Skip to main content
SpringerLink
Log in

Solving permutation flow-shop scheduling problem by rhinoceros search algorithm

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In this paper, a novel meta-heuristic search algorithm inspired by rhinoceros’ natural behaviour is proposed, namely rhinoceros search algorithm (RSA). Similar to our earlier version called elephant search algorithm, RSA simplifies certain habitual characteristics of rhinoceros and stream-lines the search operations, thereby reducing the number of operational parameters required to configure the model. Via computer simulation, it is shown that RSA is able to outperform certain classical meta-heuristic algorithms. Different dimensions of optimization problems are tested, and good results are observed by RSA. The RSA is also implemented on permutation flow-shop scheduling problem (PFSP) with some representation method. Four different problem scales are used. Compared with partible swarm optimization (PSO) on PFSP, the RSA outperforms PSO on different problem scales with a 3% improvement.

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.

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

Similar content being viewed by others

Notes

  1. UCI machine learning data archive. http://archive.ics.uci.edu/ml.

  2. http://www.cs.colostate.edu/sched/generator/.

References

  • Brown CT, Liebovitch LS, Glendon R (2007) Lévy flights in Dobe Ju/’hoansi foraging patterns. Hum Ecol 35(1):129–138

    Article  Google Scholar 

  • Campbell HG, Dudek RA, Smith ML (1970) A heuristic algorithm for the \(n\) job, \(m\) machine sequencing problem. Manag Sci 16(10):B-630

    Article  MATH  Google Scholar 

  • Chen C-L, Huang S-Y, Tzeng Y-R, Chen C-L (2014) Revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem. Soft Comput 18(11):2271–2282

    Article  Google Scholar 

  • Deb S, Fong S, Tian Z (2015) Elephant search algorithm for optimization problems. In: 2015 10th international conference on digital information management (ICDIM). IEEE, pp 249–255

  • Garey MR, Johnson DS, Sethi R (1976) The complexity of flowshop and jobshop scheduling. Math Oper Res 1(2):117–129

    Article  MathSciNet  MATH  Google Scholar 

  • Iyer SK, Saxena B (2004) Improved genetic algorithm for the permutation flowshop scheduling problem. Comput Oper Res 31(4):593–606

    Article  MathSciNet  MATH  Google Scholar 

  • Lee CY, Yao X (2004) Evolutionary programming using mutations based on the Lévy probability distribution. IEEE Trans Evol Comput 8(1):1–13

    Article  Google Scholar 

  • Liu Y-F, Liu S-Y (2013) A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem. Appl Soft Comput 13(3):1459–1463

    Article  Google Scholar 

  • Malakooti B (2013) Operations and production systems with multiple objectives. Wiley, London

    Google Scholar 

  • Marinakis Y, Marinaki M (2013) Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem. Soft Comput 17(7):1159–1173

    Article  MATH  Google Scholar 

  • Nawaz M, Enscore EE, Ham I (1983) A heuristic algorithm for the \(m\)-machine, \(n\)-job flow-shop sequencing problem. Omega 11(1):91–95

    Article  Google Scholar 

  • Nowicki E, Smutnicki C (1996) A fast tabu search algorithm for the permutation flow-shop problem. Eur J Oper Res 91(1):160–175

    Article  MATH  Google Scholar 

  • Palmer DS (1965) Sequencing jobs through a multi-stage process in the minimum total time—a quick method of obtaining a near optimum. J Oper Res Soc 16(1):101–107

    Article  Google Scholar 

  • Taillard E (1993) Benchmarks for basic scheduling problems. Eur J Ope Res 64(2):278–285

    Article  MATH  Google Scholar 

  • Tasgetiren MF et al (2007) A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. Eur J Oper Res 177(3):1930–1947

    Article  MATH  Google Scholar 

  • Wang H, Wang W, Sun H, Cui Z, Rahnamayan S, Zeng S (2017) A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems. Soft Comput 21(15):4297–4307

    Article  Google Scholar 

  • Widmer M, Hertz A (1989) A new heuristic method for the flow shop sequencing problem. Eur J Oper Res 41(2):186–193

    Article  MATH  Google Scholar 

  • Ying K-C, Liao C-J (2004) An ant colony system for permutation flow-shop sequencing. Comput Oper Res 31(5):791–801

    Article  MATH  Google Scholar 

  • Zou F, Chen D, Lu R, Wang P (2016) Hierarchical multi-swarm cooperative teaching-learning-based optimization for global optimization. Soft Comput 21:1–22

    Google Scholar 

Download references

Acknowledgements

The authors are grateful for financial support from the research Grants (1) ‘Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data Mining Performance’ from the University of Macau (Grant No. MYRG2016-00069-FST); (2) ‘Temporal Data Stream Mining by Using Incrementally Optimized Very Fast Decision Forest (iOVFDF)’, which are offered by the University of Macau (Grant No. MYRG2015-00128-FST); and (3) ‘A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel‘, from FDCT, Macau SAR government (Grant No. FDCT/126/2014/A3).

Author information

Authors and Affiliations

  1. IT and Educational Consultant, Ranchi, India

    Suash Deb

  2. Decision Sciences and Modelling Program, Victoria University, Melbourne, Australia

    Suash Deb

  3. Department of Computer and Information Science, University of Macau, Taipa, Macau SAR, China

    Zhonghuan Tian, Simon Fong & Rui Tang

  4. School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia

    Raymond Wong

  5. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

Authors
  1. Suash Deb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhonghuan Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simon Fong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rui Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Raymond Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nilanjan Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simon Fong.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Human and Animal Rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by S. Deb, T. Hanne, K. C. Wong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deb, S., Tian, Z., Fong, S. et al. Solving permutation flow-shop scheduling problem by rhinoceros search algorithm. Soft Comput 22, 6025–6034 (2018). https://doi.org/10.1007/s00500-018-3075-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3075-3

Keywords

Search

Navigation