Skip to main content

Advertisement

SpringerLink
Log in

Solving the two-stage hybrid flow shop scheduling problem based on mutant firefly algorithm

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

With the development of industry, the manufacturing system becomes more and more complex. The traditional manufacturing industry is gradually changing to intelligent manufacturing. And it leads to the increase of difficulty of scheduling. This paper presents a mutant firefly algorithm for two-stage hybrid flow shop scheduling problem with two objective functions. One of it is the simultaneous rate for the arrival of different parts of products at assembly stage and another is the on-time delivery rate based on the products delivery schedule. The function can strengthen the link between the manufacturing stage and the assembly stage. Furthermore, this paper proposes two coding methods, external coding system and internal coding system, to make the coding operation easy to understand and efficient. The simulation results show that the optimized algorithm has better convergence and higher efficiency of calculating. And it has good performance in reducing the number of work in process as well as the pressure on the buffer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Allahverdi A, Al-Anzi FS (2008) The two-stage assembly flowshop scheduling problem with bicriteria of makespan and mean completion time. Int J Adv Manuf Tech 37(1–2):166–177

    Article  Google Scholar 

  • Allahverdi A, Aydilek H (2015) The two stage assembly flowshop scheduling problem to minimize total tardiness. J Intell Manuf 26(2):225–237

    Article  Google Scholar 

  • Bożejko W, Pempera J, Smutnicki C (2013) Parallel tabu search algorithm for the hybrid flow shop problem. Comput Ind Eng 65(3):466–474

    Article  Google Scholar 

  • Chou FD (2013) Particle swarm optimization with cocktail decoding method for hybrid flow shop scheduling problems with multiprocessor tasks. Int J Prod Econ 141(1):137–145

    Article  Google Scholar 

  • Chung TP, Liao CJ (2013) An immunoglobulin-based artificial immune system for solving the hybrid flow shop problem. Appl Soft Comput 13(8):3729–3736

    Article  Google Scholar 

  • Engin O, Ceran G, Yilmaz MK (2011) An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems. Appl Soft Comput 11(3):3056–3065

    Article  Google Scholar 

  • Fattahi P, Hosseini et al (2013) A mathematical model and extension algorithm for assembly flexible flow;shop scheduling problem. Int J Adv Manuf Technol 65(5–8):787–802

    Article  Google Scholar 

  • Gupta JND (1988) Two-stage hybrid flowshop scheduling problem. J Oper Res Soc 39(4):359–364

    Article  MATH  Google Scholar 

  • Hongsheng Z, Dongjin Y, Zhang L (2017) Multi-QoS cloud workflow scheduling based on firefly algorithm and dynamic priorities. CIMS 23(5):963–971

    Google Scholar 

  • Hosseini SMH (2016) Modeling the hybrid flow shop scheduling problem followed by an assembly stage considering aging effects and preventive maintenance activities. pp 1215–1233

  • Chen J, Huang GQ, Luo H et al (2015) Synchronisation of production scheduling and shipment in an assembly flowshop. Int J Prod Res 53(9):2787–2802

    Article  Google Scholar 

  • Kahraman C, Engin O (2010) Multiprocessor task scheduling in multistage hybrid flow-shops: a parallel greedy algorithm approach. Appl Soft Comput 10(4):1293–1300

    Article  Google Scholar 

  • Langjun P (2016) Research on Firefly-algorithm-based flexible job shop scheduling problem. Dissertation, Xiangtan University

  • Li JQ, Pan QK (2015) Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm. Elsevier Sci 316(C):487–502

    Google Scholar 

  • Li X, Fu P, Xia L et al (2017) Integrated sequencing of serial parallel mixed assembly line based artificial bee colony. Robot Cim Int Manuf 23(3):567–574

    Google Scholar 

  • Liang X, Huang M, Ning T (2016) Flexible job shop scheduling based on improved hybrid immune algorithm. J Ambient Intell Human Comput 2016:1–7

    Google Scholar 

  • Liao CJ, Tjandradjaja E, Chung TP (2012) An approach using particle swarm optimization and bottleneck heuristic to solve HFSP. Appl Soft Comput 12(6):1755–1764

    Article  Google Scholar 

  • Lin BMT (2015) Two-stage flow shop scheduling with dedicated machines. Int J Prod Res 53(4):1094–1097

    Article  Google Scholar 

  • Lin IC, Cheng CY (2016) Case study of physical internet for improving efficiency in solar cell industry. J Ambient Intell Human Comput 2016:1–10

    Google Scholar 

  • Luo R (2016) Study on the scheduling and optimization on the manufacturing and assembly in Hybrid Flow shop for one-of-a kind production. Dissertation, Guangdong University of Technology

