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- W3096926878 abstract "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." @default.
- W3096926878 created "2020-11-09" @default.
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- W3096926878 date "2020-01-01" @default.
- W3096926878 modified "2023-10-06" @default.
- W3096926878 title "Scheduling in cloud manufacturing systems: Recent systematic literature review" @default.
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- W3096926878 doi "https://doi.org/10.3934/mbe.2020377" @default.
- W3096926878 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33378901" @default.
- W3096926878 hasPublicationYear "2020" @default.
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