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How to cite this paper
APA: Kang, H., Lee, A & Su, Y. (2025). Multi-objective mixed-model assembly line balancing with hierarchical worker assignment: A case study of gear reducer manufacturing operations. International Journal of Industrial Engineering Computations, 16(1), 69-92.
Chicago/Turabian: Kang, H., Lee, A & Su, Y. 2025. "Multi-objective mixed-model assembly line balancing with hierarchical worker assignment: A case study of gear reducer manufacturing operations." International Journal of Industrial Engineering Computations 16, no. 1 (2025): 69-92.
AMA: Kang, H., Lee, A & Su, Y. Multi-objective mixed-model assembly line balancing with hierarchical worker assignment: A case study of gear reducer manufacturing operations. International Journal of Industrial Engineering Computations. 2025;16(1):69-92.
References
Ab. Rashid, M.F.F., Mohd Rose, A.N., Nik Mohamed, N.M.Z., & Mohd Romlay, F.R. (2020). Improved moth flame optimization algorithm to optimize cost-oriented two-sided assembly line balancing. Engineering Computations, 37(2), 638-663.
Alhomaidhi, E. (2024). Enhancing efficiency and adaptability in mixed model line balancing through the fusion of learning effects and worker prerequisites. International Journal of Industrial Engineering Computations, 15(2), 541-552.
Bartholdi, J.J. (1993). Balancing two-sided assembly lines: a case study. International Journal of Production Research, 31(10), 2447-2461.
Belkharroubi, L., & Yahyaoui, K. (2022). Solving the mixed-model assembly line balancing problem type-I using a hybrid reactive GRASP. Production and Manufacturing Research, 10(1), 108-131.
Campana, N.P., Iori, M., & Moreira, M.C.O. (2022). Mathematical models and heuristic methods for the assembly line balancing problem with hierarchical worker assignment. International Journal of Production Research, 60(7), 2193-2211.
Chen, J.C., Chen, Y.Y., Chen, T.L., & Kuo, Y.H. (2019). Applying two-phase adaptive genetic algorithm to solve multi-model assembly line balancing problems in TFT-LCD Module Process. Journal of Manufacturing Systems, 52, 86-99.
Chen, J., Jia, X., & He, Q. (2023). A novel bi-level multi-objective genetic algorithm for integrated assembly line balancing and part feeding problem. International Journal of Production Research, 61(2), 580-603.
Chutima, P., & Khotsaenlee, A. (2022). Multi-objective parallel adjacent U-shaped assembly line balancing collaborated by robots and normal and disabled workers. Computers & Operations Research, 143, 105775.
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.
Delice, Y., Aydoğan, E.K., Himmetoğlu, S., & Özcan, U. (2023). Integrated mixed-model assembly line balancing and parts feeding with supermarkets. CIRP Journal of Manufacturing Science and Technology, 41, 1-18.
Faccio, M., Gamberi, M., & Bortolini, M. (2016). Hierarchical approach for paced mixed-model assembly line balancing and sequencing with jolly operators. International Journal of Production Research, 54(3), 761-777.
Goldberg, D.E., Korb, B., & Deb, K. (1989). Messy genetic algorithms: motivation, analysis, and first results. Complex Systems, 3(5), 493-530.
Kang, H.-Y., & Lee, A.H.I. (2023). An evolutionary genetic algorithm for a multi-objective two-sided assembly line balancing problem: a case study of automotive manufacturing operations. Quality Technology and Quantitative Management, 20(1), 66-88.
Korytkowski, P. (2017). Competences-based performance model of multi-skilled workers with learning and forgetting. Expert Systems with Applications, 77, 226-235.
Lee, A.H. I., Kang, H.-Y., & Chen, C.-L. (2021). Multi-objective assembly line balancing problem with setup times using fuzzy goal programming and genetic algorithm. Symmetry, 13(2), 1-28.
Li, D., Zhang, C., Tian, G., Shao, X., & Li, Z. (2018). Multiobjective program and hybrid imperialist competitive algorithm for the mixed-model two-sided assembly lines subject to multiple constraints. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(1), 119-129.
Li, S., Butterfield, J., & Murphy, A. (2023). A new multi-objective genetic algorithm for assembly line balancing. Journal of Computing and Information Science in Engineering, 23(3), 034502.
Liao, S.-G., Zhang, Y.-B., Sang, C.-Y., & Liu, H. (2023). A genetic algorithm for balancing and sequencing of mixed-model two-sided assembly line with unpaced synchronous transfer. Applied Soft Computing, 146, 110638.
Lin, W.Y., Lee, W.Y., & Hong, T.P. (2003). Adapting crossover and mutation rates in genetic algorithms. Journal of Information Science and Engineering, 19(5), 889-903.
