Genetic algorithms for solving multiprocessor scheduling problems

  • Yasuhiro Tsujimura
  • Mitsuo Gen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1285)


Multiprocessor Scheduling Problems (MSP) are generally difficult to solve due to precedence relations between the tasks and complexity of the task graph. This problem is extremely difficult to solve and generally intractable, however, it is well known that some relaxed or simplified subproblems constructed from the original scheduling problems by imposing a variety of restricting conditions still fall into the class of NP-hard problems.

In this paper we propose a new approach using Genetic Algorithm (GA) to solve the MSP, i.e., to find a schedule that minimizes the total elapsed time for executing the tasks in the processors. We introduce the concept of the height function and new genetic operators, and discuss the efficiency of the proposed method using a numerical example.


Multiprocessor Scheduling Problem (MSP) Genetic Algorithm (GA) Height Function 


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  1. 1.
    Adam, T. L., K. M. Chandy and J. R. Dickson: A Comparison of List Schedules for Parallel Processing Systems, Commun. Ass. Comput. Mach., Vol.17, pp.685–690, 1974.Google Scholar
  2. 2.
    Bernstein, A. J.: Analysis of Programs for Parallel Processing, IEEE Trans. Electron. Comput., Vol.15, pp.757–763, 1966.Google Scholar
  3. 3.
    Coffman, E. G.: Computer and Job-shop Scheduling Theory, New York, John Wiley & Sons, 1976.Google Scholar
  4. 4.
    Dhodhi, M. K., I. Ahmad and I. Ahmad: A Multiprocessor Scheduling Scheme Using Problem-Space Genetic Algorithms, Proc. of 1995 IEEE Inter. Conf. on Evolutionary Copmputation, Vol.l, pp.214–219, 1995.Google Scholar
  5. 5.
    Gen, M., Y. Tsujimura and E. Kubota: Genetic Algorithm for Multiprocessor Scheduling Problems, Proc. of 10th Fuzzy System Symposium, pp.43–46, 1994.(in Japanese)Google Scholar
  6. 6.
    Gen, M. and R. Cheng: Genetic Algorithms and Engineering Design, John Wiley & Sons, 1996.Google Scholar
  7. 7.
    Graham, R. L.: Bounds for Certain Multiprocessing Timing Anomalies, Bell System Technology Journal, Vol.45, pp.1563–1581, 1966.Google Scholar
  8. 8.
    Grimson, W. E. L.: On Recognition of Curved Objects, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.ll, No.6, pp.632–643, 1989.Google Scholar
  9. 9.
    Hou, E. S. H., H. Ren and N. Ansari: Efficient Multiprocessor Scheduling Based on Genetic Algorithms, Dynamic, Genetic, and Chaotic Programming, Branko Soucek and the IRIS Group, John Wiley and Sons, 1992.Google Scholar
  10. 10.
    Hu, T. C.: Parallel Sequencing and Assembly Line Problems, Operations Research, Vol.9, pp.841–848, 1961.Google Scholar
  11. 11.
    Kasahara, H. and S. Narita: Practical Multiprocessor Scheduling Algorithms for Efficient Parallel Processing, IEEE Trans. on Computers, Vol.33, No.11, pp.1023–1029, 1984.Google Scholar
  12. 12.
    Kasahara, H. and S. Narita: Parallel Processing of Robot-Arm Control Corrrputation on a Multimicroprocessor System, IEEE Trans. on Robotics and Automation, Vol.l, No.2, pp.104–113, 1985.Google Scholar
  13. 13.
    Michalewicz, Z.,: Genetic Algorithms + Data Structures = Evolution Programs 2nd ed., Springer-Verlag, 1994.Google Scholar
  14. 14.
    Muntz, R. R. and E. G. Coffman: Optimal Preemptive Scheduling on TwoProcessor Systems, IEEE Trans. on Computers, Vol.18, pp.1014–1020, 1969.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Yasuhiro Tsujimura
    • 1
  • Mitsuo Gen
    • 1
  1. 1.Department of Industrial and Systems EngineeringAshikaga Institute of TechnologyAshikagaJapan

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