iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S10845-014-0988-2
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 Skip to main content

Advertisement

Springer Nature Link
Log in

Simultaneous order scheduling and mixed-model sequencing in assemble-to-order production environment: a multi-objective hybrid artificial bee colony algorithm

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

In today’s competitive manufacturing market, effective production planning and scheduling are crucial to streamline production and increase profit. Successful production planning can achieve efficient capacity utilization and fulfill customer demand in a timely manner. For assemble-to-order companies, the assembly production planning is mainly driven by customer orders. In literature, the master production schedule which assigns production orders of individual models to production intervals is generally treated independently from the product sequencing, which might lead to local optimization for the final assembly schedule. In this paper, both order scheduling and mixed-model sequencing are taken into account simultaneously to formulate the final assembly schedule. Three objectives are concurrently considered including, maximizing net profit earned from orders, reducing sequence-dependent setup time between different models and leveling material usage. A novel multi-objective hybrid artificial bee colony (MHABC) algorithm combined with some steps of genetic algorithm and the Pareto optimality is developed to solve the current problem. Experiments are conducted and performance of the proposed MHABC algorithm is examined with the improved strength Pareto evolutionary algorithm (SPEA2). The results indicate that the proposed MHABC performs better as compared to the SPEA2 and gives better Pareto optimal solutions. Finally, a practical case problem from an engineering machinery company is solved with the proposed approach for simultaneous order scheduling and mixed-model sequencing.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Ajorlou, S., & Shams, I. (2013). Artificial bee colony algorithm for CONWIP production control system in a multi-product multi-machine manufacturing environment. Journal of Intelligent Manufacturing, 24(6), 1145–1156.

    Article  Google Scholar 

  • Akay, B., & Karaboga, D. (2012). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, 23(4), 1001–1014.

  • Bolat, A. (2003). A mathematical model for selecting mixed-models with due dates. International Journal of Production Research, 41(5), 897–918.

    Article  Google Scholar 

  • Boysen, N., Fliedner, M., & Scholl, A. (2009). Production planning of mixed-model assembly lines: Overview and extensions. Production Planning and Control, 20(5), 455–471.

    Article  Google Scholar 

  • Brajevic, I., & Tuba, M. (2013). An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems. Journal of Intelligent Manufacturing, 24(4), 729–740.

    Article  Google Scholar 

  • Che, Z. H., & Chiang, C. J. (2010). A modified Pareto genetic algorithm for multi-objective build-to-order supply chain planning with product assembly. Advances in Engineering Software, 41(7–8), 1011–1022.

  • Chern, C., Lei, S., & Huang, K. (2014). Solving a multi-objective master planning problem with substitution and a recycling process for a capacitated multi-commodity supply chain network. Journal of Intelligent Manufacturing, 25(1), 1–25.

    Article  Google Scholar 

  • Coello, C. A. C., & Cortes, N. C. (2005). Solving multiobjective optimization problems using an artificial immune system. Genetic Programming and Evolvable Machines, 6(2), 163–190.

  • Chen, Y. (2014). The order fulfillment planning problem considering multi-site order allocation and single-site shop floor scheduling. Journal of Intelligent Manufacturing, 25(3), 441–458.

    Article  Google Scholar 

  • De Lit, P., Latinne, P., Rekiek, B., & Delchambre, A. (2001). Assembly planning with an ordering genetic algorithm. International Journal of Production Research, 39(16), 3623–3640.

    Article  Google Scholar 

  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

  • 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.

  • Ding, F. Y., & Tolani, R. (2003). Production planning to support mixed-model assembly. Computers and Industrial Engineering, 45(3), 375–392.

    Article  Google Scholar 

  • Gans, J. E. (2008). Neu-und Anpassungsplanung der Struktur von getakteten Fließproduktionssystemen für variantenreiche Serienprodukte in der Montage. Dissertation, Universität Paderborn, Paderborn.

  • Hyun, C. J., Kim, Y., & Kim, Y. K. (1998). A Genetic algorithm for multiple objective sequencing problems in mixed-model assembly lines. Computers and Operations Research, 25(7–8), 675–690.

    Article  Google Scholar 

  • Hung, T., & Chan, K. (2013). Uncertainty quantifications of Pareto optima in multiobjective problems. Journal of Intelligent Manufacturing, 24(2), 385–395.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, (Oct 2005). Computer Engineering Department, Erciyes University Turkey.

  • Kim, Y. K., Hyun, C. J., & Kim, Y. (1996). Sequencing in mixed-model assembly lines: A genetic algorithm approach. Computers and Operations Research, 23(12), 1131–1145.

    Article  Google Scholar 

  • 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 Advanced Manufacturing Technology, 55(9–12), 1159–1169.

    Article  Google Scholar 

  • Lin, L., Hao, X. C., Gen, M., & Jo, J. B. (2012). Network modeling and evolutionary optimization for scheduling in manufacturing. Journal of Intelligent Manufacturing, 23(6SI), 2237–2253.

    Article  Google Scholar 

  • Moradi, H., Zandieh, M., & Mahdavi, I. (2011). Non-dominated ranked genetic algorithm for a multi-objective mixed-model assembly line sequencing problem. International Journal of Production Research, 49(12), 3479–3499.

    Article  Google Scholar 

  • 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.

  • Miltenburg, J. (1989). Level schedules for mixed-model assembly lines in just-in-time production systems. Management Science, 35(2), 192–207.

  • Monden, Y. (1993). The Toyota production system. Norcross, GA: Industrial Engineering and Management Press, Institute of Industrial Engineers.

  • 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.

    Article  Google Scholar 

  • Schott, J. R. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization. Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA.

  • Tasgetiren, M. F., Pan, Q. K., & Suganthan, P. N. (2011). A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops. Information Sciences, 181(16), 3459–3475.

  • Volling, T. (2009). Auftragsbezogene Planung bei variantenreicher Serienproduktion. Dissertation, Technische Universität Braunschweig, Gabler, Wiesbaden.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wang, G., Cui, H., & Xu, P. (2010). Order schedule on multi-mixed-model assembly lines in assembly-to-order environments. 2010 International Conference of Information Science and Management Engineering, Xi’an, Aug 7–8, (vol. 1, pp. 563–566).

  • Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the performance of the strength Pareto evolutionary algorithm. TIK-Report 103, May 2001.

Download references

Acknowledgments

This work has been supported by MOST (the Ministry of Science and Technology of China) under the Grant Nos. 2013AA040206, 2012BAF12B20, and 2012BAH08F04, and by the National Natural Science Foundation of China (Grant Nos. 51035001, 51121002, 71271156, and 71131004).

Author information

Authors and Affiliations

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology, HUST-SANY Joint Laboratory of Advanced Manufacturing, Huazhong University of Science and Technology, Wuhan, 430074, China

    Baoxi Wang, Zailin Guan, Saif Ullah, Xianhao Xu & Zongdong He

  2. SANY Heavy Industry Co., Ltd., Changsha, 410100, China

    Zongdong He

Authors
  1. Baoxi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zailin Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saif Ullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xianhao Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zongdong He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zailin Guan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, B., Guan, Z., Ullah, S. et al. Simultaneous order scheduling and mixed-model sequencing in assemble-to-order production environment: a multi-objective hybrid artificial bee colony algorithm. J Intell Manuf 28, 419–436 (2017). https://doi.org/10.1007/s10845-014-0988-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-014-0988-2

Keywords

Search

Navigation