LINGO User’s Manual (2018). version 18. LINGO System Inc., Chicago.
Liu, X., Yang, X., & Lei, M. (2021). Optimisation of mixed-model assembly line balancing problem under uncertain demand. Journal of Manufacturing Systems, 59, 214-227.
Liu, Y., Shen, W., Zhang, C., & Sun, X. (2023). Agent-based simulation and optimization of hybrid flow shop considering multi-skilled workers and fatigue factors. Robotics and Computer-Integrated Manufacturing, 80, 102478.
Małachowski, B., & Korytkowski, P. (2016). Competence-based performance model of multi-skilled Workers. Computers and Industrial Engineering, 91, 165–177.
MATLAB User’s Manual (2019). version 2019. The MathWorks, Inc., Massachusetts.
Meng, K., Tang, Q., Cheng, L., & Zhang, Z. (2022). Mixed-model assembly line balancing problem considering preventive maintenance scenarios: MILP model and cooperative co-evolutionary algorithm. Applied Soft Computing, 127, 109341.
Nourmohammadi, A., Ng, A.H.C., Fathi, M., Vollebregt, J., Hanson, L. (2023). Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling. CIRP Journal of Manufacturing Science and Technology, 47, 71-85.
Özcan, U., & Toklu, B. (2009). Balancing of mixed-model two-sided assembly lines. Computers and Industrial Engineering, 57, 217-227.
Rabbani, M., Behbahan, S.Z.B., & Farrokhi-Asl, H. (2020). The collaboration of human-robot in mixed-model four-sided assembly line balancing problem. Journal of Intelligent & Robotic Systems, 100, 71-81.
Rahman, H.F., Janardhanan, M.N., & Ponnambalam, S.G. (2023). Energy aware semi-automatic assembly line balancing problem considering ergonomic risk and uncertain processing time. Expert Systems with Applications, 231, 120737.
Razali, M.M., Kamarudin, N.H., Ab. Rashid, M.F.F., & Mohd Rose, A.N. (2019). Recent trend in mixed-model assembly line balancing optimization using soft computing approaches. Engineering Computations, 36(2), 622-645.
Saif, U., Guan, Z., Liu, W. Wang, B., & Zhang, C. (2014). Multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line. International Journal of Advanced Manufacturing Technology, 75, 1809-1827.
Samouei, P., & Sobhishoja, M. (2023). Robust counterpart mathematical models for balancing, sequencing, and assignment of robotic U-shaped assembly lines with considering failures and setup times. OPSEARCH, 60, 87-124.
Simaria, A.S., & Vilarinho, P.M. (2004). A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II. Computers & Industrial Engineering, 47, 391-407.
Soysal-Kurt, H., İşleyen, S.K., Gökçen, H. (2024). Balancing and sequencing of mixed-model parallel robotic assembly lines considering energy consumption. Flexible Services and Manufacturing Journal, Online first.
Sun, X., Guo, S., Guo, J., Du, B., Yang, Z., & Wang, K. (2024). A Pareto-based hybrid genetic simulated annealing algorithm for multi-objective hybrid production line balancing problem considering disassembly and assembly. International Journal of Production Research, 62(13):4809-4830.
Sungur, B., & Yavuz, Y. (2015). Assembly line balancing with hierarchical worker assignment. Journal of Manufacturing Systems, 37, 290-298.
Tanhaie, F., Rabbani, M., & Manavizadeh, N. (2020). Simultaneous balancing and worker assignment problem for mixed-model assembly lines in a make-to-order environment considering control points and assignment restrictions. Journal of Modelling in Management, 15(1), 1-34.
Tonge, F. M. (1961). A heuristic program for assembly line balancing. Prentice-Hall, New Jersey.
Zhang, B., Xu, L., & Zhang, J. (2020). A multi-objective cellular genetic algorithm for energy-oriented balancing and sequencing problem of mixed-model assembly line. Journal of Cleaner Production, 244, 118845.
Zhang, J.H., Li, A.P., & Liu, X.M. (2019). Hybrid genetic algorithm for a type-II robust mixed-model assembly line balancing problem with interval task times. Advances in Manufacturing, 7, 117-132.
Zhang, Z., Tang, Q., & Chica, M. (2021). A robust MILP and gene expression programming based on heuristic rules for mixed-model multi-manned assembly line balancing. Applied Soft Computing, 109, 107513.
Alhomaidhi, E. (2024). Enhancing efficiency and adaptability in mixed model line balancing through the fusion of learning effects and worker prerequisites. International Journal of Industrial Engineering Computations, 15(2), 541-552.
Bartholdi, J.J. (1993). Balancing two-sided assembly lines: a case study. International Journal of Production Research, 31(10), 2447-2461.
