Skip to main content
SpringerLink
Log in

Advances in Hybrid Genetic Algorithms with Learning and GPU for Scheduling Problems: Brief Survey and Case Study

  • Conference paper
  • First Online:
Proceedings of the Fourteenth International Conference on Management Science and Engineering Management (ICMSEM 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1190))

  • 1040 Accesses

Abstract

Scheduling is one of the very important tools for treating a complex combinatorial optimization problem (COP) model, where it can have a major impact on the productivity of a manufacturing process. The most well known models of scheduling are confirmed as NP-hard or NP-complete problems. The aim of scheduling is to find a schedule with the best performance through selecting resources for each operation, the sequence for each resource and the beginning time. Genetic algorithm is one of the most efficient methods among metaheuristics for solving the real-world manufacturing problems. In this paper we firstly survey the literature on genetic algorithms (GAs) with GPU acceleration. A parallel multiobjective GA (MoGA) acceleration with CUDA (Compute Unified Device Architecture) will be introduced. A parallel hybrid multiobjective GA with learning is introduced through a real-world case study of the train scheduling problem and numerical experiments on GPU for multiobjective GA approaches are also demonstrated.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Blackstone, J.H., Phillips, D.T., Hogg, G.L.: A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int. J. Prod. Res. 20(1), 27–45 (1982)

    Article  Google Scholar 

  2. Blazewicz, J., Ecker, K., et al.: Handbook on scheduling: from theory to applications. J. Sched. 12(4), 433–434 (2009)

    Article  Google Scholar 

  3. Brucker, P.: Scheduling Algorithms. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  4. Cheng, J.R., Gen, M.: Accelerating genetic algorithms with GPU computing: a selective overview. Comput. Ind. Eng. 128, 514–525 (2019)

    Article  Google Scholar 

  5. Cheng, J.R., Gen, M., Tsujimura, Y.: A tutorial survey of job-shop scheduling problems using genetic algorithm, part i: representation. Comput. Ind. Eng. 30(4), 983–997 (1996)

    Article  Google Scholar 

  6. Cheng, J.R., Gen, M., Tsujimura, Y.: A tutorial survey of job-shop scheduling problems using genetic algorithm, part ii: hybrid genetic search strategies. Comput. Ind. Eng. 36(2), 343–364 (1999)

    Article  Google Scholar 

  7. Cheng, J.R., Grossman, M., McKercher, T.: Professional CUDA C programming. Wiley, Indianapolis (2014)

    Google Scholar 

  8. Chou, C.W., Chien, C.F., Gen, M.: A multiobjective hybrid genetic algorithm for TFT-LCD module assembly scheduling. IEEE Trans. Autom. Sci. Eng. 11(3), 692–705 (2014)

    Article  Google Scholar 

  9. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)

    MATH  Google Scholar 

  10. Deb, K., Pratap, A., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  11. Gen, M., Cheng, R.: Genetic Algorithms and Engineering Design. Wiley, New York (1997)

    Google Scholar 

  12. Gen, M., Cheng, R.: Genetic Algorithms and Engineering Optimization. Wiley, New York (2000)

    Google Scholar 

  13. Gen, M., Lin, L.: Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey. J. Intell. Manuf. 25(5), 849–866 (2014)

    Article  MathSciNet  Google Scholar 

  14. Gen, M., Cheng, J.R., Lin, L.: Network Models and Optimization: Multiobjective Genetic Algorithm Approach. Springer, London (2008)

    MATH  Google Scholar 

  15. Gen, M., Zhang, W.Q., et al.: Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling. Comput. Ind. Eng. 112, 616–633 (2017)

    Article  Google Scholar 

  16. Giffler, B., Thompson, G.: Algorithms for solving production scheduling problem. Oper. Res. 8(4), 487–503 (1960)

    Article  MathSciNet  Google Scholar 

  17. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)

