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Journal of Intelligent Manufacturing

, Volume 30, Issue 3, pp 1195–1220 | Cite as

Multi-objective artificial bee colony algorithm for order oriented simultaneous sequencing and balancing of multi-mixed model assembly line

  • Ullah Saif
  • Zailin Guan
  • Li ZhangEmail author
  • Fei Zhang
  • Baoxi Wang
  • Jahanzaib Mirza
Article

Abstract

In multi-mixed model assembly lines, customer orders with different demand of models and due dates make it critical to decide the sequencing of different models and balancing of lines. Therefore, current research, first time, investigated an order oriented simultaneous sequencing and balancing problem of multi-mixed model assembly lines with an aim to minimize the variation in material usage, minimize the maximum makespan among the multi-lines and minimize the penalty cost of the late delivery models from different orders simultaneously. Moreover, a new mix-minimum part sequencing method is developed and a multi-objective artificial bee colony (MABC) algorithm is proposed to get the solution for the considered problem. Experiments are performed on standard assembly line data taken from operations library (OR) to test the performance of the proposed MABC algorithm against a famous multi-objective algorithm (Strength Pareto Evolutionary Algorithm i.e. SPEA 2) in literature. Moreover, the proposed MABC algorithm is also tested on the data taken from a well reputed manufacturing company in China against the famous algorithm in literature (i.e. SPEA 2). End results indicate that the proposed MABC outperforms SPEA 2 algorithm for both standard data and company data problems.

Keywords

Multi-mixed model assembly line Simultaneous sequencing and balancing Multi-objective optimization Artificial bee colony algorithm Pareto solutions 

Notes

Acknowledgements

This work has been supported by MOST (Ministry of Science & Technology of China) under the Grants Nos. 2012AA040909, 2012BAH08F04, & 2013AA040206, and by the National Natural Science Foundation of China (Grants Nos. 51035001, 50825503, & 71271156).

