Advertisement

Total Tardiness Minimization in a Flow Shop with Blocking Using an Iterated Greedy Algorithm

  • Nouri NouhaEmail author
  • Ladhari Talel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 464)

Abstract

We highlight in this paper the competitive performance of the Iterated Greedy algorithm (IG) for solving the flow shop problem under blocking. A new instance of IG is used to minimize the total tardiness criterion. Basically, due to the NP-hardness of this blocking problem, we employ another variant of the NEH heuristic to form primary solution. Subsequently, we apply recurrently constructive methods to some fixed solution and then we use an acceptance criterion to decide whether the new generated solution substitutes the old one. Indeed, the perturbation of an incumbent solution is done by means of the destruction and construction phases. Despite its simplicity, the IG algorithm under blocking has shown its effectiveness, based on Ronconi and Henriques benchmark, when compared to state-of-the-art meta-heuristics.

Keywords

Blocking Flow shop Total tardiness IG 

References

  1. 1.
    Graham, R., Lawler, E., Lenstra, J., Rinnooy, K.: Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discret. Math. 5, 287–362 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Hall, N., Sriskandarajah, C.: A survey of machine scheduling problems with blocking and no-wait in process. Oper. Res. 44, 25–510 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Gilmore, P., Gomory, R.: Sequencing a one state variable machine: a solvable case of the traveling salesman problem. Oper. Res. 5, 79–655 (1964)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Pinedo, M.: Scheduling: Theory, Algorithms, and Systems. Pretice Hall, U.A.S (2008)Google Scholar
  5. 5.
    McCormick, S., Pinedo, M., Shenker, S., Wolf, B.: Sequencing in an assembly line with blocking to minimize cycle time. OR 37, 925–935 (1989)CrossRefzbMATHGoogle Scholar
  6. 6.
    Nawaz, M., Enscore, J., Ham, I.: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11, 91–95 (1983)CrossRefGoogle Scholar
  7. 7.
    Ronconi, D.: A note on constructive heuristics for the flowshop problem with blocking. Int. J. Prod. Econ. 87, 39–48 (2004)CrossRefGoogle Scholar
  8. 8.
    Wang, L., Pan, Q., Tasgetiren, M.: Minimizing the total flow time in a flowshop with blocking by using hybrid harmony search algorithms. Expert Syst. Appl. 12, 7929–7936 (2010)CrossRefGoogle Scholar
  9. 9.
    Ronconi, D.P., Henriques, L.R.S.: Some heuristic algorithms for total tardiness minimization in a flowshop with blocking. Omega 2, 272–81 (2009)CrossRefGoogle Scholar
  10. 10.
    Caraffa, V., Ianes, S., Bagchi, T.P., Sriskandarajah, C.: Minimizing makespan in a flowshop using genetic algorithms. Int. J. Prod. Econ. 2, 15–101 (2001)Google Scholar
  11. 11.
    Ronconi, D.: A branch-and-bound algorithm to minimize the makespan in a flowshop problem with blocking. Ann. Oper. Res. 1, 53–65 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Grabowski, J., Pempera, J.: The permutation flowshop problem with blocking. A tabu search approach. Omega 3, 11–302 (2007)Google Scholar
  13. 13.
    Wang, L., Pan, Q., Suganthan, P., Wang, W., Wang, Y.: A novel hybrid discrete differential evolution algorithm for blocking flowshop scheduling problems. Comput. Oper. Res. 3, 20–509 (2010)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Qian, B., Wang, L., Huang, D.X., Wang, W.L., Wang, X.: An effective hybrid DE-based algorithm for multi-objective flowshop scheduling with limited buffers. Comput. Oper. Res. 1, 3–209 (2009)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Ribas, I., Companys, R., Tort-Martorell, X.: An iterated greedy algorithm for the flowhsop scheduling problem with blocking. Omega 3, 293–301 (2011)CrossRefGoogle Scholar
