Skip to main content
SpringerLink
Log in

An Effective Genetic Algorithm for Multi-objective IPPS with Various Flexibilities in Process Planning

  • Chapter
  • First Online:
Effective Methods for Integrated Process Planning and Scheduling

Part of the book series: Engineering Applications of Computational Methods ((EACM,volume 2))

  • 666 Accesses

Abstract

In this chapter, an effective genetic algorithm is proposed to optimize the multi-objective Integrated Process Planning and Scheduling (IPPS) problem with various flexibilities in process planning. Three types of flexibilities related to process, sequence, and machine are considered. And three objectives including makespan, total machine workload, and maximal machine workload are taken into account simultaneously. According to the model and characteristics of multi-objective IPPS, the framework of the proposed algorithm is designed to optimize three objectives simultaneously. Effective genetic operations are employed in the proposed algorithm. Pareto set is set to store and maintain the solutions obtained during the searching procedure; the proposed algorithm could get several Pareto-optimal solutions during one searching process. Two experiments are employed to test the performance of the proposed algorithm. The experiment results show that the proposed algorithm can solve multi-objective IPPS problems with various flexibilities in process planning effectively and obtain good solutions.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Li XY, Gao L, Li WD (2012) Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling. Expert Syst Appl 39:288–297

    Article  Google Scholar 

  2. Li XY, Gao L, Zhang CY, Shao XY (2010) A review on integrated process planning and scheduling. Int J Manuf Res 5:161–180

    Article  Google Scholar 

  3. Guo YW, Li WD, Mileham AR, Owen GW (2009) Applications of particle swarm optimisation in integrated process planning and scheduling. Robot Comput-Integr Manuf 25:280–288

    Article  Google Scholar 

  4. Li XY, Gao L, Wen XY (2013) Application of an efficient modified particle swarm optimization algorithm for process planning. Int J Adv Manuf Technol 67:1355–1369

    Article  Google Scholar 

  5. Li WD, Ong SK, Nee AYC (2002) Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int J Prod Res 40:1899–1922

    Article  Google Scholar 

  6. Zhang C, Li P, Rao Y, Li S (2005) A new hybrid GA/SA algorithm for the job shop scheduling problem. Lect Notes Comput Sci 3448:246–259

    Article  Google Scholar 

  7. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197

    Article  Google Scholar 

  8. Baykasoğlu A, Özbakır L (2009) A grammatical optimization approach for integrated process planning and scheduling. J Intell Manuf 20:211–221

    Article  Google Scholar 

  9. Rajkumar M, Asokan P, Page T, Arunachalam S (2010) A GRASP algorithm for the integration of process planning and scheduling in a flexible job-shop. Int J Manuf Res 5:230–251

    Article  Google Scholar 

  10. Li WD, McMahon CA (2007) A simulated annealing-based optimization approach for integrated process planning and scheduling. Int J Comput Integr Manuf 20:80–95

    Article  Google Scholar 

  11. Ma GH, Zhang YF, Nee AYC (2000) A simulated annealing-based optimization algorithm for process planning. Int J Prod Res 38:2671–2687

    Article  Google Scholar 

  12. Wang YF, Zhang YF, Fuh JYH (2009) Using hybrid particle swarm optimization for process planning problem. In: Proceedings of the computational sciences and optimization, 2009. pp 304–308

    Google Scholar 

  13. Zhang YF, Nee AYC (2001) Applications of genetic algorithms and simulated annealing in process planning optimization. In: Wang J, Kusiak A (eds) Computational intelligence in manufacturing handbook

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Xinyu Li

  2. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Liang Gao

Authors
  1. Xinyu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinyu Li .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer-Verlag GmbH Germany, part of Springer Nature and Science Press, Beijing

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Li, X., Gao, L. (2020). An Effective Genetic Algorithm for Multi-objective IPPS with Various Flexibilities in Process Planning. In: Effective Methods for Integrated Process Planning and Scheduling. Engineering Applications of Computational Methods, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55305-3_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-55305-3_15

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-55303-9

  • Online ISBN: 978-3-662-55305-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation