Open Access

ARTICLE

The Cloud Manufacturing Resource Scheduling Optimization Method Based on Game Theory

Xiaoxuan Yang^*, Zhou Fang

Heilongjiang Province Cyberspace Research Center, Heilongjiang Province Harbin, 150090, China

* Corresponding Author: Xiaoxuan Yang. Email:

Journal on Artificial Intelligence 2022, 4(4), 229-243. https://doi.org/10.32604/jai.2022.035368

Received 18 August 2022; Accepted 20 October 2022; Issue published 25 May 2023

Abstract

In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment, this paper proposes to use load balancing, service cost and service quality as optimization goals for resource scheduling, however, resource providers have resource utilization requirements for cloud manufacturing platforms. In the process of resource optimization scheduling, the interests of all parties have conflicts of interest, which makes it impossible to obtain better optimization results for resource scheduling. Therefore, a multithreaded auto-negotiation method based on the Stackelberg game is proposed to resolve conflicts of interest in the process of resource scheduling. The cloud manufacturing platform first calculates the expected value reduction plan for each round of global optimization, using the negotiation algorithm based on the Stackelberg game, the cloud manufacturing platform negotiates and mediates with the participants’ agents, to maximize self-interest by constantly changing one’s own plan, iteratively find multiple sets of locally optimized negotiation plans and return to the cloud manufacturing platform. Through multiple rounds of negotiation and calculation, we finally get a target expected value reduction plan that takes into account the benefits of the resource provider and the overall benefits of the completion of the manufacturing task. Finally, through experimental simulation and comparative analysis, the validity and rationality of the model are verified.

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