Document Type : Research Paper

Authors

Department of Industrial Engineering, Faculty of Engineering, Khayyam University, Mashhad, Iran.

Abstract

The use of assembly lines is one of the important approaches in mass production of industrial products. Imbalance of assembly lines increases cycle time and idle times, resulting in reduced production rates, line efficiency, and increased system costs, which ultimately lead to low productivity. A hybrid model assembly line is a type of production line on which various models of products are assembled. These assembly lines are increasingly accepted in the industry in order to overcome the diversity of customer demand. The hybrid model assembly line is able to respond quickly to sudden changes in demand for different models of a product without maintaining a large inventory.
The purpose of this paper is to present a multi-objective integer linear mathematical programming model for balancing assembly lines, which is solved using the general criteria method. The three objective functions considered in this model are: (1) Minimizing cycle time (2) Minimize the idle time of each station and (3) increase the efficiency of the assembly line. In order to investigate the model, Iran-Shargh Neishabour Company has been considered as a case study. After implementing the proposed model of the paper, the results show the optimal performance of the proposed model and the studied parameters in line balancing have been significantly improved.

Keywords

Main Subjects

[1]     Fisel, J., Exner, Y., Stricker, N., & Lanza, G. (2019). Changeability and flexibility of assembly line balancing as a multi-objective optimization problem. Journal of manufacturing systems, 53, 150-158. https://doi.org/10.1016/j.jmsy.2019.09.012
[2]     Zhong, Y., Deng, Z., & Xu, K. (2019). An effective artificial fish swarm optimization algorithm for two-sided assembly line balancing problems. Computers and industrial engineering, 138, 106121. https://doi.org/10.1016/j.cie.2019.106121
[3]     Sun, B. Q., Wang, L., & Peng, Z. P. (2020). Bound-guided hybrid estimation of distribution algorithm for energy-efficient robotic assembly line balancing. Computers and Industrial engineering, 146, 106604. https://doi.org/10.1016/j.cie.2020.106604
[4]     Liu, R., Liu, M., Chu, F., Zheng, F., & Chu, C. (2021). Eco-friendly multi-skilled worker assignment and assembly line balancing problem. Computers and industrial engineering, 151, 106944. https://doi.org/10.1016/j.cie.2020.106944
[5]     Çil, Z. A., Li, Z., Mete, S., & Özceylan, E. (2020). Mathematical model and bee algorithms for mixed-model assembly line balancing problem with physical human–robot collaboration. Applied soft computing, 93, 106394. https://doi.org/10.1016/j.asoc.2020.106394
[6]     Çil, Z. A., & Kizilay, D. (2020). Constraint programming model for multi-manned assembly line balancing problem. Computers and operations research, 124, 105069. https://doi.org/10.1016/j.cor.2020.105069
[7]     Eghtesadifard, M., Khalifeh, M., & Khorram, M. (2020). A systematic review of research themes and hot topics in assembly line balancing through the web of science within 1990–2017. Computers and industrial engineering, 139, 106182. https://doi.org/10.1016/j.cie.2019.106182
[8]     Jackson, J. R. (1956). A computing procedure for a line balancing problem. Management science, 2(3), 261-271. https://doi.org/10.1287/mnsc.2.3.261
[9]     Ramezanian, R., & Ezzatpanah, A. (2015). Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem. Computers and industrial engineering, 87, 74-80. https://doi.org/10.1016/j.cie.2015.04.017
[10] Chutima, P., & Chimklai, P. (2012). Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimisation with negative knowledge. Computers and industrial engineering, 62(1), 39-55. https://doi.org/10.1016/j.cie.2011.08.015
[11] Liu, B., & Liu, B. (2009). Theory and practice of uncertain programming (Vol. 239). Berlin: Springer.
[12] Alavidoost, M. H., Babazadeh, H., & Sayyari, S. T. (2016). An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem. Applied soft computing, 40, 221-235. https://doi.org/10.1016/j.asoc.2015.11.025
[13] Ponnambalam, S. G., Aravindan, P., & Naidu, G. M. (2000). A multi-objective genetic algorithm for solving assembly line balancing problem. The international journal of advanced manufacturing technology, 16(5), 341-352. https://doi.org/10.1007/s001700050166
[14] Baybars, I. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management science, 32(8), 909-932. https://doi.org/10.1287/mnsc.32.8.909
