An Enhanced Migrating Birds Optimization for the Flexible Job Shop Scheduling Problem with Lot Streaming

  • Tao Meng
  • Quan-ke PanEmail author
  • Qing-da Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)


This paper presents an enhanced migrating birds optimization (enMBO) for the flexible job shop scheduling problem with the consideration of lot streaming and the goal is minimizing total flowtime. In enMBO, to explore the solution space efficiently, we design a search scheme which is capable of adjusting the search radius with the increase of iteration. In addition, MBO concentrates too much on local search and hence is easily trapped in local optimum. To handle this, a special mechanism that based on precedence operation crossover is developed and incorporated into the evolutionary framework. We conduct simulations on well-known benchmarks with different scales and results verify the significance of schemes designed above. Moreover, by comparing with recent algorithms, the proposed enMBO shows its high performance for the considered problem.


Flexible job shop scheduling problem Migrating birds optimization Lot streaming Crossover operation Meta-heuristic 



This research is partially supported by the National Science Foundation of China 51575212 and 61174187, and Shanghai Key Laboratory of Power station Automation Technology.


  1. 1.
    Gonzalez, M., Vela, C., Varela, R.: Scatter search with path relinking for the flexible job shop scheduling problem. Eur. J. Oper. Res. 245(1), 35–45 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Brucker, P., Schlie, R.: Job-shop scheduling with multi-purpose machines. Computing 45(4), 369–375 (1990)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Yuan, Y., Xu, H., Yang, J.D.: A hybrid harmony search algorithm for the flexible job shop scheduling problem. Appl. Soft Comput. 13(7), 3259–3272 (2013)CrossRefGoogle Scholar
  4. 4.
    Yuan, Y., Xu, H.: Flexible job shop scheduling using hybrid differential evolution algorithms. Comput. Ind. Eng. 65, 246–260 (2013)CrossRefGoogle Scholar
  5. 5.
    Wang, J.F., Du, B.Q., Ding, H.M.: A genetic algorithm for the flexible job-shop scheduling problem. Comput. Oper. Res. 35(10), 3202–3212 (2008)CrossRefGoogle Scholar
  6. 6.
    Gao, K.Z., Suganthan, P.N., Pan, Q.K., Chua, T.J., Chong, C.S., Cai, T.X.: An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Syst. Appl. 65(C), 52–67 (2016)CrossRefGoogle Scholar
  7. 7.
    Xu, Y., Wang, L., Wang, S.Y., Liu, M.: An effective teaching-learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time. Neurocomputing 148, 260–268 (2015)CrossRefGoogle Scholar
  8. 8.
    Liu, Q., Zhan, M.M., Chekem, F.O., Shao, X.Y., Ying, B.S., Sutherland, J.W.: A hybrid fruit fly algorithm for solving flexible job-shop scheduling to reduce manufacturing carbon footprint. J. Clean. Prod. 168(1), 668–678 (2017)CrossRefGoogle Scholar
  9. 9.
    Demir, Y., Isleyen, S.K.: An effective genetic algorithm for flexible job-shop scheduling with overlapping in operations. Int. J. Prod. Res. 52(13), 3905–3921 (2014)CrossRefGoogle Scholar
  10. 10.
    Fattahi, P., Jolai, F., Arkat, J.: Flexible job shop scheduling with overlapping in operations. Appl. Math. Model. 33(7), 3076–3087 (2009)CrossRefGoogle Scholar
  11. 11.
    Farughi, H., Yegane, B.Y., Soltanpanah, H., Zaheri, F., Naseri, F.: Considering the flexibility and overlapping in operation in job shop scheduling based on meta-heuristic algorithms. Aust. J. Basic Appl. Sci. 5(11), 526–533 (2011)Google Scholar
  12. 12.
    Bozek, A., Werner, F.: Flexible job shop scheduling with lot streaming and sublot size optimization. Int. J. Prod. Res. (in Press)Google Scholar
  13. 13.
    Duman, E., Uysal, M., Alkaya, A.F.: Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inf. Sci. 217(24), 65–77 (2011)MathSciNetGoogle Scholar
  14. 14.
    Sioud, A., Gagne, C.: Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times. Eur. J. Oper. Res. 264(1), 66–73 (2018)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Tongur, V., Ülker, E.: The analysis of migrating birds optimization algorithm with neighborhood operator on traveling salesman problem. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds.) Intelligent and Evolutionary Systems. PALO, vol. 5, pp. 227–237. Springer, Cham (2016). Scholar
  16. 16.
    Niroomand, S., Hadi-Vencheh, A., Şahin, R., Vizvári, B.: Modified migrating birds optimization algorithm for closed loop layout with exact distances in flexible manufacturing systems. Expert Syst. Appl. 42(19), 6586–6597 (2015)CrossRefGoogle Scholar
  17. 17.
    Meng, T., Pan, Q.K., Li, J.Q., Sang, H.Y.: An improved migrating birds optimization for an integrated lot-streaming flow shop scheduling problem. Swarm Evol. Comput. 38, 64–78 (2018)CrossRefGoogle Scholar
  18. 18.
    Barnes, J.W., Chambers, J.B.: Flexible job shop scheduling by tabu search. Graduate Program in Operations Research and Industrial Engineering, The University of Texas, Austin, TX, Technical Report Series: ORP96-09 (1996)Google Scholar
  19. 19.
    Ruiz, R., Stutzle, T.: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Eur. J. Oper. Res. 177(3), 2033–2049 (2007)CrossRefGoogle Scholar
  20. 20.
    Pan, Q.K., Ruiz, R.: An estimation of distribution algorithm for lot-streaming flow shop problems with setup times. Omega 40, 166–180 (2012)CrossRefGoogle Scholar
  21. 21.
    Wang, L., Yang, R., Xu, Y., Niu, Q., Pardalos, P.M., Fei, M.R.: An improved adaptive binary harmony search algorithm. Inf. Sci. 232(5), 58–87 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechatronic Engineering and AutomationShanghai UniversityShanghaiPeople’s Republic of China
  2. 2.School of Mathematical ScienceLiaocheng UniversityLiaochengPeople’s Republic of China
  3. 3.State Key Laboratory of Synthetic Automation for Process IndustriesNortheastern UniversityShenyangPeople’s Republic of China

Personalised recommendations