Towards decentralised job shop scheduling as a web service

References

• Abar, S., Theodoropoulos, G. K., Lemarinier, P., & O’Hare, G. M. (2017). Agent based modelling and simulation tools: A review of the state-of-art software. Computer Science Review, 24, 13–26. https://doi.org/10.1016/j.cosrev.2017.03.001
   Web of Science ®Google Scholar
• Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J., & Vrgoč, D. (2017). Foundations of modern query languages for graph databases. ACM Computing Surveys (CSUR), 50(5), 68. https://doi.org/10.1145/3104031
   Web of Science ®Google Scholar
• Aureli, S., & Del Baldo, M. (2016). Formal inter-firm cooperation and international expansion: How Italian SMEs are using the network contract. In The Changing Global Economy and its Impact on International Entrepreneurship. Edward Elgar Publishing. (pp. 157).
   Google Scholar
• Baffo, I., Confessore, G., & Stecca, G. (2013). A decentralized model for flow shop production with flexible transportation system. Journal of Manufacturing Systems, 32(1), 68–77. https://doi.org/10.1016/j.jmsy.2012.10.002
   Web of Science ®Google Scholar
• Bölöni, L., & Turgut, D. (2017). Value of information based scheduling of cloud computing resources. Future Generation Computer Systems, 71, 212–220. https://doi.org/10.1016/j.future.2016.10.024
   Web of Science ®Google Scholar
• Chen, G., Jiang, T., Wang, M., Tang, X., & Ji, W. (2020). Modeling and reasoning of IoT architecture in semantic ontology dimension. Computer Communications, 153, 580–594. https://doi.org/10.1016/j.comcom.2020.02.006
   Web of Science ®Google Scholar
• Egger, G., Chaltsev, D., Giusti, A., & Matt, D. T. (2020). A deployment-friendly decentralized scheduling approach for cooperative multi-agent systems in production systems. Procedia Manufacturing, 52, 127–132. https://doi.org/10.1016/j.promfg.2020.11.023
   Google Scholar
• Eleftheriadis, M. S. R. J., & Myklebust, O. (2016). A guideline of quality steps towards zero defect manufacturing in industry. In Proc. Int. Conf. Ind. Eng. Oper. Manag. IEOM Society International. (pp. 332–340).
   Google Scholar
• Fuchigami, H. Y., & Rangel, S. (2018). A survey of case studies in production scheduling: Analysis and perspectives. Journal of Computational Science, 25, 425–436. https://doi.org/10.1016/j.jocs.2017.06.004
   Web of Science ®Google Scholar
• Ghadimi, P., Wang, C., Lim, M. K., & Heavey, C. (2019). Intelligent sustainable supplier selection using multi-agent technology: Theory and application for Industry 4.0 supply chains. Computers & Industrial Engineering, 127, 588–600. https://doi.org/10.1016/j.cie.2018.10.050
   Web of Science ®Google Scholar
• Ghiyasinasab, M., Lehoux, N., Ménard, S., & Cloutier, C. (2020). Production planning and project scheduling for engineer-to-order systems-case study for engineered wood production. International Journal of Production Research, 59(4), 1–20. https://doi.org/10.1080/00207543.2020.1717009
   Web of Science ®Google Scholar
• Giordani, S., Lujak, M., & Martinelli, F. (2013). A distributed multi-agent production planning and scheduling framework for mobile robots. Computers & Industrial Engineering, 64(1), 19–30. https://doi.org/10.1016/j.cie.2012.09.004
   Web of Science ®Google Scholar
• Guo, Z. X., Ngai, E. W. T., Yang, C., & Liang, X. (2015). An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment. International Journal of Production Economics, 159, 16–28. https://doi.org/10.1016/j.ijpe.2014.09.004
   Web of Science ®Google Scholar
• Harjunkoski, I., Maravelias, C. T., Bongers, P., Castro, P. M., Engell, S., Grossmann, I. E., Hooker, J., Méndez, C., Sand, G., & Wassick, J. (2014). Scope for industrial applications of production scheduling models and solution methods. Computers & Chemical Engineering, 62, 161–193. https://doi.org/10.1016/j.compchemeng.2013.12.001
   Web of Science ®Google Scholar
• Helo, P., Hao, Y., Toshev, R., & Boldosova, V. (2021). Cloud manufacturing ecosystem analysis and design. Robotics and Computer-Integrated Manufacturing, 67, 102050. https://doi.org/10.1016/j.rcim.2020.102050
   Web of Science ®Google Scholar
• Jules, G., & Saadat, M. (2017). Agent cooperation mechanism for decentralized manufacturing scheduling. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(12), 3351–3362. https://doi.org/10.1109/TSMC.2016.2578879
   Web of Science ®Google Scholar
• Jules, G. D., Saadat, M., & Saeidlou, S. (2015). Holonic ontology and interaction protocol for manufacturing network organization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(5), 819–830. https://doi.org/10.1109/TSMC.2014.2387099
   Web of Science ®Google Scholar
• Kaiser, R., Weiss, W., Falelakis, M., Michalakopoulos, S., & Ursu, M. F. (2012, July). A rule-based virtual director enhancing group communication. In Multimedia and Expo Workshops (ICMEW), 2012 IEEE International Conference on (pp. 187–192). Melbourne, VIC, Australia: IEEE.
