Skip to main content
Log in

Considering the conveyer stoppages in sequencing mixed-model assembly lines by a new fuzzy programming approach

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Mixed-model production is the practice of assembling different and distinct models in a line without changeovers with responding to sudden demand changes for a variety of models. In this paper, we specify sequence of models to minimize conveyer stoppages. We assume that our lines are fixed and we cannot change the balance of the lines. When the condition of lines like setup cost and demand of each model change, it is important to specify the sequence for minimizing the conveyer stoppages without balancing the line again because the main lines are fixed. We consider three objective functions simultaneously: minimizing the variation in the actual and required production capacity of the line and minimizing the objectives which increase the chance of conveyer stoppage, including: (a) minimizing the total setup time, (b) minimizing the total production variation cost, and (c) minimizing the total utility work cost. Because of conflicting objectives, we propose the fuzzy goal programming-based approach to solve the model. Finally, we present an estimator for nearness of conveyer stoppages and study about affecting of sub-lines and changing the conveyer velocity in a station for reducing stoppages.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Mirzapour Al-e-Hashem SMJ, Aryanezhad MB (2009) An efficient method to solve a mixed-model assembly line sequencing problem considering a sub-line. World Appl Sci J 6(2):168–181

    Google Scholar 

  2. Xiaobo Z, Zhou Z, Asres A (1999) A note on Toyota’s goal of sequencing mixed models on an assembly line. Comput Ind Eng 36:57–65

    Article  Google Scholar 

  3. Yano CA, Rachamadugu R (1991) Sequencing to minimize work overload in assembly lines with product options. Manage Sci 37:572–586

    Article  Google Scholar 

  4. Bautista J, Companys R, Corominas A (1996) Heuristics and exact algorithms for solving the Monden problem. Eur J Opl Res 88:101–113

    Article  MATH  Google Scholar 

  5. Leu Y, Matheson LA, Rees LP (1996) Sequencing mixed-model assembly lines with genetic algorithms. Comput Ind Eng 30:1027–1036

    Article  Google Scholar 

  6. Miltenburg J, Sinnamon G (1989) Scheduling mixed-model multi-level just-in-time production systems. Int J Prod Res 27:487–509

    Article  Google Scholar 

  7. Boysen N, Fliedner M, Scholl A (2007) Sequencing mixed-model assembly lines: survey, classification and model critique. Eur J Oper Res 192(2):349–373

    Article  MathSciNet  Google Scholar 

  8. Karkmazel T, Meral S (2003) Theory and methodology. Bicriteria sequencing method for the mixed-model assembly line in just-in-time production systems. Eur J Oper Res 131:188–207

    Article  Google Scholar 

  9. Scholl A, Klein R, Domschke W (1998) Pattern based vocabulary building for effectively sequencing mixed-model assembly lines. Journal of Heuristics 4:359–381

    Article  MATH  Google Scholar 

  10. Mahdavi I, Javadi B, Sahebjamnia N, Mahdavi-Amiri N (2009) A two-phase linear programming methodology for fuzzy multi-objective mixed-model assembly line problem. Int J Adv Manuf Technol 44:1010–1023

    Article  Google Scholar 

  11. Hsiao FH, Xu SD, Lin CY, Tsai ZR (2008) Robustness design of fuzzy control for nonlinear multiple time-delay large-scale systems via neural-network-based approach. IEEE Trans Syst Man Cybern B Cybern 38(1):244–251

    Article  Google Scholar 

  12. Chen CY, Hsu JRC, Chen CW (2005) Fuzzy logic derivation of neural network models with time delays in subsystems. Int J Artif Intell Tools 14(6):967–974

    Article  MathSciNet  Google Scholar 

  13. Tamura T, Long H, Ohno K (1999) A sequencing problem to level part usage rates and work loads for a mixed-model assembly line with a bypass subline. Int J Prod Econ 60–61:557–564

    Article  Google Scholar 

  14. Mansouri SA (2005) A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines. Eur J Oper Res 167:696–716

    Article  MathSciNet  MATH  Google Scholar 

  15. Miltenburg J, Sinnamon G (1995) Revisiting the mixed-model multi-level just-in-time scheduling problem. Int J Prod Res 33:2049–2052