    MATH  Google Scholar 

  18. Hao, X.C.: Study on hybridized estimation of distribution algorithm with probabilistic graphical models and scheduling applications. Ph.D. thesis, Waseda University, Japan (2016)

    Google Scholar 

  19. Hwuang, C.L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications a State-of-the-Art Survey. Springer, New York (2012)

    Google Scholar 

  20. Jourdan, L., Dhaenens, C., Talbi, E.G.: Using data mining techniques to help metaheuristics: a short survey. IEEE Trans. Evol. Comput. 4030, 57–69 (2006)

    Google Scholar 

  21. Kromer, P., Platos, J., et al.: Many-threaded differential evolution on the GPU. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs (2013)

    Google Scholar 

  22. Kromer, P., Platos, J., Snasel, V.: Nature-inspired meta-heuristics on modern GPUs: state of the art and brief survey of selected algorithms. Int. J. Parallel Prog. 42, 681–709 (2014)

    Article  Google Scholar 

  23. Kruger, F., Maitre, O., et al.: Generic local search (memetic) algorithm on a single GPGPU chip. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs (2013)

    Google Scholar 

  24. Lin, L., Gen, M.: Hybrid evolutionary optimization with learning for production scheduling: state-of-the-art survey on algorithms and applications. Int. J. Prod. Res. 56, 193–223 (2018)

    Article  Google Scholar 

  25. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, New York (1994)

    Book  Google Scholar 

  26. Mittal, S., Vetter, J.S.: A survey of CPU-GPU heterogeneous computing techniques. ACM Comput. Surv. 47(4), 69.1–69.35 (2015)

    Article  Google Scholar 

  27. Moreno, J.J., Ortega, G., et al.: Improving the performance and energy of non-dominated sorting for evolutionary multiobjective optimization on GPU/CPU platforms. J. Glob. Optim. 71, 631–649 (2018)

    Article  MathSciNet  Google Scholar 

  28. Munawar, A., Wahib, M., et al.: Hybrid of genetic algorithm and local search to solve Max-Sat problem using nVidia CUDA framework. Genet. Program. Evol. Mach. 10(4), 391–415 (2009)

    Article  Google Scholar 

  29. Munawar, A., Wahib, M., et al.: arGA: adaptive resolution micro-genetic algorithm with tabu search to solve MINLP problems using GPU. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs (2013)

    Google Scholar 

  30. Nitisiri, K., Gen, M., Ohwada, H.: A parallel multi-objective genetic algorithm with learning-based mutation for railway scheduling. Comput. Ind. Eng. 130, 381–394 (2019)

    Article  Google Scholar 

  31. Ortega, G., Filatovas, E., et al.: Non-dominated sorting procedure for Pareto dominance ranking on multicore CPU and/or GPU. J. Glob. Optim. 69, 607–627 (2017)

    Article  MathSciNet  Google Scholar 

  32. Panwalkar, S.S., Iskander, W.: A survey of scheduling rules. Oper. Res. 25(1), 45–61 (1977)

    Article  MathSciNet  Google Scholar 

  33. Pedemonte, M., Alba, E., Luna, F.: Bitwise operations for GPU implementation of genetic algorithms. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 439–446 (2011)

    Google Scholar 

  34. Pedemonte, M., Luna, F., Albain, E.: New ideas in parallel metaheuristics on GPU: systolic genetic search. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs (2013)

    Google Scholar 

  35. Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems. Springer, NewYork (2016)

    Book  Google Scholar 

  36. Sato, Y., Hasegawa, N., Sato, M.: Acceleration of genetic algorithms for sudoku solution on many-core processors. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs (2013)

    Google Scholar 

  37. Schaffer, J.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100 (1985)

    Google Scholar 

  38. Sharma, D., Collet, P.: Implementation techniques for massively parallel multi-objective optimization. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs (2013)

    Google Scholar 

  39. Solomon, S., Thulasiraman, P., Thulasiram, R.K.: Scheduling using multiple swam particle optimization with memetic features on graphics processing units. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs (2013)

