Abstract
Within the emerging industrial sustainability domain, production efficiency interventions are gaining practical interest since manufacturing plants are facing increasing pressure to reduce their carbon footprint, driven by concerns related to energy costs and climate changes. This work focuses on the challenging issue of energy aware production scheduling and rescheduling systems (EAPSRS). The proposed multi-agent architecture (MA-EAPSRS) is hybrid, combining the predictive and the reactive phase while taking into account sustainability in both parts. It is composed of two cooperating multi-agent systems: the first one represents the smart manufacturing plant and the second one is the smart energy supply plant. It is based on interactions and negotiations between factory schedulers and energy providers. Uncertainties in term of machine’s disruptions and variation of processing time and in term of energy availability are also considered. In order to assess the proposed approach, an illustrative case study addressing the problem is presented and discussed.
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References
Baykasoglu, A.: Linguistic-based meta-heuristic optimization model for flexible job shop scheduling. Int. J. Prod. Res. 40(17), 4523–4543 (2002)
Bilge, P., Badurdeen, F., Seliger, G., Jawahir, I.: A novel manufacturing architecture for sustainable value creation. CIRP Ann. 65(1), 455–458 (2016)
Giret, A., Trentesaux, D., Salido, M.A., Garcia, E., Adam, E.: A holonic multi-agent methodology to design sustainable intelligent manufacturing control systems. J. Clean. Prod. 167(C), 1370–1386 (2017)
Gonzalez, M., Oddi, A., Rasconi, R.: Multi-objective optimization in a job shop with energy costs through hybrid evolutionary techniques. In: Twenty-Seventh International Conference on Automated Planning and Scheduling, USA, 18–23 June 2017, pp. 140–148 (2017)
He, Y., Li, Y., Wu, T., Sutherland, J.W.: An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops. J. Clean. Prod. 87, 245–254 (2015)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Australia, 27 November–1 December 1995, pp. 1942–1948 (1995)
Liao, L.M., Huang, C.J.: A multi-agent based rescheduling framework for mixed-model assembly line balancing. In: IEEE International Conference on Industrial Engineering and Engineering Management, China, 10–13 December 2012, pp. 474–478 (2012)
May, G., Barletta, I., Stahl, B., Taisch, M.: Energy management in production: a novel method to develop key performance indicators for improving energy efficiency. Appl. Energy 149, 46–61 (2015)
May, G., Stahl, B., Taisch, M.: Energy management in manufacturing: toward eco-factories of the future a focus group study. Appl. Energy 164, 628–638 (2016)
Nouiri, M., Bekrar, A., Jemai, A., Niar, S., Ammari, A.C.: An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J. Intell. Manuf. 29(3), 603–615 (2018)
Nouiri, M., Bekrar, A., Trentesaux, D.: Towards energy efficient scheduling and rescheduling for dynamic flexible job shop problem. In: Proceedings of the IFAC Symposium on Information Control Problems in Manufacturing, Bergamo, Italy, 11–13 June 2018, vol. 51, no. 11, pp. 1275–1280 (2018)
Nouiri, M., Jemai, A., Ammari, A.C., Bekrar, A., Niar, S.: An effective particle swarm optimization algorithm for flexible job-shop scheduling problem. In: IEEE International Conference on Industrial Engineering and Systems Management, Morocco, 28–30 October 2013, pp. 1–6 (2013)
Paolucci, M., Anghinol, D., Tonelli, F.: Facing energy-aware scheduling: a multi-objective extension of a scheduling support system for improving energy efficiency in a moulding industry. Soft Comput. 21, 3687–3698 (2017)
Plitsos, S., Repoussis, P.P., Mourtos, I., Tarantilis, C.D.: Energy-aware decision support for production scheduling. Decis. Support Syst. 93, 88–97 (2017)
Raileanu, S., Anton, F., Iatan, A., Borangiu, T., Anton, S., Morariu, O.: Resource scheduling based on energy consumption for sustainable manufacturing. J. Intell. Manuf. 28(7), 1519–1530 (2017)
Salido, M.A., Joan, E., Federico, B., Giret, A.: Rescheduling in job-shop problems for sustainable manufacturing systems. J. Clean. Prod. 162(20), S121–S132 (2016)
Tonelli, F., Bruzzone, A., Paolucci, M., Carpanzano, E., Nicolo, G., Giret, A., Salido, M., Trentesaux, D.: Assessment of mathematical programming and agent-based modelling for offline scheduling: application to energy aware manufacturing. CIRP Ann. Manuf. Technol. 65(1), 405–408 (2016)
Trentesaux, D., Giret, A., Tonelli, F., Skobelev, P.: Emerging key requirements for future energy-aware production scheduling systems: a multi-agent and holonic perspective. In: Service Orientation in Holonic and Multi-Agent Manufacturing. Studies in Computational Intelligence, vol. 694, pp. 127–141 (2016)
Zhang, L., Li, X., Gao, L., Zhang, G.: Dynamic rescheduling in FMS that is simultaneously considering energy consumption and schedule efficiency. Int. J. Adv. Manuf. Technol. 87(5–8), 1387–1399 (2013)
Acknowledgements
The ELSAT2020 project is co-financed by the European Union with the European Regional Development Fund, the French state and the Hauts de France Region Council. This work was also partially funded by the Spanish research projects TIN2016-80856-R and TIN2015-65515-C4-1-R.
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Nouiri, M., Trentesaux, D., Bekrar, A., Giret, A., Salido, M.A. (2019). Cooperation Between Smart Manufacturing Scheduling Systems and Energy Providers: A Multi-agent Perspective. In: Borangiu, T., Trentesaux, D., Thomas, A., Cavalieri, S. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing. SOHOMA 2018. Studies in Computational Intelligence, vol 803. Springer, Cham. https://doi.org/10.1007/978-3-030-03003-2_15
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