    Article  MATH  Google Scholar 

  16. Monden Y (1993) Toyota production system, 2nd edn. Institute of Industrial Engineers, Norcross, GA

    Google Scholar 

  17. Morabito MA, Kraus ME (1995) A note on “Scheduling mixed-model multi-level just-in-time production systems”. Int J Prod Res 33:2061–2063

    Article  MATH  Google Scholar 

  18. Steiner G, Yeomans JS (1996) Optimal level schedules in mixed-model, multi-level JIT assembly systems with pegging. Eur J Opl Res 95:38–52

    Article  MATH  Google Scholar 

  19. Sumichrast RT, Clayton ER (1996) Evaluating sequences for paced, mixed-model assembly lines with JIT component fabrication. Int J Prod Res 34:3125–3143

    Article  MATH  Google Scholar 

  20. Sumichrast RT, Russell RS (1990) Evaluating mixed-model assembly line sequencing heuristics for just-in-time production systems. J Oper Manage 9:371–389

    Article  Google Scholar 

  21. Sumichrast RT, Russell RS, Taylor BW (1992) A comparative analysis of sequencing procedures for mixed-model assembly lines in a just-in-time production system. Int J Prod Res 30:199–214

    Article  MATH  Google Scholar 

  22. Kubiak W (1993) Minimizing variation of production rates in just-in-time systems: a survey. Eur J Opl Res 66:259–271

    Article  MATH  Google Scholar 

  23. Xiaobo Z, Ohno K (1994) A sequencing problem for a mixed-model assembly line in a JIT production system. Comput Ind Eng 27:71–74

    Article  Google Scholar 

  24. Chen CY, Lin JW, Lee WI, Chen CW (2010) Fuzzy control for an oceanic structure: a case study in time-delay TLP system. J Vib Control 16:147. doi:10.1177/1077546309339424

    Article  MathSciNet  Google Scholar 

  25. Ding FY, Zhu J, Sun H (2006) Comparing two weighted approaches for sequencing mixed-model assembly lines with multiple objectives. Int J Prod Econ 102:108–131

    Article  Google Scholar 

  26. Bautista J, Cano J (2008) Minimizing work overload in mixed-model assembly lines. Int J Prod Econ 112:177–191

    Article  Google Scholar 

  27. Kim S, Jeong B (2007) Product sequencing problem in mixed-model assembly line to minimize unfinished works. Comput Ind Eng 53:206–214

    Article  Google Scholar 

  28. Yoo JK, Shimizu Y, Hinoo R (2004) A sequencing problem for mixed model assembly lines with aid of relief-man. JSME Int J 48(1):15–20

    Google Scholar 

  29. Okamura K, Yamshina H (1979) A heuristic algorithm for the assembly line model-mix sequencing problem to minimize the risk of stopping the conveyor. Int J Prod Res 17:233–247

    Article  Google Scholar 

  30. Bard JF, Dar-El EM, Shtub A (1992) An analytic framework for sequencing mixed-model assembly lines. Int J Prod Res 30:35–48

    Article  MATH  Google Scholar 

  31. Hyun CJ, Kim Y, Kim YK (1998) A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines. Comput Oper Res 25(7–8):675–690

    Article  MATH  Google Scholar 

  32. Ponnambalama SG, Aravindanb P, Subba RM (2003) Genetic algorithms for sequencing problems in mixed model assembly lines. Comput Ind Eng 45:669–690

    Article  Google Scholar 

  33. Miltenburg J, Stenier G, Yeomans S (1990) A dynamic programming algorithm for scheduling mixed model, just in time production system. Math Comput Model 13(3):57–66

    Article  MATH  Google Scholar 

  34. Tamiz M, Jones D, Romero C (1998) Goal programming for decision making: an overview of the current state-of-the-art. Eur J Oper Res 111:569–581

    Article  MATH  Google Scholar 

  35. Javadi B, Rahimi-Vahed A, Rabbani M, Dangchi M (2008) Solving a multi-objective mixed-model assembly line sequencing problem by a fuzzy goal programming approach. Int J Adv Manuf Technol 39:975–982

    Article  Google Scholar 

  36. Charnes A, Cooper WW (1961) Management models and industrial applications of linear programming, vol 4(3). Wiley/Society for Industrial and Applied Mathematics, New York, pp 267–268