[15] Xiaofeng, H., Erfei, W., Jinsong, B., & Ye, J. (2010). A branch-and-bound algorithm to minimize the line length of a two-sided assembly line. European journal of operational research, 206(3), 703-707. https://doi.org/10.1016/j.ejor.2010.02.034
[16] Özbakır, L., & Tapkan, P. (2011). Bee colony intelligence in zone constrained two-sided assembly line balancing problem. Expert systems with applications, 38(9), 11947-11957. https://doi.org/10.1016/j.eswa.2011.03.089
[17] Chen, R. S., Lu, K. Y., & Yu, S. C. (2002). A hybrid genetic algorithm approach on multi-objective of assembly planning problem. Engineering applications of artificial intelligence, 15(5), 447-457. https://doi.org/10.1016/S0952-1976(02)00073-8
[18] 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. https://doi.org/10.1016/j.ejor.2004.07.016
[19] Nourmohammadi, A., & Zandieh, M. (2011). Assembly line balancing by a new multi-objective differential evolution algorithm based on TOPSIS. International journal of production research, 49(10), 2833-2855. https://doi.org/10.1080/00207540903473367
[20] Delice, Y., Aydoğan, E. K., Söylemez, İ., & Özcan, U. (2018). An ant colony optimisation algorithm for balancing two-sided U-type assembly lines with sequence-dependent set-up times. Sādhanā, 43(12), 1-15. https://doi.org/10.1007/s12046-018-0969-9
[21] Ogan, D., & Azizoglu, M. (2015). A branch and bound method for the line balancing problem in U-shaped assembly lines with equipment requirements. Journal of manufacturing systems, 36, 46-54. https://doi.org/10.1016/j.jmsy.2015.02.007
[22] Graves, S. C., & Whitney, D. E. (1979, December). A mathematical programming procedure for equipment selection and system evaluation in programmable assembly. 1979 18th IEEE conference on decision and control including the symposium on adaptive processes (pp. 531-536). IEEE. DOI: 10.1109/CDC.1979.270236
[23] Graves, S. C., & Lamar, B. W. (1983). An integer programming procedure for assembly system design problems. Operations research, 31(3), 522-545. https://doi.org/10.1287/opre.31.3.522
[24] Ahmed, T., Sakib, N., Hridoy, R. M., & Shams, A. T. (2020). Application of line balancing heuristics for achieving an effective layout: a case study. International journal of research in industrial engineering, 9(2), 114-129.
[25] Zhang, Z., Tang, Q., & Chica, M. (2020). Multi-manned assembly line balancing with time and space constraints: A MILP model and memetic ant colony system. Computers and industrial engineering, 150, 106862. https://doi.org/10.1016/j.cie.2020.106862
[26] Zhang, B., Xu, L., & Zhang, J. (2021). Balancing and sequencing problem of mixed-model U-shaped robotic assembly line: Mathematical model and dragonfly algorithm based approach. Applied soft computing, 98, 106739. https://doi.org/10.1016/j.asoc.2020.106739
[27] Li, Z., Kucukkoc, I., & Tang, Q. (2021). Enhanced branch-bound-remember and iterative beam search algorithms for type II assembly line balancing problem. Computers and operations research, 131, 105235. https://doi.org/10.1016/j.cor.2021.105235
[28] Meng, K., Tang, Q., Zhang, Z., & Yu, C. (2021). Solving multi-objective model of assembly line balancing considering preventive maintenance scenarios using heuristic and grey wolf optimizer algorithm. Engineering applications of artificial intelligence, 100, 104183. https://doi.org/10.1016/j.engappai.2021.104183
[29] Shafi Salimi, P., & Edalatpanah, S. A. (2020). Supplier selection using fuzzy AHP method and D-Numbers. Journal of fuzzy extension and applications, 1(1), 1-14.
[30] Khalili, N., Shahnazari Shahrezaei, P., & Abri, A. G. (2020). A multi-objective optimization approach for a nurse scheduling problem considering the fatigue factor (case study: Labbafinejad Hospital). Journal of applied research on industrial engineering, 7(4), 396-423.
[31] Mohammad, P., & Kazemipoor, H. (2020). An integrated multi-objective mathematical model to select suppliers in green supply chains. International journal of research in industrial engineering, 9(3), 216-234.
[32] El-Shorbagy, M. A., Mousa, A. A. A., ALoraby, H., & Abo-Kila, T. (2020). Evolutionary algorithm for multi-objective multi-index transportation problem under fuzziness. Journal of applied research on industrial engineering, 7(1), 36-56.
Lascelles, B. G., Taylor, P. R., Miller, M. G. R., Dias, M. P., Oppel, S., Torres, L., ... & Small, C. (2016). Applying global criteria to tracking data to define important areas for marine conservation. Diversity and distributions, 22(4), 422-431. https://doi.org/10.1111/ddi.12411