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Evaluation of scatter-search approach for scheduling optimization of flexible manufacturing systems

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Abstract

Many optimization problems from the industrial engineering world (in particular manufacturing systems) are very complex in nature and quite hard to solve by conventional optimization techniques. There has been increasing interest to apply meta-heuristic methods to solve such kinds of hard optimization problems. In this work, a meta-heuristic approach called scatter-search (SS) is applied for scheduling optimization of flexible manufacturing systems by considering multiple objectives, i.e., minimizing the idle time of the machine and minimizing the total penalty cost for not meeting the due date concurrently. Scatter search (SS) contrasts with other evolutionary procedures in that it provides a wide exploration of the search space through intensification and diversification. In addition, it has a unifying principle for joining solutions and they exploit adaptive memory principle to avoid generating or incorporating duplicate solutions at various stages of the problem. In this paper, various meta-heuristic methods are used for solving three different sizes of scheduling problems taken from the literature. The results available for the various existing meta-heuristic methods are compared with results obtained by the scatter-search method. The proposed framework achieves better results for all the three problems and also achieves an average deviation of 16.67% from the best results obtained by other methods.

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References

  1. Giffler B, Thomson GL (1960) Algorithms for solving production scheduling problems. Int J Oper Res 8:487–503

    MATH  Google Scholar 

  2. Shankar K, Tzen YJ (1985) A loading and dispatching problem in a random flexible manufacturing system. Int J Prod Res 23:579–595

    Google Scholar 

  3. Lee SM, Jung HJ (1989) A multi-objective production planning model in a flexible manufacturing environment. Int J Prod Res 27(11):1981–1992

    Article  Google Scholar 

  4. Ghosh S, Gaimon C (1992) Routing flexibility and production scheduling in a flexible manufacturing system. Eur J Ophthalmol 60:344–364

    MATH  Google Scholar 

  5. Chisman JA (1986) Manufacturing cell: analytical setup times and part sequencing. Int J Adv Manuf Technol 1(5):55–60

    Article  Google Scholar 

  6. Greenberg HH (1968) A branch and bound solution to the general scheduling problem. Int J Oper Res 16:353–361

    Article  MATH  Google Scholar 

  7. Toker A, Kondakci S, Erkip N (1994) Job shop scheduling under a non-renewable resource constraint. J Oper Res Soc 45(8):942–947

    Article  MATH  Google Scholar 

  8. Hoitomt DJ, Luh PB, Pattipati KR (1993) A practical approach to job-shop scheduling problems. IEEE Trans Robot Autom 9(1):1–13

    Article  Google Scholar 

  9. Steeke KE, Soldberg JJ (1982) Loading and control policies for a flexible manufacturing system. Int J Prod Res 19(5):481–490

    Google Scholar 

  10. Chan TS, Pak HA (1986) Heuristically job allocation in a flexible manufacturing system. Int J Adv Manuf Technol 1(2):69–90

    Article  Google Scholar 

  11. He W, Kusiak A (1992) Scheduling of manufacturing systems. Int J Comput Industry 20:163–175

    Article  Google Scholar 

  12. Lee DY, Dicesare F (1994) Scheduling of flexible manufacturing systems: using Petri nets and heuristic search. IEEE Trans Robot Autom 10(2):23–132

    Google Scholar 

  13. Beigel JE, Davern JJ (1990) Genetic algorithms and job shop scheduling. Int J Comput Ind Eng 19(1–4):81–90

    Article  Google Scholar 

  14. Sridhar J, Rajendran C (1994) A genetic algorithm for family and job scheduling in a flow line based manufacturing cell. Proc 16th Int Conf Computers & IE, pp 337–340, March 7–9, 1994

  15. Kopfer H, Mattfield DC (1997) A hybrid search algorithm for the job-shop problem. In: Proceedings of the First International Conference on Operations and Quantitative Management 2:498–505, Jan 5–8, Jaipur, India

  16. Shaw MJ, Whinston AB (1989) An artificial intelligence approaches to the scheduling of flexible manufacturing systems. IIE Trans 21:170–182

    Google Scholar 

  17. Schultz J, Mertens P (1997) A comparison between an expert system, a GA and priority for production scheduling. In: Proceedings of the First International Conference on Operations and Quantitative Management 2:506–513, Jan 5–8, Jaipur, India

