Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-18T10:02:18.125Z Has data issue: false hasContentIssue false
  • English
  • Français

A cooperative co-evolutionary particle swarm optimiser based on a niche sharing scheme for the flow shop scheduling problem under uncertainty

Published online by Cambridge University Press:  04 September 2014

BIN JIAO  and
SHAOBIN YAN
BIN JIAO
Affiliation:
Electric School, Shanghai Dianji University, 690 Jiang Chuan Road, Min Hang District, Shanghai, China Email: binjiaocn@163.com
SHAOBIN YAN
Affiliation:
School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China Email: yshaobin123@sina.com
Get access Rights & Permissions [Opens in a new window]

Abstract

The flow shop scheduling problem based on ideal and precise conditions has been a focus of considerable research since the first easy scheduling problem was formulated. In reality, some uncertain factors always restrict the scheduling optimisation problem. In this paper, taking uncertain processing time as an example, we use generalised rough sets theory to transform the rough flow shop scheduling model into the precise scheduling model. We adopt a cooperative co-evolutionary particle swarm optimisation algorithm based on a niche sharing scheme (NCPSO) to minimise the makespan in comparison with the particle swarm optimiser (PSO) and co-evolution particle swarm optimiser (CPSO) algorithms. The new algorithm is characterised by a strengthening of the ability to reserve excellent particles and searching the optimal solution. Experimental results show that the new algorithm is more effective and efficient than the others.

Type
Paper
Information
Mathematical Structures in Computer Science , Volume 24 , Special Issue 5: Mathematical Method in Intelligent Computing and Automation , October 2014 , e240502
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chanas, S. and Kasperski, A. A. (2001) Minimizing Maximum Lateness in a Single Machine Scheduling Problem with Fuzzy Processing Times and Fuzzy Due Dates. Engineering Applications of Artificial Intelligence 14 (3)377–386.Google Scholar
Fortemps, P (1997) Job shop Scheduling with Imprecise Duration: Fuzzy Approach. IEEE Transactions on Fuzzy Systems 5 (4)557–569.CrossRefGoogle Scholar
Gan, J. and Warwick, K. (1999) A genetic algorithm with dynamic niche clustering for multimodal function optimization. In: Dobnikar, A., Steele, N. C., Pearson, D. W. and Albrecht, R. F. (eds.) Artificial Neural Nets and Genetic Algorithms, Springer-Verlag 248–255.Google Scholar
Gan, J. and Warwick, K. (2001) Dynamic niche clustering: a fuzzy variable radius niching technique for multimodal optimisation in GAs. In: Proceedings of IEEE Congress on Evolutionary Computation 215–222.CrossRefGoogle Scholar
Goldberg, D. and Richardson, J. (1987) Genetic algorithms with sharing for multimodal function optimization. In: Proceedings International Conference Genetic Algorithms 41–49.Google Scholar
Goldberg, D., Deb, K. and Horn, J. (1992) Massive multimodality, deception and genetic algorithms. In: Proceedings Parallel Problem Solving from Nature 2 37–46.Google Scholar
Gu, X.-S., Zheng, L., Li, P. and Zhang, W. (2004) Earliness/Tardiness Flow Shop Production Scheduling under Uncertainty within Finite Intermediate Storage. Journal of East China University of Science and Technology 30 (3)322–327.Google Scholar
Hong, T.-P. and Wang, T.-T. (2000) Fuzzy Flexible Flow Shops at Two Machine Centers for Continuous Fuzzy Domains. Information Sciences 129 (1–4)227–237.Google Scholar
Johnson, S. M. (1954) Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly (1) 61–8.CrossRefGoogle Scholar
Litoiu, M. and Tadei, R. (2001) Real-time Task Scheduling with Fuzzy Deadlines and Processing Times. Fuzzy Sets and Systems 117 (1)35–45.CrossRefGoogle Scholar
McCahon, S and Lee, E. S. (1990) Job Sequencing with Fuzzy Processing Times. Computers and Mathematics with Applications 19 (7)31–41.CrossRefGoogle Scholar
Niu, Q. and Gu, X.-S. (2008) A Novel Particle Swarm Optimization for Flow Shop Scheduling with Fuzzy Processing Time. Journal of Donghua University 25 (2)115–122.Google Scholar
Pawlak, Z. (1982) Rough sets. International Journal of Computer and Information Sciences 11 341–356.CrossRefGoogle Scholar
Pawlak, Z. (1991) Rough Sets – Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers.Google Scholar
Petrowski, A. (1996) A clearing procedure as a niching method for genetic algorithms. In: Proceedings of IEEE International Conference on Evolutionary Computation 798–803.CrossRefGoogle Scholar
Prade, H (1979) Using Fuzzy Set Theory in a Scheduling Problem: A Case Study. Fuzzy Sets and Systems 2 (2)153–165.CrossRefGoogle Scholar
Sareni, B. and Krahenbuhl, L. (1998) Fitness sharing and niching methods revisited. IEEE Transactions on Evolutionary Computation 2 (3)97–106.Google Scholar
Wu, C.-C. and Gu, X.-S. (2004) A Genetic Algorithm for Single Machine Scheduling with Fuzzy Processing Time and Multiple Objectives. Journal of Donghua University 21 (3)185–189.Google Scholar
Xu, Z.-H. and Gu, X.-S. (2006) Earliness and tardiness flow shop scheduling problems under uncertainty with finite intermediate storage. Control Theory Applications 23 (3)480–486.Google Scholar
Yu, A.-Q. and Gu, X.-S. (2006) Flow Shop Scheduling Problem under Uncertainty Based on Generalized Rough Sets. Journal of System Simulation 18 (12)3369–3372, 3376.Google Scholar
Yu, A.-Q. and Gu, X.-S. (2008) Parallel machines scheduling with uncertain processing time based on rough programming. Control and Decision 23 (12)1427–1431.Google Scholar