A genetic algorithmic approach to multi-objective scheduling in a kanban-controlled flowshop with intermediate buffer and transport constraints

  • S. Deva Prasad
  • C. Rajendran
  • O. V. Krishnaiah Chetty
Original Article


In this paper, we consider the problem of extended permutation flowshop scheduling with the intermediate buffers. The Kanban flowshop problem considered involves dual-blocking by both part type and queue size acting on machines, as well as on material handling. The objectives considered in this study include the minimization of mean completion time of containers, mean completion time of part types, and the standard deviation of mean completion time of part types. An attempt is made to solve the multi-objective problem by using a proposed genetic algorithm, called the “non-dominated and normalized distanceranked sorting multi-objective genetic algorithm” (NDSMGA). In order to evaluate the NDSMGA, we have made use of randomly generated flowshop scheduling problems with input and output buffer constraints in the flowshop. The non-dominated solutions for these problems are obtained from each of the existing methods, namely multi-objective genetic local search (MOGLS), elitist non-dominated sorting genetic algorithm (ENGA), gradual priority weighting genetic algorithm (GPWGA), modified MOGLS, and the NDSMGA. These non-dominated solutions are combined to obtain a net non-dominated solution set for a given problem. Contribution in terms of number of solutions to the net non-dominated solution set from each of these algorithms is tabulated, and the results reveal that a substantial number of non-dominated solutions are contributed by the NDSMGA.


Dual-blocking mechanisms Flowshops Genetic algorithm Kanbans Mean completion time of containers Mean completion time of part types Multiple objectives Standard deviation of mean completion time of part types 


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  1. 1.
    Ishibuchi H, Murata T (1998) A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans Syst Man Cybernetics Part C Appl Rev 28:392–403CrossRefGoogle Scholar
  2. 2.
    Bagchi T (1999) Multi objective scheduling by genetic algorithms. Kluwer, BostonGoogle Scholar
  3. 3.
    Chang PC, Hsieh JC, Lin SG (2002) The development of gradual priority weighting approach for the multi-objective flowshop scheduling problem. Int J Prod Econ 79:171–183CrossRefGoogle Scholar
  4. 4.
    Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multi-objective permutation flowshop scheduling. IEEE Trans Evol Comput 7:204–223CrossRefGoogle Scholar
  5. 5.
    Garey MR, Johnson DS, Sethi R (1976) The complexity of flow-shop and job-shop scheduling. Math Oper Res 1:117–129MATHMathSciNetGoogle Scholar
  6. 6.
    Hall NG, Sriskandarajah C (1996) A survey of machine scheduling problems with blocking and no-wait in process. Oper Res 44:510–525MATHMathSciNetCrossRefGoogle Scholar
  7. 7.
    Papadimitriou C, Kanellakis P (1980) Flowshop scheduling with limited temporary storage. J ACM 27:533–549MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Leisten R (1990) Flowshop sequencing problems with limited buffer storage. Int J Prod Res 28:2085–2100MATHCrossRefGoogle Scholar
  9. 9.
    Berkley BJ (1992) A review of the Kanban production control research literature. Prod Oper Manage 1:393–411Google Scholar
  10. 10.
    Weng MX (2000) Scheduling flowshops with limited buffer spaces. In: Proceedings of the Winter Simulation Conference, pp 1359–1363, ISSN 0743-1902, Scholar
  11. 11.
    Brucker P, Heitmann S, Hurink JL (2003) Flowshop problems with intermediate buffers. OR Spectrum 25:549–574MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Monden Y (1983) Toyota production system: a practical approach to production management. Industrial Engineering and Management Press, Atlanta, GAGoogle Scholar
  13. 13.
    Hemamalini B, Rajendran C (2000) Determination of the number of containers, production Kanbans and withdrawal Kanbans; and scheduling in Kanban flowshops — part 1. Int J Prod Res 38:2529–2548MATHCrossRefGoogle Scholar
  14. 14.
    Berkley BJ (1996) A simulation study of container size in two-card Kanban system. Int J Prod Res 34:3417–3445MATHCrossRefGoogle Scholar
  15. 15.
    Philipoom PR, Rees LP, Taylor BW III (1996) Simultaneously determining the number of Kanbans, container sizes and the final-assembly sequence of products in a just-in-time shop. Int J Prod Res 34:51–69MATHCrossRefGoogle Scholar
  16. 16.
    Sharadapriyadarshini B, Rajendran C (1997) Heuristics for scheduling in a Kanban system with dual blocking mechanisms. Eur J Oper Res 103:439–452MATHCrossRefGoogle Scholar
  17. 17.
    Rajendran C (1999) Formulations and heuristics for scheduling in a Kanban flowshop to minimize the sum of weighted flowtime, weighted tardiness and weighted earliness of containers. Int J Prod Res 37:1137–1158MATHCrossRefGoogle Scholar
  18. 18.
    Nawaz M, Enscore EE, Jr Ham I (1983) A heuristic algorithm for the m-machine, n-job flowshop sequencing problem. OMEGA 11:91–95CrossRefGoogle Scholar
  19. 19.
    Rajendran C (1993) Heuristic algorithm for scheduling in a flowshop to minimize total flowtime. Int J Prod Econ 29:65–73CrossRefGoogle Scholar
  20. 20.
    Deb K (2001) Multi-objective optimization using evolutionary algorithms, 1st ed. Wiley, New YorkMATHGoogle Scholar
  21. 21.
    Taillard E (1993) Benchmarks for basic scheduling problems. Eur J Oper Res 64:278–285MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  • S. Deva Prasad
    • 1
  • C. Rajendran
    • 2
  • O. V. Krishnaiah Chetty
    • 1
  1. 1.Department of Mechanical EngineeringIIT MadrasChennaiIndia
  2. 2.Department of Management StudiesIIT MadrasChennaiIndia

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