The Discrete Swallow Swarm Optimization for Flow-Shop Scheduling Problem

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 915)


The flow-shop scheduling problem is a well-known problem in production system. The objective is minimizing the total time it takes to process the entire job called makespan. In order to solve this NP-hard problem, we approve a new adaptation approach based on the intelligent behaviors of swallows, it is the discrete swallow swarm optimization algorithm (DSSO) present a recent metaheuristic method used to solve a combinatorial problem. The proposed algorithm is tested on different benchmarks instances and compared with different proposed algorithms. The results demonstrate that the proposed algorithm is more efficient than the other compared algorithms. It can be used to solve large instances of flow shop scheduling problem effectively.


Swallow swarm optimization algorithm Combinatorial problem Metaheuristic Flow-shop scheduling problem Makespan 


  1. 1.
    Allahverdi, A., Gupta, J.N.D., Aldowaisan, T.: A review of scheduling research involving setup considerations. Omega 27, 219–239 (1999). Scholar
  2. 2.
    Allaoui, H., Artiba, A.: Scheduling two-stage hybrid flow shop with availability constraints. Comput. Oper. Res. 33, 1399–1419 (2006). Scholar
  3. 3.
    Błażewicz, J., Ecker, K.H., Pesch, E., Schmidt, G., Weglarz, J.: Scheduling Computer and Manufacturing Processes. Springer-Verlag, Berlin Heidelberg (1996)CrossRefGoogle Scholar
  4. 4.
    Bouzidi, M., Riffi, M.E., Serhir, A.: Discrete particle swarm optimization for travelling salesman problems: new combinatorial operators. In: Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017), pp. 141–150. Springer, Cham (2017)Google Scholar
  5. 5.
    Bouzidi, S., Riffi, M.E.: Discrete swallow swarm optimization algorithm for travelling salesman problem. In: Proceedings of the 2017 International Conference on Smart Digital Environment, pp. 80–84. ACM, New York, NY, USA (2017)Google Scholar
  6. 6.
    Johnson, S.M.: Optimal two- and three-stage production schedules with setup times included. Nav. Res. Logist. Q. 1, 61–68 (1954). Scholar
  7. 7.
    Kaveh, A., Bakhshpoori, T., Afshari, E.: An efficient hybrid particle swarm and swallow swarm optimization algorithm. Comput. Struct. 143, 40–59 (2014). Scholar
  8. 8.
    Liao, C.-J., Tjandradjaja, E., Chung, T.-P.: An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem. Appl. Soft Comput. 12, 1755–1764 (2012). Scholar
  9. 9.
    Lin, Q., Gao, L., Li, X., Zhang, C.: A hybrid backtracking search algorithm for permutation flow-shop scheduling problem. Comput. Ind. Eng. 85, 437–446 (2015). Scholar
  10. 10.
    Liu, Y.-F., Liu, S.-Y.: A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem. Appl. Soft Comput. 13, 1459–1463 (2013). Scholar
  11. 11.
    Marichelvam, M.K., Prabaharan, T., Yang, X.S.: Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Appl. Soft Comput. 19, 93–101 (2014). Scholar
  12. 12.
    Murata, T., Ishibuchi, H., Tanaka, H.: Genetic algorithms for flowshop scheduling problems. Comput. Ind. Eng. 30, 1061–1071 (1996). Scholar
  13. 13.
    Neshat, M., Sepidnam, G., Sargolzaei, M.: Swallow swarm optimization algorithm: a new method to optimization. Neural Comput. Appl. 23, 429–454 (2013). Scholar
  14. 14.
    Reeves, C.R.: A genetic algorithm for flowshop sequencing. Comput. Oper. Res. 22, 5–13 (1995). Scholar
  15. 15.
    Sayoti, F.: Golden Ball Algorithm for solving Flow Shop Scheduling Problem. Int. J. Interact. Multimedia Artif. Intell. 4, 15–18 (2016)Google Scholar
  16. 16.
    Sotskov, Y.N., Shakhlevich, N.V.: NP-hardness of shop-scheduling problems with three jobs. Discrete Appl Math 59, 237–266 (1995). Scholar
  17. 17.
    Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64, 278–285 (1993). Scholar
  18. 18.
    Yagmahan, B., Yenisey, M.M.: Ant colony optimization for multi-objective flow shop scheduling problem. Comput. Ind. Eng. 54, 411–420 (2008). Scholar
  19. 19.
    Zhang, C., Sun, J., Zhu, X., Yang, Q.: An improved particle swarm optimization algorithm for flowshop scheduling problem. Inf. Process. Lett. 108, 204–209 (2008). Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.LAROSERI Laboratory, Department of Computer ScienceChouaib Doukkali UniversityEl JadidaMorocco
  2. 2.Department of MathematicsChouaib Doukkali UniversityEl JadidaMorocco

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