Abstract
Energy-efficient scheduling has become a new trend in industry and academia, mainly due to extreme weather conditions, stricter environmental regulations, and volatile energy prices. This work addresses the energy-efficient Job shop Scheduling Problem with speed adjustable machines. Thus, in addition to determining the sequence of the operations for each machine, one also needs to decide on the processing speed of each operation. We propose a multi-population biased random key genetic algorithm that finds effective solutions to the problem efficiently and outperforms the state-of-the-art solution approaches.
This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bansch, K., et al.: Energy-aware decision support models in production environments: a systematic literature review. Comput. Ind. Eng. 159, 107456 (2021)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evolut. Comput. 6(2), 182–197 (2002)
Fernandes, J.M., Homayouni, S.M., Fontes, D.B.M.M.: Energy-efficient scheduling in job shop manufacturing systems: a literature review. Sustainability 14(10), 6264 (2022)
Gonçalves, J.F., Resende, M.G.: A parallel multi-population biased random-key genetic algorithm for a container loading problem. Comput. Oper. Res. 39(2), 179–190 (2012)
He, Y., Liu, F., Cao, H.J., Li, C.B.: A bi-objective model for job-shop scheduling problem to minimize both energy consumption and makespan. J. Cent. South Univ. Technol. 12(2), 167–171 (2005)
Homayouni, S.M., Fontes, D.B.M.M., Gonçalves, J.F.: A multistart biased random key genetic algorithm for the flexible job shop scheduling problem with transportation. Int. Trans. Oper. Res. 30(2), 688–716 (2023)
Lu, C., Zhang, B., Gao, L., Yi, J., Mou, J.: A knowledge-based multiobjective memetic algorithm for green job shop scheduling with variable machining speeds. IEEE Syst. J. 16(1), 844–855 (2021)
Meng, L., Zhang, C., Shao, X., Ren, Y.: MILP models for energy-aware flexible job shop scheduling problem. J. Clean. Prod. 210, 710–723 (2019)
Salido, M.A., Escamilla, J., Giret, A., Barber, F.: A genetic algorithm for energy-efficiency in job-shop scheduling. Int. J. Adv. Manuf. Technol. 85(5), 1303–1314 (2016)
Tang, D., Dai, M.: Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem. Chin. J. Mech. Eng. 28(5), 1048–1055 (2015). https://doi.org/10.3901/CJME.2015.0617.082
Yin, L., Li, X., Gao, L., Lu, C., Zhang, Z.: Energy-efficient job shop scheduling problem with variable spindle speed using a novel multi-objective algorithm. Adv. Mech. Eng. 9(4), 1–21 (2017)
Zhang, R., Chiong, R.: Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. J. Clean. Prod. 112, 3361–3375 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Homayouni, S.M., Fontes, D.B.M.M., Fontes, F.A.C.C. (2023). A Multi-Population BRKGA for Energy-Efficient Job Shop Scheduling with Speed Adjustable Machines. In: Di Gaspero, L., Festa, P., Nakib, A., Pavone, M. (eds) Metaheuristics. MIC 2022. Lecture Notes in Computer Science, vol 13838. Springer, Cham. https://doi.org/10.1007/978-3-031-26504-4_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-26504-4_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26503-7
Online ISBN: 978-3-031-26504-4
eBook Packages: Computer ScienceComputer Science (R0)