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Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems

  • Jun-Qing LiEmail author
  • Quan-Ke Pan
  • Kai-Zhou Gao
ORIGINAL ARTICLE

Abstract

This paper presents a hybrid Pareto-based discrete artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each solution corresponds to a food source, which composes of two components, i.e., the routing component and the scheduling component. Each component is filled with discrete values. A crossover operator is developed for the employed bees to learn valuable information from each other. An external Pareto archive set is designed to record the non-dominated solutions found so far. A fast Pareto set update function is introduced in the algorithm. Several local search approaches are designed to balance the exploration and exploitation capability of the algorithm. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

Keywords

Flexbile job shop scheduling problem Discrete artificial bee colony Multi-objective optimization Crossover operator 

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References

  1. 1.
    Jain AS, Meeran S (1998) Deterministic job-shop scheduling: past, present and future. Eur J Oper Res 113(2):390–434CrossRefGoogle Scholar
  2. 2.
    Garey MR, Johnson DS, Sethi R (1976) The complexity of flowshop and job shop scheduling. Math Oper Res 1(2):117–129zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Brandimarte P (1993) Routing and scheduling in a flexible job shop by tabu search. Ann Oper Res 22:158–183Google Scholar
  4. 4.
    Saidi-mehrabad M, Fattahi P (2007) Flexible job shop scheduling with tabu search algorithms. Int J Adv Manuf Technol 32:563–570CrossRefGoogle Scholar
  5. 5.
    Ennigrou M, Ghedira K (2008) New local diversification techniques for flexible job shop scheduling problem with a multi-agent approach. Auton Agent Multi-Ag 17:270–287CrossRefGoogle Scholar
  6. 6.
    Gao L, Peng CY, Zhou C, Li PG (2006) Solving flexible job shop scheduling problem using general particle swarm optimization. In: Proceedings of the 36th CIE Conference on Computers & Industrial Engineering, Taipei, China, June 20–23, 2006, pp. 3018–3027Google Scholar
  7. 7.
    Liouane N, Saad I, Hammadi S, Borne P (2007) Ant systems & local search optimization for flexible job-shop scheduling production. Int J Comput Commun Control 2:174–184Google Scholar
  8. 8.
    Ho NB, Tay JC, Lai EMK (2007) An effective architecture for learning and evolving flexible job-shop schedules. Eur J Oper Res 179(2):316–333zbMATHCrossRefGoogle Scholar
  9. 9.
    Pezzella F, Morganti G, Ciaschetti G (2008) A genetic algorithm for the flexible job-shop scheduling problem. Comput Oper Res 35:3202–3212zbMATHCrossRefGoogle Scholar
  10. 10.
    Gao J, Sun L, Gen M (2008) A hybrid genetic, variable neighborhood descent algorithm for flexible job shop scheduling problems. Comput Oper Res 35(9):2892–2907zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Yazdani M, Amiri M, Zandieh M (2010) Flexible job-shop scheduling with parallel variable neighborhood search algorithm. Expet Syst Appl 37:678–687CrossRefGoogle Scholar
  12. 12.
    Xing LN, Chen YW, Wang P, Zhao QS, Xiong J (2010) A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl Soft Comput 10:888–896CrossRefGoogle Scholar
  13. 13.
    Bagheri A, Zandieh M, Mahdavi I, Yazdani M (2010) An artificial immune algorithm for the flexible job-shop scheduling problem. Future Gener Comput Syst 26:533–541CrossRefGoogle Scholar
  14. 14.
    Kacem I, Hammadi S, Borne P (2002) Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Math Comput Simul 60:245–276zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Kacem I, Hammadi S, Borne P (2002) Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems. IEEE T Syst Man Cy C, Part C 32(1):408–419Google Scholar
  16. 16.
    Xia WJ, Wu ZM (2005) An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Comput Ind Eng 48(2):409–425CrossRefMathSciNetGoogle Scholar
  17. 17.
    Zhang GH, Shao XY, Li PG, Gao L (2009) An effective hybrid swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput Ind Eng 56(4):1309–1318CrossRefGoogle Scholar
  18. 18.
    Ho NB, Tay JC (2008) Solving multiple-objective flexible job shop problems by evolution and local search. IEEE T Syst Man Cy C, Part C 38(5):674–685CrossRefGoogle Scholar
  19. 19.
    Xing LN, Chen YW, Yang KW (2009) An efficient search method for multi-objective flexible job shop scheduling problems. J Intell Manuf 20:283–293CrossRefGoogle Scholar
  20. 20.
    Li JQ, Pan QK, Liang YC (2010) An effective hybrid tabu search algorithm for multi-objective flexible job shop scheduling problems. Comput Ind Eng 59(4):647–662CrossRefGoogle Scholar
  21. 21.
    Chan FTS, Chung SH, Chan LY, Finke G, Tiwari MK (2006) Solving distributed FMS scheduling problems subject to maintenance: genetic algorithms approach. Robot Cim-Int Manuf 22:493–504CrossRefGoogle Scholar
  22. 22.
    Gao J, Gen M, Sun LY (2006) Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm. J Intell Manuf 17:493–507CrossRefGoogle Scholar
  23. 23.
    Wang SJ, Yu JB (2010) An effective heuristic for flexible job-shop scheduling problem with maintenance activities. Comput Ind Eng 59:436–447CrossRefGoogle Scholar
  24. 24.
    Chan FTS, Wong TC, Chan LY (2006) Flexible job-shop scheduling problem under resource constraints. Int J Prod Res 44(11):2071–2089zbMATHCrossRefGoogle Scholar
  25. 25.
    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, Technical Report TR06. Computer Engineering Department, Erciyes University, TurkeyGoogle Scholar
  26. 26.
    Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–171zbMATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697CrossRefGoogle Scholar
  28. 28.
    Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132zbMATHCrossRefMathSciNetGoogle Scholar
  29. 29.
    Pan QK, Tasgetiren MF, Suganthan PN, Chua TJ (2010) A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Inf Sci. doi: 10.1016/j.ins.2009.12.025 Google Scholar
  30. 30.
    Li JQ, Pan QK, Suganthan PN, Chua TJ (2010) A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem. Int J Adv Manuf Technol. doi: 10.1007/s00170-010-2743-y Google Scholar
  31. 31.
    Pan QK, Wang L, Qian B (2009) A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems. Comput Oper Res 36(8):2498–2511zbMATHCrossRefMathSciNetGoogle Scholar
  32. 32.
    Deb K, Paratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-Π. IEEE T Evolut Comput 6(2):182–197CrossRefGoogle Scholar
  33. 33.
    Wang L (2003) Shop scheduling with genetic algorithms. Tsinghua university press, BeijingGoogle Scholar
  34. 34.
    Ho NB, Tay JC (2004) GENACE: an efficient cultural algorithm for solving the flexible job-shop problem. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC2004) Piscataway, pp. 1759–1766Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.College of Computer ScienceLiaocheng UniversityLiaochengPeople’s Republic of China
  2. 2.State Key Lab. of Digital Manufacturing Equipment & Technology in Huazhong University of Science & TechnologyWuhanPeople’s Republic of China

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