Cloud manufacturing is an emerging paradigm of global manufacturing networks. Through centralized management and operation of distributed manufacturing services, it can deal with different requirement tasks submitted by multiple customers in parallel. Therefore, the cloud manufacturing multi-task scheduling problem has attracted increasing attention from researchers. This article proposes a new cloud manufacturing multi-task scheduling model based on game theory from the customer perspective. The optimal result for a cloud manufacturing platform is derived from the Nash equilibrium point in the game. As the cloud manufacturing multi-task scheduling problem is known as an NP-hard combinatorial optimization problem, an extended biogeography-based optimization algorithm that embeds three improvements is presented to solve the corresponding model. Compared with the basic biogeography-based optimization algorithm, genetic algorithm, and particle swarm optimization, the experimental simulation results demonstrate that the extended biogeography-based optimization algorithm finds a better schedule for the proposed model. Its benefit is to provide each customer with reliable services that fulfill the demanded manufacturing tasks at reasonable cost and time.

中文翻译：

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使用扩展的基于生物地理学的优化算法在云制造中基于博弈论的多任务调度

云制造是全球制造网络的新兴范式。通过分布式制造服务的集中管理和运营，可以并行处理多个客户提交的不同需求任务。因此，云制造多任务调度问题越来越受到研究者的关注。本文从客户角度提出了一种基于博弈论的新型云制造多任务调度模型。云制造平台的最优结果来自博弈中的纳什均衡点。由于云制造多任务调度问题被称为 NP-hard 组合优化问题，因此提出了一种基于生物地理学的扩展优化算法，该算法嵌入了三个改进方法来求解相应的模型。与基于生物地理学的基本优化算法、遗传算法和粒子群优化相比，实验仿真结果表明，扩展的基于生物地理学的优化算法为所提出的模型找到了更好的调度。它的好处是为每个客户提供可靠的服务，以合理的成本和时间完成所需的制造任务。