Study on No-Wait Flexible Flow Shop Scheduling with Multi-objective

  • Ze TaoEmail author
  • Xiaoxia Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11745)


A multi-objective flexible flow shop scheduling model is constructed inclusive of production period, total expense, and mean flow time, which is based on the characteristics of dual-resource constrained no-wait flow shop scheduling problem with unrelated parallel machines. A genetic algorithm based on Pareto is proposed to solve the multi-objective scheduling problem. Then, consider the machine and worker constraints, and unrelated parallel machines and the successive processing, the production period is given through pushing reversely from the operation. The starting time of some jobs will be delayed and the spare time of machines will be increased in order to ensure the consecutive operations of the same job. Considering three objectives, an optimal set is given, and compared to other algorithms, simulation results show that the method is effective and feasible. At last, a comparative analysis of the same case is made from no-wait flow shop scheduling and flow shop scheduling with non-consecutive operation.


No-wait Multi-objective Flexible flow shop Dual-resource 


  1. 1.
    Aldowaisan, T.A., Allahverdi, A.: No-wait flow shop scheduling problem to minimize the number of tardy jobs. Int. J. Adv. Manuf. Technol. 61, 311–323 (2012)CrossRefGoogle Scholar
  2. 2.
    Engin, O., Güçlü, A.: A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems. Appl. Soft Comput. 72, 166–176 (2018)CrossRefGoogle Scholar
  3. 3.
    Zhao, F., Qin, S., Zhang, Y., et al.: A hybrid biogeography-based optimization with variable neighborhood search mechanism for no-wait flow shop scheduling problem. Expert Syst. Appl. 126, 321–339 (2019)CrossRefGoogle Scholar
  4. 4.
    Khalili, M.: A multi-objective electromagnetism algorithm for a bi-objective hybrid no-wait flow shop scheduling problem. Int. J. Adv. Manuf. Technol. 70, 1591–1601 (2014)CrossRefGoogle Scholar
  5. 5.
    Song, J.W., Tang, J.F.: No-wait hybrid flow shop scheduling method based on discrete particle swarm optimization. J. Syst. Simul. 22(10), 2257–2261 (2010)Google Scholar
  6. 6.
    Wang, B.L., Li, T.K., Sun, B.: TSP-based heuristic algorithm for permutation flow shop scheduling with limited waiting time constraints. Control Decis. 27(5), 768–772 (2012)MathSciNetGoogle Scholar
  7. 7.
    Pan, Q.K., Wang, W.H., Zhu, J.Y.: Modified discrete particle swarm optimization algorithm for no-wait flow shop problem. Comput. Integr. Manuf. Syst. 13(6), 1127–1130 (2007)Google Scholar
  8. 8.
    Zhang, Q.L., Chen, Y.S.: Particle swarm optimization algorithm for bi-directional no-wait hybrid flow shop problem. Comput. Integr. Manuf. Syst. 19(10), 2503–2509 (2013)Google Scholar
  9. 9.
    Reddy, B.S.P., Rao, C.S.P.: A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS. Int. J. Adv. Manuf. Technol. 31, 602–613 (2006)CrossRefGoogle Scholar
  10. 10.
    Deva Prasad, S., Rajendran, C., Krishnaiah Chetty, O.V.: A genetic algorithm approach to multi-objective scheduling in a Kanban-controlled flow shop with intermediate buffer and transport constraints. Int. J. Adv. Manuf. Technol. 29, 564–576 (2006)CrossRefGoogle Scholar
  11. 11.
    Huang, H.J., Lu, T.P.: Solving a multi-objective flexible job shop scheduling problem with timed Petri nets and genetic algorithm. Discrete Math. Algorithms Appl. 2(2), 221–237 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Tao, Z.: Study on examination center scheduling problem based on genetic algorithm and simulated annealing algorithm. Bio Technol. (23), 14354–14361(2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringShenyang Ligong UniversityShenyangChina
  2. 2.Henan University of TechnologyZhengzhouChina

Personalised recommendations