  16. 16.
    Deng, G., XU, Z., Gu, X.: A discrete artificial bee colony algorithm for minimizing the total flow time in the blocking flow shop scheduling. Chin. J. Chem. Eng. 20, 1067–1073 (2012)CrossRefGoogle Scholar
  17. 17.
    Ribas, N., Companys, R.: Efficient heuristic algorithms for the blocking flow shop scheduling problem with total flow time minimization. Comput. Ind. Eng. (2015). doi: 10.1016/j.cie.2015.04.013
  18. 18.
    Han, Y.-Y., Liang, J., Pan, Q.-K., Li, J.-Q., Sang, H.-Y., Cao, N.: Effective hybrid discrete artificial bee colony algorithms for the total flowtime minimization in the blocking flowshop problem. Int. J. Adv. Manuf. Technol. 67, 397–414 (2013)CrossRefGoogle Scholar
  19. 19.
    Lin, S., Ying, K.: Minimizing makespan in a blocking flowshop using a revised artificial immune system algorithm. Omega 41, 383–389 (2013)CrossRefGoogle Scholar
  20. 20.
    Pan, Q., Wang, L.: Effective heuristics for the blocking flowshop scheduling problem with makespan minimization. Omega 2, 218–29 (2012)CrossRefGoogle Scholar
  21. 21.
    Wang, X., Tang, L.: A discrete particle swarm optimization algorithm with self-adaptive diversity control for the permutation flowshop problem with blocking. Appl. Soft Comput. 12, 652–662 (2012)CrossRefGoogle Scholar
  22. 22.
    Pan, Q., Wang, L., Sang, H., Li, J., Liu, M.: A high performing memetic algorithm for the flowshop scheduling problem with blocking. IEEE Trans. Autom. Sci. Eng. 10, 741–756 (2013)CrossRefGoogle Scholar
  23. 23.
    Davendra, D., Bialic-Davendra, M., Senkerik, R., Pluhacek, M.: Scheduling the flow shop with blocking problem with the chaos-induced discrete self organising migrating algorithm. In: Proceedings 27th European Conference on Modelling and Simulation (2013)Google Scholar
  24. 24.
    Ribas, I., Companys, R., Tort-Martorell, X.: An efficient iterated local search algorithm for the total tardiness blocking flow shop problem. Int. J. Prod. Res. 51, 5238–5252 (2013)CrossRefGoogle Scholar
  25. 25.
    Nouri, N., Ladhari, T.: Minimizing regular objectives for blocking permutation flow shop scheduling: heuristic approaches. GECCO, 441–448 (2015)Google Scholar
  26. 26.
    Armentano, V.A., Ronconi, D.P.: Minimizaçâo do tempo total de atraso no problema de flowshop com buffer zero através de busca tabu. Gestao Produçao 7(3), 352–363 (2000)CrossRefGoogle Scholar
  27. 27.
    Ruiz, R., Stützle, T.: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Eur. J. Oper. Res. 177, 49–2033 (2007)CrossRefzbMATHGoogle Scholar
  28. 28.
    Ruiz, R., Stützle, T.: An iterated greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. Eur. J. Oper. Res. 187, 1143–1159 (2008)CrossRefzbMATHGoogle Scholar
  29. 29.
    Johnson, S.M.: Optimal two- and three-stage production schedules with setup time included. Naval Res. Logist. Q. 1, 8–61 (2013)Google Scholar
  30. 30.
    Potts, C.N., Van Wassenhove, L.N.: A decomposition algorithm for the single machine total tardiness problem. Oper. Res. Lett. 1, 81–177 (1982)CrossRefzbMATHGoogle Scholar
  31. 31.
    Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64, 85–278 (1993)CrossRefzbMATHGoogle Scholar
  32. 32.
    Baker, K.R., Bertrand, J.W.M.: An investigation of due date assignment rules with constrained tightness. J. Oper. Manag. 3, 109–120 (1984)Google Scholar
  33. 33.
    Januario, T.O., Arroyo, J.E.C., Moreira, M.C.O.: Nature Inspired Cooperative Strategies for Optimization (NICSO 2008), pp. 153–164. Springer, Berlin (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Ecole Supérieure des Sciences Economiques et Commerciales de TunisUniversity of TunisTunisTunisia
  2. 2.College of BusinessUmm Al-Qura UniversityMeccaSaudi Arabia

Personalised recommendations