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An effective shuffled frog-leaping algorithm for hybrid flow-shop scheduling with multiprocessor tasks

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Abstract

As a strongly NP-hard problem, the hybrid flow-shop problem with multiprocessor tasks (HFSPMT) has gained increasing attention due to its academic significance and application value. In this paper, an effective algorithm based on the shuffled frog leaping algorithm (SFLA) is proposed for solving the HFSPMT. First, three decoding methods are used together to decode a solution to a better schedule. Especially, the forward scheduling decoding method is employed, aiming at narrowing the idle time between consecutive operations in the processor as well as increasing the flexibility in selecting processors to schedule the following operations. Second, a bilevel crossover is designed to make each individual share the good “meme” within each memeplex and exchange the good “meme” between different memeplexes. Third, multiple local search operators are used in an effective way by employing the meta-Lamarckian learning strategy to enhance the local exploitation ability. Meanwhile, the use of crossover and local search together in the SFLA can enrich the memetic search behavior and balance the exploration and exploitation abilities. In addition, the effect of parameter setting of the algorithm is investigated based on the Taguchi method of design of experiment, and suitable values are suggested for the parameters. Extensive testing results based on two types of well-known benchmarks are provided. And the effectiveness of the proposed algorithm is demonstrated by the comparisons with some existing algorithms.

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References

  1. Ruiz R, Antonio VR (2010) The hybrid flow shop scheduling problem. Eur J Oper Res 205(1):1–18

    Article  MATH  Google Scholar 

  2. Alaykýran K, Engin O, Döyen A (2007) Using ant colony optimization to solve hybrid flow shop problems. Int J Adv Manuf Technol 35:541–550

    Article  Google Scholar 

  3. Gholami M, Zandieh M, Alem-Tabriz A (2009) Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. Int J Adv Manuf Technol 42:189–201

    Article  Google Scholar 

  4. Rashidi E, Jahandar M, Zandieh M (2010) An improved hybrid multi-objective parallel genetic algorithm for hybrid flow shop scheduling with unrelated parallel machines. Int J Adv Manuf Technol 49:1129–1139

    Article  Google Scholar 

  5. Drozdowski M (1996) Scheduling multiprocessor tasks—an overview. Eur J Oper Res 94(2):215–230

    Article  MATH  Google Scholar 

  6. Guan Y, Xiao WQ, Cheung RK, Li CL (2002) A multiprocessor task scheduling model for berth allocation: heuristic and worst-case analysis. Oper Res Lett 30:343–350

    Article  MathSciNet  MATH  Google Scholar 

  7. Oğuz C, Zinder Y, Do VH, Janiak A, Lichtenstein M (2004) Hybrid flow-shop scheduling problems with multiprocessor task systems. Eur J Oper Res 152:115–131

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  9. Ercan MF, Oğuz C (2005) Performance of local search heuristics on scheduling a class of pipelined multiprocessor tasks. Comput Electr Eng 31:537–555

    Article  MATH  Google Scholar 

  10. Bertel S, Billaut JC (2004) A genetic algorithm for an industrial multiprocessor flow shop scheduling with recirculation. Eur J Oper Res 159(3):651–662

    Article  MathSciNet  MATH  Google Scholar 

  11. Kahraman C, Engin O, Kaya I, Ozturk RE (2010) Multiprocessor task scheduling in multistage hybrid flow-shops: a parallel greedy algorithm approach. Appl Soft Comput 10:1293–1300

    Article  Google Scholar 

  12. Chen J, Lee CY (1999) General multiprocessor task scheduling. Nav Res Logist 46:57–74

    Article  MathSciNet  MATH  Google Scholar 

  13. Lee CY, Cai X (1999) Scheduling one and two-processor tasks on two parallel processors. IIE Trans 1999(31):445–455

    Google Scholar 

  14. Krawczyk H, Kubale M (1985) An approximation algorithm for the permutation for diagnostic test scheduling in multicomputer systems. IEEE Trans Comput 34:869–872

    Article  Google Scholar 

  15. Błażewicz J, Dell'Olmo P, Drozdowski M, Speranza MG (1992) Scheduling multiprocessor tasks on three dedicated processors. Inf Process Lett 41:275–280

    Article  MATH  Google Scholar 

  16. Brucker P, Knust S, Roper Y (2000) Scheduling UET task systems with concurrency on two parallel identical processors. Methods of Operations Research 52(3):369–387

