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A meta-heuristic approach to single machine scheduling problems

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Abstract

A new meta-heuristic evolutionary algorithm, named a memetic algorithm, for solving single machine total weighted tardiness scheduling problems is presented in this paper. Scheduling problems are proved to be NP-hard (Non-deterministic polynomial-time hard) types of problems and they are not easily or exactly solved for larger sizes. Therefore, application of the meta-heuristic technique to solve such NP hard problems is pursued by many researchers. The memetic algorithm is a marriage between population-based global searches with local improvement for each individual. The algorithm is tested with benchmark problems available in the OR (operations research) library. The results of the proposed algorithm are compared with the best available results and were found to be nearer to optimal. The memetic algorithm performs better than the heuristics like earliest due date and modified due date.

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References

  1. Baker KR (1974) An introduction to sequencing and scheduling. Wiley, New York

  2. Bahram A, Ramakrishnan KR (1996) A computational experiment of covert-AU class of rules for single machine tardiness scheduling problems. Comput Ind Eng 32(2):201–209

    Google Scholar 

  3. Lenstra JK, Rinnooy Kan AHG, Brucker P (1977). Complexity on machine scheduling problems. Ann Discr Math 1:331–342

    Google Scholar 

  4. Lawler EL, Lenstra JK, Rinnooy Kan AHG, Shmoys DB (1993) Sequencing and scheduling: algorithms and complexity. In: Graves SC, Rinnooy Kan AHG, Zipkin PH (eds) Logistics of production and inventory: hand books in OR & M.S., vol 4. Elsevier, Amsterdam, pp 445–522

  5. Du J, Leung JYT (1990) Minimizing total tardiness on one processor in np-hard. Math Oper Res 15:483–495

    Google Scholar 

  6. Dawkins R (1976) The selfish gene. Oxford University Press, Oxford

  7. Merz P, Freisleben B (1999) A comparision of memetic algorithm, Tabu search and ant colonies for the quadratic assignment problem. In: Proceedings of the 1999 Congress on Evolutionary Computations, Washington D.C., pp 2063–2070

  8. Merz P, Freisleben B (2000) Fitness landscapes, memetic algorithms and greedy operators for graph bipartitioning. Evol Comput 8(1):61–91

    Article  CAS  PubMed  Google Scholar 

  9. Moscato P., Norman MG (1992) A memetic approach for the travelling salesman problem — implementation of a computational ecology for combinatorial optimisation on message-passing system. In: Proceedings of the International Conference on Parallel Computing and Transputer Applications, IOS Press, Amsterdam, pp 177–186

  10. Burke EK, Newall JP, Weare RF (1996) A memetic algorithm for university exam timetabling. In: Burke EK, Ross P (eds) The practise and theory of automated timetabling. Lecture notes in Computer Sciences, vol 1153. Springer, Berlin Heidelberg New York, pp 211–250

  11. Burke EK, Newall JP (1999) A multi-stage evolutionary algorithm for the time table problem. IEEE Trans Evol Comput 3(1):63–74

    Article  Google Scholar 

  12. Burke EK, Smith AJ (2000) hybrid evolutionary techniques for the maintenance scheduling problem. IEEE Trans Power Eng Soc 15(1):122–128

    Article  Google Scholar 

  13. Franca PM, Mendes A, Moscato P (2001) A memetic algorithm for the total tardiness single machine scheduling problem. Eur J Oper Res 132(1):224–242

    Article  Google Scholar 

  14. Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, MA

  15. Starkweather T, McDaniel S, Whitley C, Mathias K, Whitley D (1991) A comparison of genetic sequencing operators. In proceedings of the 4th International Conference on Genetic Algorithms, San Diego, CA, pp 69–76. Morgan Kaufmann, San Francisco

  16. Congram RK, Potts CN, Van de Velde S (2002) An iterated dynasearch algorithm for the single-machine total weighted tardiness scheduling problems. J Comput 14(1):52–67

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi, Mepco Nager, 626 005, Tamilnadu, India

    R. Maheswaran

  2. School of Engineering and Science, Monash University Malaysia, 46150, Petaling Jaya, Selangor, Malaysia

    S.G. Ponnambalam

  3. Department of Information Technology, SSN College of Engineeering, Chennai, 603 110, Tamilnadu, India

    C. Aravindan

Authors
  1. R. Maheswaran
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  2. S.G. Ponnambalam
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  3. C. Aravindan
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Corresponding author

Correspondence to S.G. Ponnambalam.

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Maheswaran, R., Ponnambalam, S. & Aravindan, C. A meta-heuristic approach to single machine scheduling problems. Int J Adv Manuf Technol 25, 772–776 (2005). https://doi.org/10.1007/s00170-003-1864-y

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  • DOI: https://doi.org/10.1007/s00170-003-1864-y

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