Abstract
This paper proposes an accurate dynamic programming algorithm (EDPA) to solve the single-machine scheduling problem with release time (SMSP_RTs), which targets total carbon emissions. First of all, considering the constraints of each workpiece with different release time and expected completion time, and the different speeds of the machine consume different energy, the length of time the workpiece is processed at different speeds is different, and the problem is established on this basis. Sorting model. The model can be expressed as a ternary method. Then, construct a state recursive equation and design an exact dynamic programming algorithm (EDPA). EDPA has the complexity of pseudo-polynomial time to obtain the optimal solution to the problem. Finally, through simulation experiments, the effectiveness of the proposed EDPA is verified, and the influence of machine speed on energy saving and emission reduction is analyzed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, L., Wang, J., Wu, C.: Research progress in green shop scheduling optimization. Control Decis. (2018). Gong, H., Tang, L., Duin, C.W.: A two-stage flow shop scheduling problem on a batching machine and a discrete machine with blocking and shared setup times. Comput. Oper. Res. 37(5), 960–969 (2010)
Che, A., Lv, K., Levner, E., et al.: Energy consumption minimization for single machine scheduling with bounded maximum tardiness. In: IEEE, International Conference on Networking, Sensing and Control, pp. 146–150. IEEE (2015). Fang, K., Uhan, N.A., Zhao, F., Sutherland, J.W.: Flow shop scheduling with peak power consumption constraints. Ann. Oper. Res. 206(1), 115–145 (2013)
Mouzon, G., Yildirim, M.: A framework to minimise total energy consumption and total tardiness on a single machine. Int. J. Sustain. Eng. 1(2), 105–116 (2008). Liu, C.H., Huang, D.H.: Reduction of power consumption and carbon footprints by applying multi-objective optimisation via genetic algorithms. Int. J. Prod. Res. 52(2), 337–352 (2014)
Wang, Y.C., Wang, M.J., Lin, S.C.: Selection of cutting conditions for power constrained parallel machine scheduling. Robot. Comput. Integr. Manuf. 43, 105–110 (2017)
Bin, H.: Research on parallel machine scheduling considering machine switch. Ind. Eng. Manage. 16(2), 60–64 (2011)
Wu, X., Sun, Y.: Green scheduling problem of flexible job shop with multiple speeds of machine. Comput. Integr. Manuf. Syst. 24(4) (2018)
Ding, J.Y., Song, S., Wu, C.: Carbon-efficient scheduling of flow shops by multi-objective optimization. Eur. J. Oper. Res. 248(3), 758–771 (2015)
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)
Guohui, Z., Shijie, D.: Research on low carbon flexible job shop scheduling problem considering machine speed. J. Comput. Appl. 34(4), 1072–1075 (2017)
Guo, P., Cheng, W., Wang, Y.: Scheduling step-deteriorating jobs to minimise the total weighted tardiness on a single machine. Int. J. Syst. Sci. Oper. Logistics 4(2), 16 (2017)
Çakar, T.: Single machine scheduling with unequal release date using neuro-dominance rule. J. Intell. Manuf. 22(4), 481–490 (2011)
Chen, C.L., Bulfin, R.L.: Complexity of single machine, multi-criteria scheduling problems. Eur. J. Oper. Res. 70(1), 115–125 (1993)
Zhong, T., Xiao, W., Xu, H., et al.: Hybrid evolutionary algorithm for single-machine scheduling problem with preparation time. J. Comput. Appl. 30(11), 3248–3252 (2013)
Ye, Q.: Research on a class of single machine scheduling problem based on improved ant colony algorithm. Hefei University of Technology (2008)
Wang, J.: Sustainable machine scheduling problem with minimizing carbon emissions. Control Decis. 32(6), 1063–1068 (2017)
Acknowledgements
This research is partially supported by National Natural Science Foundation of China (51665025), Applied Basic Research Key Project of Yunnan, China, and National Natural Science Fund for Distinguished Young Scholars of China (61525304).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, A., Qian, B., Hu, R., Wang, L., Li, SH. (2019). Single-Machine Green Scheduling Problem of Multi-speed Machine. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_63
Download citation
DOI: https://doi.org/10.1007/978-3-030-26969-2_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26968-5
Online ISBN: 978-3-030-26969-2
eBook Packages: Computer ScienceComputer Science (R0)