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

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.

Automated summary

This article proposes a new utility-aware cloud manufacturing multi-task scheduling model, incorporating the utilities of both customers and manufacturers. An improved non-dominated sorting genetic algorithm-II is used to find the approximate optimal Pareto solution set, which is then ranked using game theory to recommend the optimal solution to the cloud manufacturing system. Simulation experiments confirm the effectiveness of the proposed algorithm compared to three baseline multi-objective evolutionary algorithms.