Scheduling in cloud manufacturing systems: Recent systematic literature review

Agustín Halty; Rodrigo Sánchez; Valentín Vázquez; Víctor Viana; Pedro Piñeyro; Daniel Alejandro Rossit; Agustín Halty; Rodrigo Sánchez; Valentín Vázquez; Víctor Viana; Pedro Piñeyro; Daniel Alejandro Rossit

doi:10.3934/mbe.2020377

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 7378-7397. doi: 10.3934/mbe.2020377

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Scheduling in cloud manufacturing systems: Recent systematic literature review

1.
Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, Montevideo, Uruguay
2.
Departamento de Ingeniería, Universidad Nacional del Sur and INMABB-CONICET, Av. Alem 1253, Bahía Blanca, Argentina

Received: 31 August 2020 Accepted: 08 October 2020 Published: 28 October 2020

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.
- cloud manufacturing,
- scheduling,
- literature review,
- Industry 4.0,
- Internet of Things,
- cyber-physical systems,
- optimization,
- multi-objective,
- cloud computing
Citation: Agustín Halty, Rodrigo Sánchez, Valentín Vázquez, Víctor Viana, Pedro Piñeyro, Daniel Alejandro Rossit. Scheduling in cloud manufacturing systems: Recent systematic literature review[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 7378-7397. doi: 10.3934/mbe.2020377

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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.

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