   Google Scholar
• Kolus, A., El-Khalifa, A., Al-Turki, U. M., & Duffuaa, S. O. (2020). An integrated mathematical model for production scheduling and preventive maintenance planning. International Journal of Quality & Reliability Management, 37(6/7), 925–937. https://doi.org/10.1108/IJQRM-10-2019-0335
   Web of Science ®Google Scholar
• Leitão, P., Mařík, V., & Vrba, P. (2013). Past, present, and future of industrial agent applications. IEEE Transactions on Industrial Informatics, 9(4), 2360–2372. https://doi.org/10.1109/TII.2012.2222034
   Web of Science ®Google Scholar
• Li, H., Wu, Y., & Chen, M. (2020). Adaptive fault-tolerant tracking control for discrete-time multiagent systems via reinforcement learning algorithm. IEEE Transactions on Cybernetics. 51(3), 1163 - 1174. https://doi.org/10.1109/TCYB.2020.2982168
   Web of Science ®Google Scholar
• Lin, L., & Gen, M. (2018). Hybrid evolutionary optimisation with learning for production scheduling: State-of-the-art survey on algorithms and applications. International Journal of Production Research, 56(1-2), 193-223. https://doi.org/10.1080/00207543.2018.1437288
   Google Scholar
• Lu, Y., & Xu, X. (2017). A semantic web-based framework for service composition in a cloud manufacturing environment. Journal of Manufacturing Systems, 42, 69–81. https://doi.org/10.1016/j.jmsy.2016.11.004
   Web of Science ®Google Scholar
• Misener, R., & Floudas, C. A. (2014). ANTIGONE: Algorithms for continuous/integer global optimization of nonlinear equations. Journal of Global Optimization, 59(2–3), 503–526. https://doi.org/10.1007/s10898-014-0166-2
   Web of Science ®Google Scholar
• Morgan, J., & O’Donnell, G. E. (2017). Enabling a ubiquitous and cloud manufacturing foundation with field-level service-oriented architecture. International Journal of Computer Integrated Manufacturing, 30(4–5), 442–458. https://doi.org/10.1080/0951192X.2015.1032355
   Web of Science ®Google Scholar
• Mourtzis, D., Doukas, M., & Giannoulis, C. (2016). An inference-based knowledge reuse framework for historical product and production information retrieval. Procedia CIRP, 41, 472–477. https://doi.org/10.1016/j.procir.2015.12.026
   Google Scholar
• Muth, J. F., & Thompson, G. L. (Eds.). (1963). Industrial scheduling. Prentice-Hall.
   Google Scholar
• Nedic, A., & Ozdaglar, A. (2009). Distributed subgradient methods for multi-agent optimization. IEEE Transactions on Automatic Control, 54(1), 48–61. https://doi.org/10.1109/TAC.2008.2009515
   Web of Science ®Google Scholar
• Pérez-Urbina, H., Rodrıguez-Dıaz, E., Grove, M., Konstantinidis, G., & Sirin, E. (2012). Evaluation of query rewriting approaches for OWL 2. In Proc. of the Joint Workshop on Scalable and High-Performance Semantic Web Systems (SSWS+ HPCSW 2012) (Vol. 943). Boston, USA.
   Google Scholar
• Prodan, R., Wieczorek, M., & Fard, H. M. (2011). Double auction-based scheduling of scientific applications in distributed grid and cloud environments. Journal of Grid Computing, 9(4), 531–548. https://doi.org/10.1007/s10723-011-9196-x
   Web of Science ®Google Scholar
• Ren, L., Zhang, L., Wang, L., Tao, F., & Chai, X. (2017). Cloud manufacturing: Key characteristics and applications. International Journal of Computer Integrated Manufacturing, 30(6), 501–515. https://doi.org/10.1080/0951192X.2014.902105
   Web of Science ®Google Scholar
• Rodriguez, M. A., Montagna, J. M., Vecchietti, A., & Corsano, G. (2017). Generalized disjunctive programming model for the multi-period production planning optimization: An application in a polyurethane foam manufacturing plant. Computers & Chemical Engineering, 103, 69–80. https://doi.org/10.1016/j.compchemeng.2017.03.006
   Web of Science ®Google Scholar
• Roy, B., & Sussmann, B. (1964). Les problemes d’ordonnancement avec contraintes disjonctives. In Note ds (pp. 9). SEMA.
