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A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem

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Abstract

A novel hybrid tabu search algorithm with a fast public critical block neighborhood structure (TSPCB) is proposed in this paper to solve the flexible job shop scheduling problem with the criterion to minimize the maximum completion time (makespan). First, a mix of four machine assignment rules and four operation scheduling rules is developed to improve the quality of initial solutions to empower the hybrid algorithm with good exploration capability. Second, an effective neighborhood structure to conduct local search in the machine assignment module is proposed, which integrates three adaptive approaches. Third, a speedup local search method with three kinds of insert and swap neighborhood structures based on public critical block theory is presented. With the fast neighborhood structure, the TSPCB algorithm can enhance its exploitation capability. Simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is concluded that the proposed TSPCB algorithm is superior to several recently published algorithms in terms of solution quality, convergence ability, and efficiency.

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References

  1. Nuijten WPM, Aarts EHL (1996) A computational study of constraint satisfaction for multiple capacitated job shop scheduling. Eur J Oper Res 90(2):269–284

    Article  MATH  Google Scholar 

  2. Jain AS, Meeran S (1998) Deterministic job-shop scheduling: past, present and future. Eur J Oper Res 113(2):390–434

    Article  Google Scholar 

  3. Garey MR, Johnson DS, Sethi R (1996) The complexity of flowshop and job shop scheduling. Math Oper Res 1(2):117–129

    Article  MathSciNet  Google Scholar 

  4. Bruker P, Schlie R (1990) Job-shop scheduling with multi-purpose machines. Computing 45(4):369–375

    Article  MathSciNet  Google Scholar 

  5. Brandimarte P (1993) Routing and scheduling in a flexible job shop by tabu search. Ann Oper Res 41:157–183

    Article  MATH  Google Scholar 

  6. Saidi-mehrabad M, Fattahi P (2007) Flexible job shop scheduling with tabu search algorithms. Int J Adv Manuf Technol 32:563–570

    Article  Google Scholar 

  7. Fattahi P, Saidi M, Jolai F (2007) Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. J Intell Manuf 18:331–342

    Article  Google Scholar 

  8. Ennigrou M, Ghedira K (2008) New local diversification techniques for flexible job shop scheduling problem with a multi-agent approach. Auton Agent Multi-Ag 17:270–287

    Article  Google Scholar 

  9. 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 60:245–276

    Article  MATH  MathSciNet  Google Scholar 

  10. Gao L, Peng CY, Zhou C, Li PG (2006) Solving flexible job shop scheduling problem using general particle swarm optimization. Proceedings of the 36th CIE Conference on Computers & Industrial Engineering 3018-3027

  11. Xia WJ, Wu ZM (2005) An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Comput Ind Eng 48(2):409–425

    Article  MathSciNet  Google Scholar 

  12. Liu HB, Abraham A, Grosan C (2007) A novel variable neighborhood particle swarm optimization for multi-objective flexible job-shop scheduling Problems. Proceeding of the second IEEE International Conference on Digital Information Management (ICDIM’2007). Lyon, France, pp 138–145

    Google Scholar 

  13. Zhang GH, Shao XY, Li PG, Gao L (2009) An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput Ind Eng 56(4):1309–1318

    Article  Google Scholar 

  14. Ho NB, Tay JC, Lai EMK (2007) An effective architecture for learning and evolving flexible job-shop schedules. Eur J Oper Res 179(2):316–333

    Article  MATH  Google Scholar 

  15. Pezzella F, Morganti G, Ciaschetti G (2008) A genetic algorithm for the flexible job-shop scheduling problem. Comput Oper Res 35:3202–3212

    Article  MATH  Google Scholar 

  16. 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 10:888–896

    Article  Google Scholar 

  17. Yazdani M, Amiri M, Zandieh M (2010) Flexible job-shop scheduling with parallel variable neighborhood search algorithm. Expet Syst Appl 37:678–687

    Article  Google Scholar 

  18. Hurink E, Jurisch B, Thole M (1994) Tabu search for the job shop scheduling problem with multi-purpose machines. Oper Res Spektrum 15:205–215

    Article  MATH  MathSciNet  Google Scholar 

  19. Dauzere-Peres S, Paulli J (1997) An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Ann Oper Res 70:281–306

    Article  MATH  MathSciNet  Google Scholar 

  20. Mastrolilli M, Gambardella LM (2000) Effective neighborhood functions for the flexible job shop problem. J Sched 3(1):3–20

    Article  MATH  MathSciNet  Google Scholar 

  21. Glover F (1990) Tabu search: a tutorial. Interfaces 20(4):74–94

    Article  Google Scholar 

  22. Vilcot G, Billaut JC (2008) A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem. Eur J Oper Res 190:398–411

    Article  MATH  MathSciNet  Google Scholar 

  23. Pezzella F, Merelli E (2000) A tabu search method guided by shifting bottleneck for the job shop scheduling problem. Eur J Oper Res 120:297–310

    Article  MATH  MathSciNet  Google Scholar 

  24. Kim CO, Shin HJ (2003) Scheduling jobs on parallel machines: a restricted tabu search approach. Int J Adv Manuf Technol 22:278–287

    Article  Google Scholar 

  25. Lei D, Wu ZM (2006) Tabu search for multiple-criteria manufacturing cell design. Int J Adv Manuf Technol 28:950–956

    Article  Google Scholar 

  26. Gen M, Tsujimura Y, Kubota E (1994) Solving job-shop scheduling problem using genetic algorithm. Proceeding of the 16th international conference on computer and industrial engineering (ICCC & IE-94) Ashikaga pp.576-579

  27. Van Laarhoven PJM, Aarts EHL, Lenstra JK (1992) Job shop scheduling by simulated annealing. Oper Res 40:113–125

    Article  MATH  MathSciNet  Google Scholar 

  28. Blazewicz J, Domschke W, Pesch E (1996) The job shop scheduling problem: conventional and new solution techniques. Eur J Oper Res 93:1–33

    Article  MATH  Google Scholar 

  29. Zhang CY, Li PG, Guan ZL, Rao YQ (2007) A tabu search algorithm with a new neighborhood structure for the job shop scheduling. Comput Oper Res 34:3229–3242

    Article  MATH  MathSciNet  Google Scholar 

  30. Dell'Amico AM, Trubian AM (1993) Applying tabu search to the job-shop scheduling problem. Ann Oper Res 41(1–4):231–252

    Article  MATH  Google Scholar 

  31. Nowicki E, Smutnicki C (1996) A fast taboo search algorithm for the job-shop problem. Man Sci 42:797–813

    MATH  Google Scholar 

  32. Balas E, Vazacopoulos A (1998) Guided local search with shifting bottleneck for job shop scheduling. Manage Sci 44:262–275

    Article  MATH  Google Scholar 

  33. 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 32(1):408–419

    Google Scholar 

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Correspondence to Quan-Ke Pan.

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Li, JQ., Pan, QK., Suganthan, P.N. et al. A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem. Int J Adv Manuf Technol 52, 683–697 (2011). https://doi.org/10.1007/s00170-010-2743-y

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