Abstract
Swarm intelligence algorithm (SIA) is an important artificial intelligence technology, which has been widely applied in various research fields. Recently, adopting various multi-objective SIAs (MOSIAs) to solve multi-objective flow shop scheduling problem (MOFSP) has attracted wide research attention. However, there are fewer review papers on the MOFSP. Many new MOSIAs have been proposed to solve MOFSP in the last decade. Therefore, in this study, MOSIAs of MOFSP over the past decade are briefly reviewed and analyzed. Based on the existing problems and new trend of Industry 4.0, several new promising future research directions are pointed out. These research directions are: (1) new hybrid MOSIA; (2) MOSIA with high computational efficiency; (3) MOSIA based on machine learning and big data; (4) multi-objective approach; (5) many-objective flowshop scheduling.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Xing, Y., Chen, Y., Lv, C., et al.: Swarm intelligence-based power allocation and relay selection algorithm for wireless cooperative network. KSII Trans. Internet Inf. Syst. 10(3), 1111–1130 (2016)
Lazzús, J.A., Rivera, M., López-Caraballo, C.H.: Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm. Phys. Lett. A 380(11–12), 1164–1171 (2016)
Zhang, X., Zhang, X.: Shift based adaptive differential evolution for PID controller designs using swarm intelligence algorithm. Clust. Comput. 20(1), 1–9 (2016)
De, D.H., Villarubia, G., DePaz, J.F., et al.: Multi-sensor information fusion for optimizing electric bicycle routes using a swarm intelligence algorithm. Sensors 17(11), 2501 (2017)
Minella, G., Ruiz, R., Ciavotta, M.: A review and evaluation of multi-objective algorithms for the flowshop scheduling problem. Inf. J. Comput. 20(3), 451–471 (2008)
Sun, Y., Zhang, C., Gao, L., Wang, X.: Multi-objective optimization algorithms for flow shop scheduling problem: a review and prospects. Int. J. Adv. Manuf. Technol. 55(5–8), 723–739 (2011)
Yenisey, M.M., Yagmahan, B.: Multi-objective permutation flow shop scheduling problem: literature review, classification and current trends. Omega 45(2), 119–135 (2014)
Lin, S.W., Ying, K.C.: Minimizing makespan and total flow time in permutation flow shops by a bi-objective multi-start simulated-annealing algorithm. Comput. Oper. Res. 40(6), 1625–1647 (2013)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Michigan (1975)
Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput. 4(2), 87–112 (1994)
Chang, P.C., Chen, S.H., Fan, C.Y., et al.: Genetic algorithm integrated with artificial chromosomes for multi-objective flowshop scheduling problems. Appl. Math. Comput. 205(2), 550–561 (2008)
Framinan, J.M.: A fitness-based weighting mechanism for multicriteria flowshop scheduling using genetic algorithms. Int. J. Adv. Manuf. Technol. 43(9–10), 939–948 (2009)
Pour, N.S., Tavakkolimoghaddam, R., Asadi, H.: Optimizing a multi-objectives flow shop scheduling problem by a novel genetic algorithm. Int. J. Ind. Eng. Comput. 4(3), 345–354 (2013)
Karimi, N., Davoudpour, H.: A high performing metaheuristic for multi-objective flowshop scheduling problem. Comput. Oper. Res. 52, 149–156 (2014)
Fu, Y., Huang, M., Wang, H., et al.: Multipopulation multiobjective genetic algorithm for multiobjective permutation flow shop scheduling problem. Control Theor. Appl. 10(33), 1281–1288 (2016)
Rajkumar, R., Shahabudeen, P.: Bi-criteria improved genetic algorithm for scheduling in flowshops to minimize makespan and total flowtime of jobs. Int. J. Comput. Integr. Manuf. 22(10), 987–998 (2009)
Ruiz, R., Allahverdi, A.: Minimizing the bicriteria of makespan and maximum tardiness with an upper bound on maximum tardiness. Comput. Oper. Res. 36(4), 1268–1283 (2009)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Uysal, O., Bulkan, S.: Comparison of genetic algorithm and particle swarm optimization for bicriteria permutation flowshop scheduling problem. Int. J. Comput. Intell. Res. 4(2), 159–175 (2008)
Li, B.B., Wang, L., Liu, B.: An effective PSO-based hybrid algorithm for multiobjective permutation flow shop scheduling. IEEE Trans. Syst. Man, Cybern.-Part A: Syst. Humans 38(4), 818–831 (2008)
Sha, D.Y., Lin, H.H.: A particle swarm optimization for multi-objective flowshop scheduling. Int. J. Adv. Manuf. Technol. 45(7–8), 749–758 (2009)
Tsai, J.T., Yang, C.I., Chou, J.H.: Hybrid sliding level Taguchi-based particle swarm optimization for flowshop scheduling problems. Appl. Soft Comput. 15(2), 177–192 (2014)
Yang, C.I., Chou, J.H., Chang, C.K.: Hybrid Taguchi-based particle swarm optimization for flowshop scheduling problem. Arab. J. Sci. Eng. 39(3), 2393–2412 (2014)
He, L.J., Liu, C., Zhu, G.Y.: High-dimensional multi-objective flow shop scheduling optimization based on relative entropy of fuzzy sets. Comput. Integr. Manuf. Syst. 21(10), 2704–2710 (2015)
Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2007)
Yagmahan, B., Yenisey, M.M.: A multi-objective ant colony system algorithm for flow shop scheduling problem. Expert Syst. Appl. 37(2), 1361–1368 (2010)
Rabanimotlagh, A.: An efficient ant colony optimization algorithm for multiobjective flow shop scheduling problem. World Acad. Sci. Eng. Technol. 5(3), 598–604 (2011)
Moscato, P., Cotta, C.: A modern introduction to memetic algorithms. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-1665-5_6
Chiang, T.C., Cheng, H.C., Fu, L.C.: NNMA: an effective memetic algorithm for solving multiobjective permutation flow shop scheduling problems. Expert Syst. Appl. 38(5), 5986–5999 (2011)
Li, X.T., Ma, S.J.: Multi-objective memetic search algorithm for multi-objective permutation flow shop scheduling problem. IEEE Access 4, 2154–2165 (2017)
Larranaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Press, Boston (2002)
Chang, P.C.: A pareto block-based estimation and distribution algorithm for multi-objective permutation flow shop scheduling problem. Int. J. Prod. Res. 53(3), 793–834 (2015)
Zangari, M., Mendiburu, A., Santana, R., Pozo, A.: Multiobjective decomposition-based mallows models estimation of distribution algorithm. A case of study for permutation flowshop scheduling problem. Inf. Sci. 397(C), 137–154 (2017)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(C), 341–359 (1997)
Pan, Q.K., Wang, L., Qian, B.: A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems. Comput. Oper. Res. 36(8), 2498–2511 (2009)
Zhu, G.Y., Chen, X.B., Liu, Y.L.: Flow shop multi-objective scheduling optimization research based on grey entropy relation analysis and the algorithm relation. Control Decis. 29(1), 135–140 (2014)
Tavakkoli-Moghaddam, R., Rahimi-Vahed, A.R., Mirzaei, A.H.: Solving a multi-objective no-wait flow shop scheduling problem with an immune algorithm. Int. J. Adv. Manuf. Technol. 36(9–10), 969–981 (2008)
Rahimi-Vahed, A., Dangchi, M., Rafiei, H., et al.: A novel hybrid multi-objective shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem. Int. J. Adv. Manuf. Technol. 41(11–12), 1227–1239 (2009)
Naderi, B., Tavakkoli-Moghaddam, R., Khalili, M.: Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan. Knowl.-Based Syst. 23(2), 77–85 (2010)
Frosolini, M., Braglia, M., Zammori, F.A.: A modified harmony search algorithm for the multi-objective flow shop scheduling problem with due dates. Int. J. Prod. Res. 49(20), 5957–5985 (2011)
Chen, K.J., Zhou, X.M.: Improved food chain algorithm for multi objective permutation flow shop scheduling. China Mech. Eng. 26(3), 348–353 (2015)
Xu, Z.H., Li, J.M., Gu, X.S.: Multi-objective flow shop scheduling problem based on GMOGSO. Control Decis. 31(10), 1772–1778 (2016)
Huang, X., Ye, C.M., Cao, L.: Chaos invasive weed optimization algorithm for multiobjective permutation flow shop scheduling problem. Syst. Eng.-Theory Pract. 37(1), 253–262 (2017)
Zhu, G.Y., He, L.J., Ju, X.W., Zhang, W.B.: A fitness assignment strategy based on the grey and entropy parallel analysis and its application to MOEA. Eur. J. Oper. Res. 265(3), 813–828 (2018)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)
Chia, J.Y., Goh, C.K., Shim, V.A., et al.: A data mining approach to evolutionary optimisation of noisy multi-objective problems. Int. J. Syst. Sci. 43(7), 1217–1247 (2012)
Wang, X.P., Tang, L.X.: An adaptive multi-population differential evolution algorithm for continuous multi-objective optimization. Inf. Sci. 348(2), 124–141 (2016)
Zhang, H.X., Lu, J.: Adaptive evolutionary programming based on reinforcement learning. Inf. Sci. 178(4), 971–984 (2008)
Zhang, J., Zhan, Z.H., Lin, Y., et al.: Evolutionary computation meets machine learning: a survey. IEEE Comput. Intell. Mag. 6(4), 68–75 (2011)
Luo, Y., Duan, Y., Li, W.F., Pace, P., Fortino, G.: Workshop networks integration using mobile intelligence in smart factories. IEEE Commun. Mag. 56(2), 68–75 (2018)
Luo, Y., Duan, Y., Li, F.W., Pace, P., Fortino, G.: A novel mobile and hierarchical data transmission architecture for smart factories. IEEE Trans. Industr. Inf. 14(8), 3534–3546 (2018)
Acknowledgement
This paper is supported by the National Natural Science Foundation of China (61571336 and 71874132).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
He, L., Li, W., Zhang, Y., Cao, J. (2018). Review of Swarm Intelligence Algorithms for Multi-objective Flowshop Scheduling. In: Xiang, Y., Sun, J., Fortino, G., Guerrieri, A., Jung, J. (eds) Internet and Distributed Computing Systems. IDCS 2018. Lecture Notes in Computer Science(), vol 11226. Springer, Cham. https://doi.org/10.1007/978-3-030-02738-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-02738-4_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02737-7
Online ISBN: 978-3-030-02738-4
eBook Packages: Computer ScienceComputer Science (R0)