Journal of Intelligent Manufacturing

, Volume 26, Issue 1, pp 169–187 | Cite as

Active scheduling for hybrid flowshop with family setup time and inconsistent family formation

  • Hao LuoEmail author
  • Abraham Zhang
  • George Q. Huang


This research is motivated by a real-life hybrid flowshop scheduling problem where jobs are organized in families according to their machine settings and tools. This type of problem is common in the production process of standard metal components. The problem is complicated by the requirement of family setup time when a machine changes from processing one job family to another and the formation of job families varies in different stages. This problem has been previously solved with a non-delay scheduling heuristic in which no machine is kept idle. This research illustrates that inserting intentional idle time into a non-delay schedule can further reduce the total setup time as well as makespan. With the inserted idle time, the non-delay schedules are extended to active schedules. This paper presents a mechanism to determine the locations and lengths of intentional idle times in the efficient active schedules. Four active scheduling approaches are developed by integrating two types of waiting factor operators into two non-delay approaches. Computational experiments have been conducted to compare the proposed active scheduling approaches in terms of effectiveness and efficiency. The results have shown that the proposed active scheduling approaches are superior to non-delay scheduling. The analysis of variance has been applied on the factors related to scheduling environment, problem size and scheduling approach. The analysis has identified factors that are most influential on the scheduling result.


Hybrid flowshop scheduling Family setup time Inconsistent family formation Active schedule Non-delay schedule 



This paper is supported by National Natural Science Foundation of China (51105081), Guangdong Natural Science Foundation (S2012010010016), National Science and Technology Ministry of China (2012BAF12B10), Industry-University-Research Cooperation Key Project of Guangdong Science and Technology Commission (2011B090400409), Guangdong College Talent Import Scheme (11ZK0066), Guangzhou Pearl River New Star Fund Science and Technology Planning Project (2011J2200017), and HKU Seed Funding for Basic Research (201111159135). Partial financial supports from Zhejiang, Hangzhou and Lin’an governments are gratefully acknowledged.


  1. Allahverdi, A., Ng, C. T., Cheng, T. C. E., & Kovalyov, M. Y. (2008). A survey of scheduling problems with setup times or costs. European Journal of Operational Research, 187(3), 985–1032.CrossRefGoogle Scholar
  2. Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering design. New York: Wiley.Google Scholar
  3. Goldgerg, D., & Lingle, R. (1985). Alleles, loci, and the traveling salesman problem. Hillsdale: Paper presented at the First international Conference on Genetic Algorithms and their Applications.Google Scholar
  4. Gupta, J. N. D., & Schaller, J. E. (2005). Minimizing flow time in a flow-line manufacturing cell with family setup times. Journal of the Operational Research Society, 57(2), 163–176.Google Scholar
  5. Kim, H.-W., & Lee, D.-H. (2009). Heuristic algorithms for re-entrant hybrid flow shop scheduling with unrelated parallel machines. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(4), 433–442.CrossRefGoogle Scholar
  6. Lin, B. M. T., & Cheng, T. C. E. (2005). Two-machine flowshop batching and scheduling. Annals of Operations Research, 133, 149–161.CrossRefGoogle Scholar
  7. Logendran, R., Carson, S., & Hanson, E. (2005). Group scheduling in flexible flow shops. International Journal of Production Economics, 96(2), 143–155.CrossRefGoogle Scholar
  8. Logendran, R., deSzoeke, P., & Barnard, F. (2006). Sequence-dependent group scheduling problems in flexible flow shops. International Journal of Production Economics, 102(1), 66–86.Google Scholar
  9. Luo, H., Huang, G. Q., Shi, Y., Qu, T., & Zhang, Y. (2012). Hybrid flowshop scheduling with family setup time and inconsistent family formation. International Journal of Production Research, 50(6), 1457–1475. Google Scholar
  10. Mendes, J. J. M., Gonçalves, J. F., & Resende, M. G. C. (2009). A random key based genetic algorithm for the resource constrained project scheduling problem. Computers & Operations Research, 36(1), 92–109.CrossRefGoogle Scholar
  11. Michalewicz, Z. (1996). Genetic algorithms+data structures=evolution programs. Berlin: Springer.CrossRefGoogle Scholar
  12. Mirsanei, H., Zandieh, M., Moayed, M., & Khabbazi, M. (2011). A simulated annealing algorithm approach to hybrid flow shop scheduling with sequence-dependent setup times. Journal of Intelligent Manufacturing, 22(6), 965–978.CrossRefGoogle Scholar
  13. Pinedo, M. L. (2008). Scheduling: Theory, algorithms, and systems (3rd ed.). Berlin: Springer.Google Scholar
  14. Salmasi, N., Logendran, R., & Skandari, M. (2011). Makespan minimization of a flowshop sequence-dependent group scheduling problem. The International Journal of Advanced Manufacturing Technology, 56(5), 699–710.CrossRefGoogle Scholar
  15. Sprecher, A., Kolisch, R., & Drexl, A. (1995). Semi-active, active, and non-delay schedules for the resource-constrained project scheduling problem. European Journal of Operational Research, 80(1), 94–102.CrossRefGoogle Scholar
  16. Zandieh, M., Mozaffari, E., & Gholami, M. (2010). A robust genetic algorithm for scheduling realistic hybrid flexible flow line problems. Journal of Intelligent Manufacturing, 21(6), 731–743.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.HKU-ZIRI Laboratory for Physical Internet, Department of Industrial and Manufacturing Systems EngineeringThe University of Hong KongPokfulamHong Kong
  2. 2.Department of Management Systems, Waikato Management SchoolThe University of WaikatoHamiltonNew Zealand

Personalised recommendations