  37. Bellman RE, Zadeh LA (1970) Decision making in a fuzzy environment. Manage Sci 17:141–164

    Article  MathSciNet  Google Scholar 

  38. Zimmermann HJ (1976) Description and optimization of fuzzy systems. Int J Gen Syst 2:209–215

    Article  Google Scholar 

  39. Zimmermann HJ (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst 1:45–56

    Article  MATH  Google Scholar 

  40. Kim JS, Whang KS (1998) Theory and methodology a tolerance approach to the fuzzy goal programming problems with unbalanced triangular membership function. Eur J Oper Res 107:614–624

    Article  MATH  Google Scholar 

  41. Martel JM, Aouni BA (1998) Diverse imprecise goal programming model formulations. J Glob Optim 12:127–138

    Article  MathSciNet  MATH  Google Scholar 

  42. Ramik J (2000) Fuzzy goals and fuzzy alternatives in goal programming problems. Fuzzy Sets Syst 111:81–86

    Article  MathSciNet  MATH  Google Scholar 

  43. Chen LH, Tsai FC (2001) Theory and methodology fuzzy goal programming with different importance and priorities. Eur J Oper Res 133:548–556

    Article  MATH  Google Scholar 

  44. Iskander MG (2004) A fuzzy weighted additive approach for stochastic fuzzy goal programming. Appl Math Comput 154:543–553

    Article  MathSciNet  MATH  Google Scholar 

  45. Chun LC (2004) A weighted max–min model for fuzzy goal programming. Fuzzy Sets Syst 142:407–420

    Article  MATH  Google Scholar 

  46. Saad OM (2005) An iterative goal programming approach for solving fuzzy multi objective integer linear programming problems. Appl Math Comput 170:216–225

    Article  MathSciNet  MATH  Google Scholar 

  47. Iskander MG (2006) Exponential membership function in stochastic fuzzy goal programming. Appl Math Comput 173:782–791

    Article  MathSciNet  MATH  Google Scholar 

  48. Hu CF, Teng CJ, Li SY (2007) Continuous optimization a fuzzy goal programming approach to multi-objective optimization problem with priorities. Eur J Oper Res 176:1319–1333

    Article  MathSciNet  MATH  Google Scholar 

  49. Akoz O, Petrovic D (2007) A fuzzy goal programming method with imprecise goal hierarchy. Eur J Oper Res 181:1427–1433

    Article  Google Scholar 

  50. Yaghoobi MA, Tamiz M (2007) A method for solving fuzzy goal programming problems based on minimax approach. Eur J Oper Res 177:1580–1590

    Article  MathSciNet  MATH  Google Scholar 

  51. Chein TL, Chen CC, Haung YC, Lin WJ (2008) Stability and almost disturbance decoupling analysis of nonlinear system subject to feedback linearization and feed forward neural network controller. IEEE Trans Neural Netw 19:1220. doi:10.1109/TNN.2008.2000207

    Article  Google Scholar 

  52. Chen CW, Yeh K, Liu KFR (2009) Adaptive fuzzy sliding mode control for seismically excited bridges with lead rubber bearing isolation. Int J Uncertain Fuzziness Knowl Based Syst 17:705. doi:10.1142/S0218488509006224

    Article  MATH  Google Scholar 

  53. Arora SR, Gupta R (2009) Continuous optimization interactive fuzzy goal programming approach for bilevel programming problem. Eur J Oper Res 194:368–376

    Article  MathSciNet  MATH  Google Scholar 

  54. Yager RR (1977) Multiple objective decision-making using fuzzy sets. Int J Man Mach Stud 9:375–382

    Article  MATH  Google Scholar 

  55. Bard JF, Shtub A, Joshi SB (1994) Sequencing mixed-model assembly lines to level parts usage and minimize the length. Int J Prod Res 32:2431–2454

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Masoud Rabbani.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rabbani, M., Radmehr, F. & Manavizadeh, N. Considering the conveyer stoppages in sequencing mixed-model assembly lines by a new fuzzy programming approach. Int J Adv Manuf Technol 54, 775–788 (2011). https://doi.org/10.1007/s00170-010-2968-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-010-2968-9

Keywords

Navigation