  • Molano JIR, Lovelle JMC, Montenegro CE et al (2017) Metamodel for integration of internet of things, social networks, the cloud and industry 4.0. J Ambient Intell Human Comput 9(3):709–723

    Article  Google Scholar 

  • Ning T, Jin H, Song X et al (2017) An improved quantum genetic algorithm based on MAGTD for dynamic FJSP. J Ambient Intell Human Comput 2017:1–10

    Google Scholar 

  • Nishi T, Hiranaka Y, Inuiguchi M (2010) Lagrangian relaxation with cut generation for hybrid flowshop scheduling problems to minimize the total weighted tardiness. Comput Oper Res 37(1):189–198

    Article  MathSciNet  MATH  Google Scholar 

  • Pan QK, Wang L, Li JQ et al (2014) A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation. Omega 45(2):42–56

    Article  Google Scholar 

  • Paul RJ (1979) A production scheduling problem in the glass-container industry. Oper Res 27(2):290–302

    Article  Google Scholar 

  • Ribas I, Leisten R, Framiñan JM (2010) Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Comput Oper Res 37(8):1439–1454

    Article  MathSciNet  MATH  Google Scholar 

  • Ruizab R (2010) The hybrid flow shop scheduling problem. Eur J Oper Res 205(1):1–18

    Article  MathSciNet  Google Scholar 

  • Teymourian E (2016) Minimising makespan in the two-stage assembly hybrid flow shop scheduling problem using artificial immune systems. Int J Prod Res 54(4):1–22

    Google Scholar 

  • Tian Y (2010) Research on particle swarm optimization algorithm an its application. Dissertation, Jilin University

  • Tian Y, Liu D(2011) A hybrid particle swarm optimization method for flow shop scheduling problem. Acta Electron Sin 48(5):1087–1093

    Google Scholar 

  • Torabzadeh E, Zandieh M (2010) Cloud theory-based simulated annealing approach for scheduling in the two-stage assembly flowshop. Adv Eng Softw 41(10):1238–1243

    Article  MATH  Google Scholar 

  • Wang S, Wang L, Xu Y (2013) Estimation of distribution algorithm for solving hybrid flow-shop scheduling problem with identical parallel machine. Comput Integr Manuf Syst 19(06):1304–1312

    Google Scholar 

  • Wang S, Liu M, Chu C (2015) A branch-and-bound algorithm for two-stage no-wait hybrid flow-shop scheduling. Int J Prod Res 53(4):1143–1167

    Article  Google Scholar 

  • Wang L, Guo C, Li Y et al (2017) An outsourcing service selection method using ANN and SFLA algorithms for cement equipment manufacturing enterprises in cloud manufacturing. J Ambient Intell Human Comput 2017(12):1–15

    Google Scholar 

  • Xuan H (2012) Three-stage HFS scheduling problem with serial batching machines. Comput Integr Manuf Syst 18(5):1006–1010

    Google Scholar 

  • Yang XS (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms, Springer, Berlin, pp 169–178

  • Yang XS (2010) Firefly Algorithm, Stochastic Test Functions and Design Optimisation. Int J Bio-Inspir Comput 2(2):78–84(7)

    Article  Google Scholar 

  • Yang J (2015) Minimizing total completion time in a two-stage hybrid flow shop with dedicated machines at the first stage. Comput Oper Res 58(C):1–8

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang Q, Zuoming Z (2017) Discrete fruit fly optimization algorithm based on dominant population for solving no-wait flow shop scheduling problem. Comput Integr Manuf Syst CIMS 23(3):609–615

    Google Scholar 

  • Zhong RY, Xu C, Chen C et al (2017) Big data analytics for physical internet-based intelligent manufacturing shop floors. Int J Prod Res 55(9):2610–2621

    Article  Google Scholar 

Download references

Acknowledgements

This research was financially supported by National Natural Science Foundation of China (Nos. 51405281, 51205242) and Shanghai Key Laboratory of Advanced Manufacturing Environment. The authors express sincere appreciation to the anonymous referees for their helpful comments to improve the quality of the paper.

Author information

Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Beibei Fan, Wenwei Yang & Zaifang Zhang

Authors
  1. Beibei Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenwei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zaifang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Beibei Fan.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, B., Yang, W. & Zhang, Z. Solving the two-stage hybrid flow shop scheduling problem based on mutant firefly algorithm. J Ambient Intell Human Comput 10, 979–990 (2019). https://doi.org/10.1007/s12652-018-0903-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-018-0903-3

Keywords

Search

Navigation