  18. Singh K, Bochynek R (1997) Comparison of heuristic search methods for sequencing problems: a computational study. In: Proceedings of the First International Conference on Operations and Quantitative Management 2:451–458, Jan 5–8, Jaipur, India

  19. Chan FTS, Chan HK (2001) Dynamic scheduling for a flexible manufacturing system-the preemptive approach. Int J Adv Manuf Syst 17(10):760–768

    Google Scholar 

  20. Chan et al (2002) The state of art in simulation study on FMS scheduling: a comprehensive survey. Int J Adv Manuf Syst 19:830–849

  21. Saygin et al (1999) Integrating flexible process plans with scheduling in flexible manufacturing system. Int J Adv Manuf Syst 15(4):268–280

  22. Lun et al (2000) Holonic concept-based methodology for part routing on flexible manufacturing system. Int J Adv Manuf Syst 18(18):483–490

  23. Saravana Sankar S, Ponnambalam SG (2001) Scheduling optimization of FMS using genetic algorithm with different coding schemes. 1st International Conference on Logistics and Supply Chain Management, PSG College of Technology, India

  24. Saravana Sankar S, Ponnambalam SG, Rajendran C (2003) A multi- objective genetic algorithm for scheduling optimization of flexible manufacturing system. Int J Adv Manuf Technol 22:229–236

    Article  Google Scholar 

  25. Jerald J et al (2005) Scheduling optimization of flexible manufacturing system using particle swarm optimization algorithm. Int J Adv Manuf Technol pp 964–971

  26. Glover F (1998) A template for scatter search and path relinking. In: Hao J-K, Lutton E, Ronald E, Schoenauer M, Snyers D (eds) Artificial evolution. Lecture notes in computer science, vol. 1363, Springer, Berlin Heidelberg New York, pp 13–54

    Google Scholar 

  27. Glover F, Laguna M, Marti R (2000) Fundamentals of scatter search and path relinking. Control Cybern 39(3):653–684

    MathSciNet  Google Scholar 

  28. Laguna M, Martí R, Campos V (1998) Intensification and diversification with elite tabu search solutions for the linear ordering problem. ComputOper Res (in press)

  29. Reeves CR, Yamada T (1998) Genetic algorithms, path relining and the flow shop sequencing problem. Evol Comput J 6(1):230

    Article  Google Scholar 

  30. Noorul haq A, Saravanan M et al (2007) A scatter search algorithm for general flowshop scheduling problem. Int J Adv Manuf Technol 31:731–736

    Google Scholar 

  31. Singh Jain A, Meeran S (2002) A multi -level hybrid framework applied to the general flow-shop scheduling problem. Comput Oper Res 29:1873–1901

    Article  Google Scholar 

  32. Reeves CR, Yamada T (1998) Genetic algorithms, path relinking and the flowshop sequencing problem. Evol Comput 6:45–60

    Article  Google Scholar 

  33. Saravana Sankar S, Ponnambalam SG (2004) An advanced scatter search technique for scheduling optimization of FMS. 21st AIMTDR2004. All India manufacturing technology, design and research conference at Vellore Institute of Technology, Vellore, Dec. 20–22, India

  34. Laguna M, Barnes JW, Glover F (1993) Intelligent scheduling with tabu search: an application to jobs with linear delay penalties and sequence-dependent setup costs and times. J Appl Intel 3:159–172

    Article  Google Scholar 

  35. Barnes JW, Vaston LK (1981) Scheduling jobs with linear delay penalties and sequence dependent setup costs. Oper Res 29:146–160

    MATH  MathSciNet  Google Scholar 

  36. Yamada T, Nakana R (1996) Scheduling by genetic local search with multi-setup crossover. 4th Int Conf Parallel Problem Solving from Nature, pp 960–969

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Authors and Affiliations

  1. Department of Mechanical Engineering, RVS College of Engineering & Technology, Dindigul, Tamil Nadu, 624005, India

    M. Saravanan

  2. Department of Production Engineering, National Institute of Technology, (Deemed University), Tiruchirappalli, Tamil Nadu, 620015, India

    A. Noorul Haq

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  1. M. Saravanan
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  2. A. Noorul Haq
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Correspondence to M. Saravanan.

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Saravanan, M., Noorul Haq, A. Evaluation of scatter-search approach for scheduling optimization of flexible manufacturing systems. Int J Adv Manuf Technol 38, 978–986 (2008). https://doi.org/10.1007/s00170-007-1134-5

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  • DOI: https://doi.org/10.1007/s00170-007-1134-5

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