    Article  MathSciNet  MATH  Google Scholar 

  17. Lloyd EL (1981) Concurrent task systems. Oper Res 106:226–253

    MathSciNet  Google Scholar 

  18. Du J, Leung YT (1989) Complexity of scheduling parallel task systems. SIAM J Discret Math 2:473–487

    Article  MathSciNet  MATH  Google Scholar 

  19. Gupta J, Tune E (1988) Minimizing tardy jobs in a two-stage hybrid flowshop. Int J Prod Res 36(9):2397–2417

    Article  Google Scholar 

  20. Gupta J (1988) Two-stage hybrid flowshop scheduling problem. J Oper Res Soc 39(4):359–364

    MATH  Google Scholar 

  21. Haouari R, M’Hallah R (1997) Heuristic algorithm for the two-stage hybrid flowshop problem. Operation Research Letters 21:42–53

    Article  MathSciNet  Google Scholar 

  22. Riane F, Artiba A, Elmaghraby SE (1998) A hybrid three-stage flowshop problem: efficient heuristics to minimize makespan. Eur J Oper Res 109:321–329

    Article  MATH  Google Scholar 

  23. Oğuz C, Ercan MF (1997) Scheduling multiprocessor tasks in a two-stage flow-shop environment. Comput Ind Eng 33:269–272

    Article  Google Scholar 

  24. Oğuz C, Ercan MF, Cheng TCE, Fung YF (2003) Heuristic algorithms for multiprocessor task scheduling in a two-stage hybrid flow-shop. Eur J Oper Res 149:390–403

    Article  MATH  Google Scholar 

  25. Serifoğlu FS, Ulusoy G (2004) Multiprocessor task scheduling in multistage hybrid flow-shops: a genetic algorithm approach. J Oper Res Soc 55:504–512

    Article  MATH  Google Scholar 

  26. Oğuz C, Ercan MF (2005) A genetic algorithm for hybrid flow-shop scheduling with multiprocessor tasks. J Sched 8:323–351

    Article  MathSciNet  MATH  Google Scholar 

  27. Ying KC, Lin SW (2006) Multiprocessor task scheduling in multistage hybrid flow-shops: an ant colony system approach. Int J Prod Res 44:3161–3177

    Article  MATH  Google Scholar 

  28. Tseng CT, Liao CJ (2008) A particle swarm optimization algorithm for hybrid flow-shop scheduling with multiprocessor tasks. Int J Prod Res 46:4655–4670

    Article  MATH  Google Scholar 

  29. Wang HM, Chou FD, Wu FC (2010) A simulated annealing for hybrid flow shop scheduling with multiprocessor tasks to minimize makespan. Int J Adv Manuf Technol 53:761–776

    Article  Google Scholar 

  30. Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154

    Article  MathSciNet  Google Scholar 

  31. Pan QK, Wang L, Gao L, Li JQ (2011) An effective shuffled frog-leaping algorithm for lot-streaming flow shop scheduling problem. Int J Adv Manuf Technol 52:699–713

    Article  Google Scholar 

  32. Graham RL, Lawler EL, Lenstra JK, Rinnooy Kan AHG (1979) Optimization and approximation in deterministic sequencing and scheduling: a survey. Annals of Discrete Mathematics 4:287–326

    Article  MathSciNet  Google Scholar 

  33. Wang L, Xu Y, Zhou G, Wang SY, Liu M (2012) A novel decoding method for the hybrid flow-shop scheduling problem with multiprocessor tasks. Int J Adv Manuf Technol 59(9):1113–1125

    Google Scholar 

  34. Wang XJ, Gao L, Zhang CY, Shao XY (2010) A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. Int J Adv Manuf Technol 51(58):757–767

    Article  Google Scholar 

  35. Wang L, Zheng DZ (2003) An effective hybrid heuristic for flow shop scheduling. Int J Adv Manuf Technol 21:38–44

    Article  Google Scholar 

  36. Liu B, Wang L, Jin YH (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cybern B Cybern 37:18–27

    Article  Google Scholar 

  37. Wang L (2001) Intelligent optimization algorithms with applications. Tsinghua University Press, Beijing

    Google Scholar 

  38. Montgomery DC (2005) Design and analysis of experiments. Wiley, Arizona

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Automation, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, 100084, China

    Ye Xu, Ling Wang, Min Liu & Sheng-yao Wang

Authors
  1. Ye Xu
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  2. Ling Wang
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  3. Min Liu
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  4. Sheng-yao Wang
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Correspondence to Ling Wang.

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Xu, Y., Wang, L., Liu, M. et al. An effective shuffled frog-leaping algorithm for hybrid flow-shop scheduling with multiprocessor tasks. Int J Adv Manuf Technol 68, 1529–1537 (2013). https://doi.org/10.1007/s00170-013-4940-y

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  • DOI: https://doi.org/10.1007/s00170-013-4940-y

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