Meta-heuristics for Real-time Routing Selection in Flexible Manufacturing Systems

Abstract

Most studies in real-time flexible manufacturing system (FMS) scheduling and control areas do not consider the effect of routing flexibility; their focus is typically on use of scheduling (i.e., dispatching) rules based on routing selection carried out prior to production. Such an approach is not applicable to random-type FMS, in which no knowledge about incoming part types is available prior to production. For such a scenario, parts can have alternative routings, even for parts of the same type. Thus, the control system of a random-type FMS requires the capability to adapt to the randomness in arrivals and other unexpected events in the system by effectively using operation and routing flexibility in real time. In this chapter, the objective is to present a comparative study of a group of meta-heuristics, including taboo search, ant colony optimization, genetic algorithms, particle swarm optimization, electromagnetic meta-heuristic, and simulated annealing, against the modified dissimilarity maximization method (modified DMM). DMM (Saygin and Kilic, 1999) is an alternative process plan selection method originally proposed for the routing selection in off-line scheduling of an FMS. In subsequent studies (Saygin et al., 2001; Saygin and Kilic, 2004) DMM has been: (i) used as a real-time decision-making tool to select routings for the parts that are in the system, (ii) tested and benchmarked against first-in-first-out/first available and equal probability loading. Based on the DMM model, a modified DMM (Hassam and Sari, 2007) is developed for selection of alternative routings in real time in an FMS. Modified DMM improves the performance of the FMS in terms of higher production rate, and higher utilization rate of the machines and the material handling system.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Basnet C, Mize JH (1994) Scheduling and control of flexible manufacturing systems: a critical review. Int. Comput. Integr. Manuf., 7(6):340–355CrossRefGoogle Scholar
  2. Birbil SI, Fang S (2003) An electromagnetism like mechanism for global optimization. J. Glob. Opt., 25:263–282MATHCrossRefMathSciNetGoogle Scholar
  3. Blum C, Roli A (2003) Meta-heuristics in combinatorial optimization: overview and conceptual Comparison. ACM Comput. Survey, 35:268–308CrossRefGoogle Scholar
  4. Byrne MD, Chutima P (1997) Real-time operational control of an FMS with full routing flexibility. Int. J. Prod. Econ., 51:109–113CrossRefGoogle Scholar
  5. Chen S, Chang P, Chan C et al. (2007) A hybrid electromagnetism-like algorithm for single machine scheduling problem. In D.-S. Huang et al. (eds) Advanced intelligent computing theories and applications. with aspects of artificial intelligence. SpringerGoogle Scholar
  6. Cho H, Wysk RA (1995) Intelligent workstation controller for computer-integrated manufacturing: problems and models. J. Manuf. Syst., 14(4):252–263CrossRefGoogle Scholar
  7. Das SK, Nagendra P (1997) Selection of routes in a flexible manufacturing facility. Int. J. Prod. Econ., 48:237–247CrossRefGoogle Scholar
  8. Dorigo M (1992) Optimization, learning and natural algorithms, PhD thesis, DEI, Politecnico di Milano, ItalyGoogle Scholar
  9. Dréo J, Pétrowski A, Siarry P et al. (2003) Métaheuristiques pour l'optimisation difficile. EyrollesGoogle Scholar
  10. Eberhart RC, Kennedy J (1995) New optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39–43Google Scholar
  11. Garey MR, Johson DS (1979) Computers and intractability a guide of the theory of NP-completeness. W.H. Freeman and Company, San Francisco, USAMATHGoogle Scholar
  12. Glover F, Greenberg HJ (1989) New approaches for heuristic search: A bilateral linkage with artificial intelligence. Eur. J. Oper. Res., 39:119–130MATHCrossRefMathSciNetGoogle Scholar
  13. Glover F, Laguana M (1997) Tabu search. article principally adapted. In Glover and Laguana (eds) Tabu search. Kluwer AcademicGoogle Scholar
