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Design of multi-stage adaptive kanban system

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Abstract

The traditional kanban system with a fixed number of cards does not work satisfactorily in an unstable environment. In the adaptive kanban-type pull control mechanism, the number of kanbans are allowed to change with respect to the inventory and backorder level. It is required to set the threshold values at which cards are added or deleted, which is a part of the design. Previous studies used local search and meta-heuristic methods to design the adaptive kanban system for single stage. In a multi-stage system the cards are circulated within the stage and their presence at designated positions will signal to the neighboring stages about the inventory. In this work, a model of the multi-stage system to traditional and adaptive kanban system is developed. A GA-based search is employed to set the parameters of the system. The results are compared with a traditional kanban system and found signs of improvement.

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References

  1. Philipoom PR, Rees LP, Taylor BW, Huang PY (1987) An investigation of the factors influencing number of kanbans required in the implementation of JIT technique with kanbans. Int J Prod Res 25:457–472

    Article  Google Scholar 

  2. Rees LP, Philipoom PR, Taylor BW, Hwang PY (1987) Dynamically adjusting the number of kanbans in a just- in- time production system using estimated values of lead time. Ii.e., Trans l9(2):199–207

    Article  Google Scholar 

  3. Savsar M, Al-Jawini A (1995) Simulation analysis of JIT systems. Int J Prod Econ 42:67–78

    Article  Google Scholar 

  4. Savsar M (1997) Simulation analysis of a pull-push system for an electronic assembly line. Int J Prod Econ 51:205–214

    Article  Google Scholar 

  5. Savsar M, Choueiki MH (2000) A Neural network procedure for kanban allocation in JIT production control system. Int J Prod Res 38(14):3247–3265

    Article  MATH  Google Scholar 

  6. Tardif V, Maaseidvaag L (2001) An adaptive approach to controlling kanban systems. Eur J Oper Res 132(2):411–424

    Article  MATH  MathSciNet  Google Scholar 

  7. Shahabudeen P, Sivakumar GD (2003) An Approach to adaptive kanban system. International conference, Society of Operational Management, IIM, Indore, India

  8. Spearman ML (1991) An analytic congestion model for closed production systems with IFR processing times. Manage Sci 37(8):1015–1029

    MATH  Google Scholar 

  9. Takahashi K, Nakamura N (1999) Reacting JIT ordering systems to unstable changes in demand. Int J Prod Res 37(10):2293–2313

    Article  MATH  Google Scholar 

  10. Hopp WJ, Roof ML (1998) Setting WIP levels with statistical throughput control (STC) in CONWIP production lines. Int J Prod Res 36(4):867–882

    Article  MATH  Google Scholar 

  11. Gupta SM, Al-Turki YAY (1997) An algorithm to dynamically adjust the number of kanbans in stochastic processing times and variable demand environment. Prod Plan Control 8(2):133–141

    Article  Google Scholar 

  12. Burno B, Dallery Y, Di mascalo M, Frein Y (2001) A multi class approximation technique for the analysis of kanban-like control systems. Int J Prod Res 39(2):307–328

    Article  Google Scholar 

  13. Di Mascolo M, Frein Y, Dallery Y (1996) An analytical method for performance evaluation of a kanban controlled production systems. Oper Res 44(1):50–64

    MATH  Google Scholar 

  14. Hunag PY, Rees LP, Taylor BW III (1983) A simulation analysis of the Japanese just-in-time technique (with kanbans) for a multiline, multistage production system. Decis Sci 14:326–344

    Article  Google Scholar 

  15. Mitra D, Mitrani I (1990) Analysis of a kanban discipline for cell coordination in production lines I. Manage Sci 36(12):1548–1566

    Google Scholar 

  16. Mitra D, Mitrani I (1991) Analysis of a kanban discipline for cell coordination in production lines II: Stochastic demands. Oper Res 35(5):807–823

    Article  Google Scholar 

  17. Pirlot M (1996) General local search methods. Eur J Oper Res 92(3):493–511

    Article  MATH  Google Scholar 

  18. Dowsland KA (1996) Genetic algorithms - a tool for OR? J Oper Res Soc 47:550–551

    Article  MATH  Google Scholar 

  19. Kochel P, Nielander U (2002) Kanban optimization by simulation and evolution. Prod Plan Control 13(8):725–734

    Article  Google Scholar 

  20. Wang H, Wang HP (1990) Determining number of kanbans: step toward non stock production. Int J Prod Res 28:2101–2115

    Article  Google Scholar 

  21. Wang H, Wang HP (1991) Optimum number of kanbans between two adjacent workstations in A JIT system. Int J Prod Econ 22(3):179–188

    Article  Google Scholar 

  22. Shahabudeen P, Krishnaiah K (1999) Design of bi-criteria kanban system using genetic algorithm. Int J Manag Syst 15:257–274

    Google Scholar 

  23. Paris JL, Tautou-Guillaume L, Pierreval H (2001) Dealing with design options in the optimization of manufacturing systems: an evolutionary approach. Int J Prod Res 39(6):1081–1094

    Article  MATH  Google Scholar 

  24. Reeves CR (1993) Genetic algorithms, in modern heuristic techniques for combinatorial problems. Wiley, New York

    Google Scholar 

  25. Sadegheih A (2006) Scheduling problem using genetic algorithm, simulated annealing and effects of parameter values on GA performance. Appl Math Model 30(2):147–154

    Article  MATH  Google Scholar 

  26. Saravanan R, Asokan P, Vijayakumar K (2003) Machining parameters optimisation for turning cylindrical stock into a continuous finished profile using genetic algorithm and simulated annealing. Int J Adv Manuf Technol 21(1):1–9

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Engg, Anna University, Chennai, India

    G. D. Sivakumar

  2. Dept. of Industrial Engg, College of Engg, Anna University, Chennai, India

    P. Shahabudeen

Authors
  1. G. D. Sivakumar
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  2. P. Shahabudeen
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Correspondence to P. Shahabudeen.

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Sivakumar, G.D., Shahabudeen, P. Design of multi-stage adaptive kanban system. Int J Adv Manuf Technol 38, 321–336 (2008). https://doi.org/10.1007/s00170-007-1093-x

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  • DOI: https://doi.org/10.1007/s00170-007-1093-x

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