    Google Scholar 

  40. Solomon, S., Thulasiraman, P., Thulasiram, R.K.: ACO with tabu search on GPUs for fast solution of the QAP. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs (2013)

    Google Scholar 

  41. Tsutsui, S., Fujimoto, N.: An analytical study of parallel GA with independent runs on GPUs. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs (2013)

    Google Scholar 

  42. Wong, M.L.: Recent advances in multiobjective genetic algorithms for manufacturing scheduling problems. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 2515–2522 (2009)

    Google Scholar 

  43. Wong, M.L., Cui, G.: Data mining using parallel multi-objective evolutionary algorithms on graphics processing units. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs (2013)

    Google Scholar 

  44. Wong, M.L., Wong, T.: Implementation of parallel genetic algorithms on graphics processing units. In: Gen, M., et al. (eds.) Intelligent and Evolutionary Systems (2009)

    Google Scholar 

  45. Yu, X.J., Gen, M.: Introduction to Evolutionary Algorithms. Springer, London (2010)

    Book  Google Scholar 

  46. Zhang, J., Zhan, Z., et al.: Evolutionary computation meets machine learning: a survey. IEEE Comput. Intell. Mag. 6(4), 68–75 (2011)

    Article  Google Scholar 

  47. Zhang, W.Q., Gen, M., Jo, J.B.: Hybrid sampling strategy-based multiobjective evolutionary algorithm for process planning and scheduling problem. J. Intell. Manuf. 25(5), 881–897 (2014)

    Article  Google Scholar 

  48. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. TIK-Report 103, 95–100 (2001)

    Google Scholar 

  49. Cheng, J.R., Gen, M.: Parallel genetic algorithms with GPU computing. In: Industry 4.0 - Impact on Intelligent Logistics and Manufacturing, pp. 69–93 (2020)

    Google Scholar 

Download references

Acknowledgements

This work is partly supported by Grant-in-Aid for Scientific Res. (C) of Japan Society of Promotion of Sci. (JSPS: No. 19K12148) and Thailand National Science and Technology Scholarship.

Author information

Authors and Affiliations

  1. Fuzzy Logic Systems Institute, Kitakyushu, Japan

    Mitsuo Gen

  2. Tokyo University of science, Tokyo, Japan

    Mitsuo Gen

  3. Broad Geophysical Technology, Inc., 10630 Haddington Dr, Houston, TX, 77043, USA

    John R. Cheng

  4. Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand

    Krisanarach Nitisiri

  5. Graduate School of Science and Engineering, Tokyo University of Science, Tokyo, Japan

    Hayato Ohwada

Authors
  1. Mitsuo Gen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John R. Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krisanarach Nitisiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hayato Ohwada
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mitsuo Gen .

Editor information

Editors and Affiliations

  1. Business School, Sichuan University, Chengdu, China

    Jiuping Xu

  2. Academy of Sciences of Moldova, Chisinau, Moldova

    Gheorghe Duca

  3. Faculty of Math and Science, Brock University, Hamilton, ON, Canada

    Syed Ejaz Ahmed

  4. ETSI Industriales de Ciudad Real, University of Castile-La Mancha (UCLM), Ciudad Real, Spain

    Fausto Pedro García Márquez

  5. Institute of Control Systems, Azerbaijan Natl Academy of Sciences, Baku, Azerbaijan

    Asaf Hajiyev

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gen, M., Cheng, J.R., Nitisiri, K., Ohwada, H. (2020). Advances in Hybrid Genetic Algorithms with Learning and GPU for Scheduling Problems: Brief Survey and Case Study. In: Xu, J., Duca, G., Ahmed, S., García Márquez, F., Hajiyev, A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. https://doi.org/10.1007/978-3-030-49829-0_24

Download citation

Publish with us

Policies and ethics

Search

Navigation