Determining the order of products entering the mixed-model assembly line using the exact methods

Document Type : Original Article

Author

Industrial Engineering Department, Kosar university of Bojnord, Bojnord, Iran

Abstract

Due to increased pressures towards Just In Time (JIT) production, most organizations tend to produce in make to order (MTO) environment, leading to competing with other manufacturers and responding quickly to customers. In this regard and according to the mentioned points, the purpose of this paper is sequencing in the Mixed-model assembly line (MMAL) in MTO environment by considering available to promise (ATP) approach. Mixed-model assembly line could be a form of line capable of manufacturing numerous models of a product on one line. In this paper, a particular parallel MMAL balancing problem is studied in a make-to-order production system. First, the orders are determined based on the connected profit and a decision support method to order acceptance/rejection with attention to ATP is considered. By developing this method and presenting a mathematical model, delivery value and due dates are calculated with attention to stock. Then, a mixed formulation is presented to determine the accepted customer orders properly according to orders due dates that warrants the customer orders are not released early or late. The model determines subsequent objectives: optimizing the idle time and utility work of labors in the manufacturing line and optimizing the entirety earliness and tardiness costs with attention to the specified precedence of customer orders. To validate the performance of the proposed model, various test problems in small size are solved using the CPLEX solver, and compared with the Lagrangian relaxation method.

Keywords


[1] J. F. Bard, E. Dar-Elj, A. Shtub, An analytic framework for sequencing mixed model assembly lines, The International Journal of Production Research, Vol. 30, No. 1, pp. 35-48, 1992. https://doi.org/10.1080/00207549208942876.
[2] N. Boysen, M. Fliedner, A. Scholl, Sequencing mixed-model assembly lines: Survey, classification and model critique, European Journal of Operational Research, Vol. 19, No. 2, pp. 349-373, 2009. https://doi.org/10.1016/j.ejor.2007.09.013.
[3] H. Mosadegh, S. F. Ghomi, G. Süer, Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics, European Journal of Operational Research., Vol. 282, No. 2, pp. 530-544, 2020. https://doi.org/10.1016/j.ejor.2019.09.021.
[4] M. Rabbani, R. Heidari, H. Farrokhi-Asl, A bi-objective mixed-model assembly line sequencing problem considering customer satisfaction and customer buying behaviour, Engineering Optimization, Vol. 50, No. 12, pp. 2123-2142, 2018. https://doi.org/10.1080/0305215X.2018.1431234.
 [5] Z. I. Kucukkoc, Z. Zhang, Branch, bound and remember algorithm for two-sided assembly line balancing problem, European Journal of Operational Research, 2020. https://doi.org/10.1016/j.ejor.2020.01.032.
[6] M. O. Ball, C. Chen, Z. Zhao, Available to promise, Handbook of quantitative supply chain analysis, Springer, pp. 447-483, 2004.
[7] Q. Yin, X. Luo, J. Hohenstein, Design of mixed-model assembly lines integrating new energy vehicles, Machines, Vol. 9, No. 12, pp. 352-379, 2021. https://doi.org/10.3390/machines9120352.
[8] M. Rabbani, R. Heidari, H. Farrokhi-Asl, A bi-objective mixed-model assembly line sequencing problem considering customer satisfaction and customer buying behaviour, Engineering Optimization, Vol. 50, No. 12, pp. 2123-2142, 2019. https://doi.org/10.1080/0305215X.2018.1431234.
[9] L. Belkharroubi, K. Yahyaoui, Solving the mixed-model assembly line balancing problem type-I using a Hybrid Reactive GRASP, Production and Manufacturing Research, Vol. 10, No. 1, pp. 108-131, 2022. https://doi.org/10.1080/21693277.2022.2065380.
[10] W. Zhang, L. Hou, R. Jiao, Dynamic takt time decisions for paced assembly lines balancing and sequencing considering highly mixed-model production: An improved artificial bee colony optimization approach, Computers and Industrial Engineering, Vol. 161, 2021. https://doi.org/10.1016/j.cie.2021.107616.
[11] Q. Liu, W. Wang, K. Zhu, C. Zhang, Y. Rao, Advanced scatter search approach and its application in a sequencing problem of mixed-model assembly lines in a case company, in Engineering Optimization iFirst, 2013. https://doi.org/10.1080/0305215X.2013.846334.
[12] J. Bautista, R. Alfaro, C. Bata, Modeling and solving the mixed-model sequencing problem to improve productivity, International Journal of Production Economics, Vol. 161, pp. 83-95, 2015. https://doi.org/10.1016/j.ijpe.2014.11.018.
[13] Y. Zhang, P. B. Luh, K. Yoneda, Mixed-model assembly line scheduling using the Lagrangian relaxation technique, IIE Transactions, Vol. 50, No. 12, pp. 125-134, 2000. https://doi.org/10.1023/A:1007606213971.
[14] M. Rabbani, S. Sadri, N. Manavizadeh, H. Rafiei, A novel bi-level hierarchy towards available-to-promise in mixed-model assembly line sequencing problems, Engineering Optimization. Vol. 47, No. 7, pp. 947-962, 2015. https://doi.org/10.1080/0305215X.2014.933823.
[15] C. Hyun, Y. Kim, Y. Kim, A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines, Computers and Operations Research. Vol. 25, No. 7, pp. 675-690, 1998. https://doi.org/10.1016/S0305-0548(98)00026-4.