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Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm

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Abstract

The increased use of flexible manufacturing systems (FMS) to efficiently provide customers with diversified products has created a significant set of operational challenges. Although extensive research has been conducted on design and operational problems of automated manufacturing systems, many problems remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and automated guided vehicle (AGV) routings. The FMS scheduling problem has been tackled by various traditional optimisation techniques. While these methods can give an optimal solution to small-scale problems, they are often inefficient when applied to larger-scale problems. In this work, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives, i.e., minimising the idle time of the machine and minimising the total penalty cost for not meeting the deadline concurrently. The memetic algorithm presented here is essentially a genetic algorithm with an element of simulated annealing. The results of the different optimisation algorithms (memetic algorithm, genetic algorithm, simulated annealing, and particle swarm algorithm) are compared and conclusions are presented .

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References

  1. 1.

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

  2. 2.

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

  3. 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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 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

  8. 8.

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

  9. 9.

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

  10. 10.

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

  11. 11.

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

  12. 12.

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

  13. 13.

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

  14. 14.

    Sridhar J, Rajendran C (1994) A genetic algorithm for family and job scheduling in a flow line based manufacturing cell. In: Proceedings of the 16th international conference on computers and IE, location, 7–9 March 1994, pp 337–340

  15. 15.

    Kopfer H, Mattfield DC (1997) A hybrid search algorithm for the job-shop problem. In: Proceedings of the 1st international conference on operations and quantitative management, Jaipur, India, 5–8 January 1997, 2:498–505

  16. 16.

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

  17. 17.

    Schultz J, Mertens P (1997) A comparison between an expert system, a GA and priority for production scheduling. In: Proceedings of the 1st international conference on operations and quantitative management, Jaipur, India, 5–8 January 1997, 2:506–513

  18. 18.

    Singh K, Bochynek R (1997) comparison of heuristic search methods for sequencing problems – a computational study. In: Proceedings of the 1st international conference on operations and quantitative management, Jaipur, India, 5–8 January 1997, 2:451–458

  19. 19.

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

  20. 20.

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

  21. 21.

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

  22. 22.

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

  23. 23.

    Kennady J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, location, day month 1995, 4:1942–1948

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Correspondence to J. Jerald.

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Jerald, J., Asokan, P., Prabaharan, G. et al. Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm. Int J Adv Manuf Technol 25, 964–971 (2005). https://doi.org/10.1007/s00170-003-1933-2

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Keywords

  • Flexible manufacturing system
  • Genetic algorithm
  • Memetic algorithm
  • Particle swarm algorithm
  • Scheduling
  • Simulated annealing