References

  1. Al-e-hashem, S. M. J. M., Aryanezhad, M. B., & Jabbarzadeh, A. (2011). A new approach to solve a mixed-model assembly line with a bypass sub line sequencing problem. International Journal of Advance Manufacturing Technology, 52, 1053–1066.CrossRefGoogle Scholar
  2. Bolat, A. (2003). A mathematical model for selecting mixed-models with due dates. International Journal of Production Research, 41(5), 897–918.CrossRefGoogle Scholar
  3. Celano, G., Costa, A., & Fichera, S. (2004). A comparative analysis of sequencing heuristics for solving the Toyota Goal Chasing problem. Robot Computer integrated manufacturing journal, 20, 573–581.CrossRefGoogle Scholar
  4. Coello, C. A. C., & Cortes, N. C. (2005). Solving multi-objective optimization problems using an artificial immune system. Genetic Programming and Evolvable Machines, 6, 163–190.CrossRefGoogle Scholar
  5. Dar-El, E. M., & Nadivi, A. (1981). A mixed-model sequencing application. International Journal of Production Research, 19, 69–84.CrossRefGoogle Scholar
  6. Ding, F. Y., & Tolani, R. (2003). Production planning to support mixed-model assembly. Computers and Industrial Engineering, 45(3), 375–392.CrossRefGoogle Scholar
  7. Dong, Q. Y., Lu, J. S., & Gui, Y. K. (2012). Integrated optimization of production planning and scheduling in mixed model assembly line. In 2012 international workshop on information and electronics engineering. Procedia engineering, 29 (pp. 3340–3347).Google Scholar
  8. Dörmer, J., Günther, H. O., Gujjula, R., & Friedrich, K. (2010). Master production scheduling for high-variant mixed-model assembly lines. In 2010 17th international annual EurOMA conference: managing operations in service economies. Portugal: Porto.Google Scholar
  9. Dormer, J., Gunther, H. O., & Gujjula, R. (2013). Master production scheduling and sequencing at mixed-model assembly lines in the automotive industry. Flexible Services and Manufacturing Journal,. doi: 10.1007/s10696-013-9173-8.CrossRefGoogle Scholar
  10. Gans, J. E. (2008). Neu-und Anpassungsplanung der Struktur von getakteten Fließproduktionssystemen für variantenreiche Serienprodukte in der Montage. Dissertation, Universität Paderborn, Paderborn.Google Scholar
  11. Hindi, K. S., & Ploszajski, G. (1994). Formulation and solution of a selection and sequencing problem in car manufacture. Computers and Industrial Engineering, 26(1), 203–211.CrossRefGoogle Scholar
  12. Jiang, Z., Lin, Li, Zhi, Li, & Zhaoqian, Li. (2012). Order-oriented cooperative sequencing optimisation in multi-mix-model assembly lines. International Journal of Production Research, 50(24), 7198–7209.CrossRefGoogle Scholar
  13. Karabati, S., & Sayin, S. (2003). Assembly line balancing in a mixed-model sequencing environment with synchronous transfers. Euorpian Journal of Operations Research, 149(2), 417–429.CrossRefGoogle Scholar
  14. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical report TR06. Turkey: Computer Engineering Department, Erciyes University.Google Scholar
  15. Kim, M., Hiroyasu, T., Miki, M., & Watanabe, S. (2004). SPEA2+: Improving the performance of the strength pareto evolutionary algorithm 2. In Lecture notes in computer science, 3242, 742–751.Google Scholar
  16. Kim, Y. K., Kim, Y. J., & Kim, Y. (1996). Genetic algorithms for assembly line balancing with various objectives. Computers and Industrial Engineering, 30(3), 397–409.CrossRefGoogle Scholar
  17. Kim, Y. K., Kim, J. Y., & Kim, Y. (2000). A coevolutionary algorithm for balancing and sequencing in mixed model assembly lines. Applied Intelligence, 13, 247–258.CrossRefGoogle Scholar
  18. Li, J.-Q., Pan, Q.-K., & Gao, K.-Z. (2011). Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. International Journal of Advance Manufacturing Technology, 55, 1159–1169.CrossRefGoogle Scholar
  19. Manavizadeh, N., Goodarzi, A. H., Rabbani, M., & Jolai, F. (2013). Order acceptance/rejection policies in determining the sequence in mixed-model assembly lines. Applied Mathematical Modelling, 37(4), 2531–2551.CrossRefGoogle Scholar
  20. Mansouri, S. A. (2005). A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines. European Journal of Operational Research, 167(3), 696–716.CrossRefGoogle Scholar
  21. Miltenburg, J. (1989). Level schedules for mixed-model assembly lines in just-in-time production systems. Management Science, 35(2), 192–207.CrossRefGoogle Scholar
  22. Mosadegh, H., Zandieh, M., & Fatemi Ghomi, S. M. T. (2012). Simultaneous solving of balancing and sequencing problems with station-dependent assembly times for mixed-model assembly lines. Applied Soft Computing, 12, 1359–1370.CrossRefGoogle Scholar
  23. Pan, Q. K., Tasgetiren, M. F., Suganthan, P. N., & Chua, T. J. (2011). A discrere artificiall bee colony algorithm for the lot-streaming flowshop scheduling problem. Information Science, 181(12), 2455–2468.CrossRefGoogle Scholar
  24. Saif, U., Guan, Z., Liu, W., Zhang, C., & Wang, B. (2014). Multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line. The International Journal of Advanced Manufacturing Technology, 75(9–12), 1809–1827.CrossRefGoogle Scholar
  25. Scholl, A. (1993). Data of Assembly Line Balancing Problems. Working Paper, TH Darmstadt.Google Scholar
  26. Scholl, A. (1999). Balancing and sequencing assembly lines (2nd ed.). Heidelberg: Physica.CrossRefGoogle Scholar
  27. Simaria, A. S., & Vilarinho, P. M. (2004). A genetic algorithm based approach to the mixed model assembly line balancing problem of type II. Computers and Industrial Engineering, 47, 391–407.CrossRefGoogle Scholar
  28. Tapkan, P., Ozbakir, L., & Baykasoglu, L. (2012). Modeling and solving constrained two aided assembly line balancing problem via bee algorithms. Applied Soft Computing, 12(1), 3343–3355.CrossRefGoogle Scholar
  29. Tasgetiren, M. F., Pan, Q. K., Suganthan, P. N., & Chen, A. H.-L. (2011). A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops. Information Sciences, 181(16), 3459–3475.CrossRefGoogle Scholar
  30. Volling, T. (2009). Auftragsbezogene Planung bei variantenreicher Serienproduktion. Dissertation, Technische Universität Braunschweig, Gabler, Wiesbaden.CrossRefGoogle Scholar
  31. Volling, T., & Spengler, T. S. (2011). Modeling and simulation of order-driven planning policies in build-to-order automobile production. International Journal of Production Economics, 131(1), 183–193.CrossRefGoogle Scholar
  32. Wang, G., Cui, H., & Xu, P. (2010). Order schedule on multi-mixed-model assembly lines in assembly-to-order environments. In 2010 international conference of information science and management engineering, Xi’an, Aug 7–8, 1 (pp. 563–566).Google Scholar
  33. Wang, B., Guan, Z., Chen, Y., Shao, X., Jin, M., & Zhang, C. (2013). An assemble-to-order production planning with the integration of order scheduling and mixed-model sequencing. Frontier of Mechanical Engineering, 8(2), 137–145.CrossRefGoogle Scholar
  34. Wang, B., Guan, Z. L., Saif, U., Xianhao, Xu, & Zongdong, He. (2014). Simultaneous order scheduling and mixed-model sequencing in assemble-to order production environment: a multi-objective hybrid artificial bee colony algorithm. Journal of Intelligent Manufacturing,. doi: 10.1007/s10845-014-0988-2.CrossRefGoogle Scholar
  35. Watanabe, S., Hiroyasu, T., & Miki, M. (2002). Neighborhood cultivation genetic algorithm for multi-objective optimization problems. In 2012 4th Asia-Pacific conference on simulated evolution and learning (SEAL-2002) (pp. 198–202).Google Scholar
  36. Zhang, W., Lin, L., Gen, M., & Chien, C. F. (2012). Hybrid sampling strategy-based multi-objective evolutionary algorithm. Procedia Computer Science, 12, 96–101.CrossRefGoogle Scholar
  37. Zhang, W., & Gen, M. (2011). An efficient multi-objective genetic algorithm for mixed-model assembly line balancing problem considering demand ratio-based cycle time. Journal of Intelligent Manufacturing, 22, 367–378.CrossRefGoogle Scholar
  38. Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multi-objective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2), 173–195.CrossRefGoogle Scholar
  39. Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. Zurich, Switzerland: Swiss Federal Institute Techonology.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Ullah Saif
    • 1
    • 2
  • Zailin Guan
    • 1
  • Li Zhang
    • 1
    Email author
  • Fei Zhang
    • 1
  • Baoxi Wang
    • 1
  • Jahanzaib Mirza
    • 2
  1. 1.State Key Lab of Digital Manufacturing Equipment and Technology, HUST-SANY Joint Lab of Advanced ManufacturingHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  2. 2.Department of Industrial EngineeringUniversity of Engineering and TechnologyTaxilaPakistan

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