Abstract
With the global development of the third industrial revolution, intelligent manufacturing has received attention from many countries and regions since it was first proposed. In the next ten years, intelligent manufacturing has become an important factor in determining international status, and it is imminent for traditional manufacturing to switch to intelligent manufacturing. Flexible job-shop scheduling is a key research problem in the field of intelligent manufacturing. In this paper, we uses a novel swarm intelligence optimization algorithm-Sparrow Search Algorithm to solve the problem of the longest processing time of workshop scheduling. The experimental results show that compared with other advanced meta-heuristic algorithms, the Sparrow Search Algorithm (SSA) can not only achieve ideal optimization accuracy in the test function, but also can achieve acceleration effects and solving capabilities that other algorithms do not have in actual shop scheduling problems.
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References
Pezzella, F., Morganti, G., Ciaschetti, G.: A genetic algorithm for the flexible job-shop scheduling problem. Comput. Oper. Res. 35(10), 3202–3212 (2008)
Kacem, I., Hammadi, S., Borne, P.: Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Math. Comput. Simul. 60(3–5), 245–276 (2014)
Karthikeyan, S., Asokan, P., Chandrasekaran, M.: A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problems with maintenance activity. Appl. Mech. Mater. 575, 922–925 (2014). https://doi.org/10.1007/s00170-014-5753-3
Xing, L.N., Chen, Y.W., Wang, P., Zhao, Q.S., Xiong, J.: A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl. Soft Comput. 10(3), 888–896 (2010)
Vallikavungal Devassia, J., Salazar-Aguilar, M.A., Boyer, V.: Flexible job-shop scheduling problem with resource recovery constraints. Int. J. Prod. Res. 56(9–10), 3326–3343 (2018)
Beheshti, Z., Shamsuddin, S.M.H.: A review of population-based meta-heuristic algorithm. Int. J. Adv. Soft Comput. Appl. 5(1), 1–35 (2013)
Tarantilis, C., Kiranoudis, C.: A meta-heuristic algorithm for the efficient distribution of perishable foods. J. Food Eng. 50(1), 1–9 (2001)
Lei, Y., De, G., Fei, L.: Improved sparrow search algorithm based DV-Hop localization in WSN. In: 2020 Chinese Automation Congress (CAC) (2020)
Liu, G., Shu, C., Liang, Z., Peng, B., Cheng, L.: A modified sparrow search algorithm with application in 3D route planning for UAV. Sensors 21(4), 1224 (2021)
Zhu, Y., Yousefi, N.: Optimal parameter identification of PEMFC stacks using adaptive sparrow search algorithm. Int. J. Hydrogen Energy 46, 9541–9552 (2021)
Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27(4), 1053–1073 (2016). https://doi.org/10.1007/s00521-015-1920-110.1007/s00521-015-1920-1
Qian, B., Wang, L., Huang, D.X., Wang, X.: An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers. Comput. Oper. Res. 33(1), 2960–2971 (2009)
Chen, P., You, C., Ding, P.: Event classification using improved salp swarm algorithm based probabilistic neural network in fiber-optic perimeter intrusion detection system. Opt. Fiber Technol. 56, 102182 (2020)
Wang, J., Yang, W., Pei, D., Tong, N.: A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm. Ener. Convers. Manag. 163, 134–150 (2018)
Chen, Y., Wang, C.-F.: Synthesis of reactively controlled antenna arrays using characteristic modes and de algorithm. IEEE Antennas Wirel. Propag. Lett. 11, 385–388 (2012)
Warid, W., Hashim, H., Norman, M., Noor, A.W.: Optimal power flow using the jaya algorithm. Energies 9(9), 678 (2016)
Ham, A.: Flexible job shop scheduling problem for parallel batch processing machine with compatible job families. Appl. Math. Model. 45(May), 551–562 (2017)
Demir, Y., Isleyen, S.K.: Evaluation of mathematical models for flexible job-shop scheduling problems. Appl. Math. Modell. 37(3), 977–988 (2013)
Liang, J., Wang, Q., Xu, W., Gao, Z., Yan, Z., Yu, F.: Improved Niche GA for FJSP. In: 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS) (2019)
Sun, L., Lin, L., Gen, M., Li, H.: A hybrid cooperative coevolution algorithm for fuzzy flexible job shop scheduling. IEEE Trans. Fuzzy Syst. 27(5), 1008–1022 (2019)
Acknowledgement
This research work is supported by the National Key Research and Development Project under Grant 2018YFB1700500, Shenzhen Technology Research Project under Grant JSGG20180507182901552, and Natural Science Foundation of China (No. 52077213 and 62003332).
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Wu, M., Yang, D., Yang, Z., Guo, Y. (2021). Sparrow Search Algorithm for Solving Flexible Jobshop Scheduling Problem. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_13
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DOI: https://doi.org/10.1007/978-3-030-78743-1_13
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