  14. Goldberg EE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Reading, MAMATHGoogle Scholar
  15. Gupta Y P, Gupta MC, Bector CR (1989) A review of scheduling rules in flexible manufacturing systems. Int. J. Comp. Integr. Manuf., 2:356–377CrossRefGoogle Scholar
  16. Hassam A, Sari Z (2007) Real-time selection of process plan in flexible manufacturing systems: Simulation study. In: Proceedings of the International Conference on Industrial Engineering and Systems Management, Beijing, ChinaGoogle Scholar
  17. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan PressGoogle Scholar
  18. Ishii N, Muraki M (1996) A process-variability-based on-line scheduling system in multi product batch process. Comput. Chemi. Eng., 20:217–234CrossRefGoogle Scholar
  19. Kazerooni A, Chan FTS, Abhary K (1997) A fuzzy integrated decision-making support system for scheduling of FMS using simulation. Comput. Integr. Manuf. Syst., 10(1):27-34CrossRefGoogle Scholar
  20. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science, 220(4598):671–680CrossRefMathSciNetGoogle Scholar
  21. Kouiss K, Pierreval H, Mebarki N (1997) Using multi-agent architecture in FMS for dynamic scheduling. J. Intell. Manuf., 8:41–47CrossRefGoogle Scholar
  22. Liu J, MacCarthy BL (1997) A goal MILP model for FMS scheduling. Eur. J. Oper. Res., 100: 441–453MATHCrossRefGoogle Scholar
  23. Metropolis N, Rosenbluth AW, Rosenbluth MN et al. (1953) Equations of state calculations by fast computing machines. J. Chem. Phys., 21:1087–1091CrossRefGoogle Scholar
  24. Nof S, Barash M, Solberg J (1979) Operational control of item flow in versatile manufacturing system. Int. J. Prod. Res., 17:479–489CrossRefGoogle Scholar
  25. Pan QK, Tasgetiren MF, Liang YC (2005) A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem with makespan criterion. In: Proceedings of the International Workshop on Planning and Scheduling Special Interest group, UK PLANSIG2005. City University, London, pp. 31–41Google Scholar
  26. Peng C, Chen FF (1998) Real-time control and scheduling of flexible manufacturing systems: a simulation based ordinal optimization approach. Int. J. Adv. Manuf. Tech., 14(10):775–786CrossRefGoogle Scholar
  27. Rachamadugu R, Stecke KE (1994) Classification and review of FMS scheduling procedures. Prod. Plan. Control, 5:2–20CrossRefGoogle Scholar
  28. Saygin C, Kilic SE (2004) Dissimilarity maximization method for real-time routing of parts in random flexible manufacturing systems. Int. J. Flex. Manuf., Syst., 16(2):169–182MATHCrossRefGoogle Scholar
  29. Saygin C, Chen FF, Singh J (2001) Real-time manipulation of alternative routings in flexible manufacturing systems: A simulation study. Int. J. of Adv. Manuf. Tech., 18:755–763CrossRefGoogle Scholar
  30. Saygin C, Kilic SE (1996) Effect of flexible process plans on performance of flexible manufacturing systems. In: Proceedings of 7th International DAAM symposium, Vienna, Austria, pp.. 393–394Google Scholar
  31. Saygin C, Kilic SE (1997) Scheduling of flexible manufacturing system. In: Proceedings of MicroCAD 97 Conference, University of Miskolc, Hungary, vol. H, pp. 19–23Google Scholar
  32. Saygin C, Kilic SE (1999) Integrating flexible manufacturing systems with scheduling in flexible manufacturing system. Int. J. Adv. Manuf. Tech., 15(4):268–280CrossRefGoogle Scholar
  33. Saygin C, Kilic SE, Toth T et al. (1995) On scheduling approaches of flexible manufacturing systems: gap between theory and practice. In: Proceedings of the 3rd IFAC/IFIP/IFORS Workshop – Intelligent Manufacturing Systems 95, Pergamon, pp. 61–66Google Scholar
  34. Shukla C S, Chen FF (1996) The state of the art in intelligent real-time FMS control: a comprehensive survey. J. Intell. Manuf., 7:441–455CrossRefGoogle Scholar
  35. Wu SYD, Wysk RA (1989) An application of discrete event simulation to on-line control and scheduling in flexible manufacturing. Int. J. Prod. Res., 27:1603–1623CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  1. 1.LATAboubekr Belkaïd University of TlemcenTlemcenAlgeria

Personalised recommendations