Abstract
Maintenance of systems such as infrastructure, services and equipment on city context requires a complex management process in order to provide quality services, comply with regulations and extend lifespan of urban equipment and infrastructure without neglecting the proper use of resources to execute maintenance jobs. Cities can generate hundreds or thousands of maintenance jobs during a particular time period. These jobs can be generated automatically by equipment or can be reported by citizens/users. This work introduces a scheduling architecture for automatic management of maintenance jobs. The proposal is able to handle preventive and corrective maintenance jobs looking for available human and instrumental resources during the common time period required by the job to be executed. The scheduler uses intelligent strategies to satisfy constraints of each job in order to get a scheduling according to the criterion of the manager of maintenance. A case study is applied to smart city.
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References
Khazraei, K., Deuse, J.: A strategic standpoint on maintenance taxonomy. J. Facil. Manag, 9(2), 96–113 (2011). doi:10.1108/14725961111128452
Javed, K., Gouriveau, R., Zerhouni, N.: State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels. Mech. Syst. Signal Process. 94, 214–236 (2017). ISSN 0888-3270
Kwok, Y.-K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv. 31(4), 406–471 (1999)
Augonnet, C., Thibault, S., Namyst, R., Wacrenier, P.A.: StarPU: a unified platform for task scheduling on heterogeneous multicore architectures. Concurr. Comput. Pract. Exp. 23(2), 187–198 (2011)
Zhang, W., Xie, H., Cao, B., Cheng, A.M.K.: Energy-aware real-time task scheduling for heterogeneous multiprocessors with particle swarm optimization algorithm. Prob. Eng. Math. (2014). doi:10.1155/2014/287475
Zhang, F., Cao, J., Li, K., Khan, S.U., Hwang, K.: Multi-objective scheduling of many tasks in cloud platforms. Future Gener. Comput. Syst. 37, 309–320 (2014). doi:10.1016/j.future.2013.09.006. ISSN 0167-739X
Moradi, E., Fatemi Ghomi, S.M.T., Zandieh, M.: Bi-objective optimization research on integrated fixed time interval preventive maintenance and production for scheduling flexible job-shop problem. Expert Syst. Appl. 38(6), 7169–7178 (2011). doi:10.1016/j.eswa.2010.12.043. ISSN 0957-4174
Lu, H., Niu, R., Liu, J., Zhu, Z.: A chaotic non-dominated sorting genetic algorithm for the multi-objective automatic test task scheduling problem. Appl. Soft Comput. 13(5), 2790–2802 (2013). doi:10.1016/j.asoc.2012.10.001. ISSN 1568-4946
Deb, K., Sindhya, K., Hakanen, J.: Multi-objective optimization. Decis. Sci. Theory Pract., 145–184 (2016). doi:10.1201/9781315183176-4. ISBN: 978-1-4665-6430-5
Liao, W., Zhang, X., Jiang, M.: An optimization model integrated production scheduling and preventive maintenance for group production. In: 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, pp. 936–940 (2016). doi:10.1109/IEEM.2016.7798015
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Valdivieso-Sarabia, R.J., Marín-Alonso, O., Guerrero-Gómez, F.G., Ferrández-Pastor, F.J., Mora-Pascual, J., García-Chamizo, J.M. (2017). Scheduler for Automatic Management of Maintenance Jobs in Large-Size Systems: A Case Study Applied to Smart City. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_6
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DOI: https://doi.org/10.1007/978-3-319-67585-5_6
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