   Google Scholar
• Ruiz, R., Pan, Q. K., & Naderi, B. (2019). Iterated greedy methods for the distributed permutation flowshop scheduling problem. Omega, 83, 213–222. https://doi.org/10.1016/j.omega.2018.03.004
   Web of Science ®Google Scholar
• Saeidlou, S., Saadat, M., & Jules, G. D. (2014, October). Cloud manufacturing analysis based on ontology mapping and multi agent systems. In 2014 IEEE International Conference on Systems, Man, and Cybernetics (Vol.10, pp. 5–8). San Diego, CA, USA.
   Google Scholar
• Saeidlou, S., Saadat, M., Jules, G. D., & Lou, P. (2019a). Knowledge and agent-based system for decentralised scheduling in manufacturing. Cogent Engineering, 6(1), 1582309. https://doi.org/10.1080/23311916.2019.1582309
   Web of Science ®Google Scholar
• Saeidlou, S., Saadat, M., Sharifi, E. A., Jules, G. D., & Peng, T. (2019b). Agent-based distributed manufacturing scheduling: An ontological approach. Cogent Engineering, 6(1), 1565630. https://doi.org/10.1080/23311916.2019.1565630
   Web of Science ®Google Scholar
• Shen, W., Hao, Q., Wang, S., Li, Y., & Ghenniwa, H. (2007). An agent-based service-oriented integration architecture for collaborative intelligent manufacturing. Robotics and Computer-Integrated Manufacturing, 23(3), 315–325. https://doi.org/10.1016/j.rcim.2006.02.009
   Web of Science ®Google Scholar
• Shen, W., Li, Y., Hao, Q., Wang, S., & Ghenniwa, H. (2005, September). Implementing collaborative manufacturing with intelligent web services. In Computer and Information Technology, 2005. CIT 2005. The Fifth International Conference on (pp. 1063–1069). Shanghai, China: IEEE.
   Google Scholar
• Sokolov, B., Ivanov, D., & Dolgui, A. (2020). Scheduling in industry 4.0 and cloud manufacturing. Springer.
   Google Scholar
• Tao, F., Zhang, L., Venkatesh, V. C., Luo, Y., & Cheng, Y. (2011). Cloud manufacturing: A computing and service-oriented manufacturing model. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(10), 1969–1976. https://doi.org/10.1177/0954405411405575
   Web of Science ®Google Scholar
• Wagner, W. P. (2017). Trends in expert system development: A longitudinal content analysis of over thirty years of expert system case studies. Expert Systems with Applications, 76, 85–96. https://doi.org/10.1016/j.eswa.2017.01.028
   Web of Science ®Google Scholar
• Wang, M., Wang, H., Vogel, D., Kumar, K., & Chiu, D. K. (2009). Agent-based negotiation and decision making for dynamic supply chain formation. Engineering Applications of Artificial Intelligence, 22(7), 1046–1055. https://doi.org/10.1016/j.engappai.2008.09.001
   Web of Science ®Google Scholar
• Wang, T., Guo, S., & Lee, C. G. (2014). Manufacturing task semantic modeling and description in cloud manufacturing system. The International Journal of Advanced Manufacturing Technology, 71(9–12), 2017–2031. https://doi.org/10.1007/s00170-014–5607-z
   Web of Science ®Google Scholar
• Yen, G. G., & Ivers, B. (2009). Job shop scheduling optimization through multiple independent particle swarms. International Journal of Intelligent Computing and Cybernetics, 2(1), 5–33. https://doi.org/10.1108/17563780910939237
   Google Scholar
• Zhang, H., & Roy, U. (2018). A semantics-based dispatching rule selection approach for job shop scheduling. Journal of Intelligent Manufacturing, 30, 2759–2779. https://doi.org/10.1007/s10845-018-1421-z
   Google Scholar
• Zhao, C., Luo, X., & Zhang, L. (2020). Modeling of service agents for simulation in cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 64, 101910. https://doi.org/10.1016/j.rcim.2019.101910
   Web of Science ®Google Scholar