).

Compliance with ethical standards

Conflicts of interest: The authors declare that there is no conflict of interests regarding the publication of this article.Ethical standard: The authors state that this research complies with ethical standards. This research does not involve either human participants or animals.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supporting information

All experimental data for this research have been public in the Figshare database (https://doi.org/10.6084/m9.figshare.12585437).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [51875503,51975512,61973267]; Natural Science Foundation of Zhejiang Province [LZ20E050001]; Key R & D Project of Zhejiang Province [2021C03153].