Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, Montevideo, Uruguay.
Departamento de Ingeniería, Universidad Nacional del Sur and INMABB-CONICET, Av. Alem 1253, Bahía Blanca, Argentina.
Math Biosci Eng. 2020 Oct 28;17(6):7378-7397. doi: 10.3934/mbe.2020377.
Cloud Manufacturing (CMFg) is a novel production paradigm that benefits from Cloud Computing in order to develop manufacturing systems linked by the cloud. These systems, based on virtual platforms, allow direct linkage between customers and suppliers of manufacturing services, regardless of geographical distance. In this way, CMfg can expand both markets for producers, and suppliers for customers. However, these linkages imply a new challenge for production planning and decision-making process, especially in Scheduling. In this paper, a systematic literature review of articles addressing scheduling in Cloud Manufacturing environments is carried out. The review takes as its starting point a seminal study published in 2019, in which all problem features are described in detail. We pay special attention to the optimization methods and problem-solving strategies that have been suggested in CMfg scheduling. From the review carried out, we can assert that CMfg is a topic of growing interest within the scientific community. We also conclude that the methods based on bio-inspired metaheuristics are by far the most widely used (they represent more than 50% of the articles found). On the other hand, we suggest some lines for future research to further consolidate this field. In particular, we want to highlight the multi-objective approach, since due to the nature of the problem and the production paradigm, the optimization objectives involved are generally in conflict. In addition, decentralized approaches such as those based on game theory are promising lines for future research.
云制造(CMfg)是一种新颖的生产范式,它受益于云计算,以便开发通过云链接的制造系统。这些基于虚拟平台的系统允许制造服务的客户和供应商直接联系,而不受地理位置的限制。通过这种方式,CMfg 可以扩展生产者的市场和客户的供应商。然而,这些联系为生产计划和决策过程带来了新的挑战,尤其是在调度方面。本文对解决云制造环境中的调度问题的文献进行了系统的综述。该综述以 2019 年发表的一篇开创性研究为起点,详细描述了所有问题的特征。我们特别关注在 CMfg 调度中提出的优化方法和问题解决策略。从进行的综述中,我们可以断言 CMfg 是科学界日益关注的一个主题。我们还得出结论,基于生物启发元启发式的方法是迄今为止使用最广泛的方法(它们代表了所发现的文章的 50%以上)。另一方面,我们提出了一些未来研究的方向,以进一步巩固这一领域。特别是,我们希望强调多目标方法,因为由于问题的性质和生产范式,所涉及的优化目标通常存在冲突。此外,基于博弈论的分散方法是未来研究的有前途的方向。