Belkharroubi, L., & Yahyaoui, K. (2022). Solving the mixed-model assembly line balancing problem type-I using a hybrid reactive GRASP. Production and Manufacturing Research, 10(1), 108-131.
Campana, N.P., Iori, M., & Moreira, M.C.O. (2022). Mathematical models and heuristic methods for the assembly line balancing problem with hierarchical worker assignment. International Journal of Production Research, 60(7), 2193-2211.
Chen, J.C., Chen, Y.Y., Chen, T.L., & Kuo, Y.H. (2019). Applying two-phase adaptive genetic algorithm to solve multi-model assembly line balancing problems in TFT-LCD Module Process. Journal of Manufacturing Systems, 52, 86-99.
Chen, J., Jia, X., & He, Q. (2023). A novel bi-level multi-objective genetic algorithm for integrated assembly line balancing and part feeding problem. International Journal of Production Research, 61(2), 580-603.
Chutima, P., & Khotsaenlee, A. (2022). Multi-objective parallel adjacent U-shaped assembly line balancing collaborated by robots and normal and disabled workers. Computers & Operations Research, 143, 105775.
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.
Delice, Y., Aydoğan, E.K., Himmetoğlu, S., & Özcan, U. (2023). Integrated mixed-model assembly line balancing and parts feeding with supermarkets. CIRP Journal of Manufacturing Science and Technology, 41, 1-18.
Faccio, M., Gamberi, M., & Bortolini, M. (2016). Hierarchical approach for paced mixed-model assembly line balancing and sequencing with jolly operators. International Journal of Production Research, 54(3), 761-777.
Goldberg, D.E., Korb, B., & Deb, K. (1989). Messy genetic algorithms: motivation, analysis, and first results. Complex Systems, 3(5), 493-530.
Kang, H.-Y., & Lee, A.H.I. (2023). An evolutionary genetic algorithm for a multi-objective two-sided assembly line balancing problem: a case study of automotive manufacturing operations. Quality Technology and Quantitative Management, 20(1), 66-88.
Korytkowski, P. (2017). Competences-based performance model of multi-skilled workers with learning and forgetting. Expert Systems with Applications, 77, 226-235.
Lee, A.H. I., Kang, H.-Y., & Chen, C.-L. (2021). Multi-objective assembly line balancing problem with setup times using fuzzy goal programming and genetic algorithm. Symmetry, 13(2), 1-28.
Li, D., Zhang, C., Tian, G., Shao, X., & Li, Z. (2018). Multiobjective program and hybrid imperialist competitive algorithm for the mixed-model two-sided assembly lines subject to multiple constraints. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(1), 119-129.
Li, S., Butterfield, J., & Murphy, A. (2023). A new multi-objective genetic algorithm for assembly line balancing. Journal of Computing and Information Science in Engineering, 23(3), 034502.
Liao, S.-G., Zhang, Y.-B., Sang, C.-Y., & Liu, H. (2023). A genetic algorithm for balancing and sequencing of mixed-model two-sided assembly line with unpaced synchronous transfer. Applied Soft Computing, 146, 110638.
Lin, W.Y., Lee, W.Y., & Hong, T.P. (2003). Adapting crossover and mutation rates in genetic algorithms. Journal of Information Science and Engineering, 19(5), 889-903.
LINGO User’s Manual (2018). version 18. LINGO System Inc., Chicago.
Liu, X., Yang, X., & Lei, M. (2021). Optimisation of mixed-model assembly line balancing problem under uncertain demand. Journal of Manufacturing Systems, 59, 214-227.
Liu, Y., Shen, W., Zhang, C., & Sun, X. (2023). Agent-based simulation and optimization of hybrid flow shop considering multi-skilled workers and fatigue factors. Robotics and Computer-Integrated Manufacturing, 80, 102478.
Małachowski, B., & Korytkowski, P. (2016). Competence-based performance model of multi-skilled Workers. Computers and Industrial Engineering, 91, 165–177.
MATLAB User’s Manual (2019). version 2019. The MathWorks, Inc., Massachusetts.
Meng, K., Tang, Q., Cheng, L., & Zhang, Z. (2022). Mixed-model assembly line balancing problem considering preventive maintenance scenarios: MILP model and cooperative co-evolutionary algorithm. Applied Soft Computing, 127, 109341.
Nourmohammadi, A., Ng, A.H.C., Fathi, M., Vollebregt, J., Hanson, L. (2023). Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling. CIRP Journal of Manufacturing Science and Technology, 47, 71-85.
Özcan, U., & Toklu, B. (2009). Balancing of mixed-model two-sided assembly lines. Computers and Industrial Engineering, 57, 217-227.
Rabbani, M., Behbahan, S.Z.B., & Farrokhi-Asl, H. (2020). The collaboration of human-robot in mixed-model four-sided assembly line balancing problem. Journal of Intelligent & Robotic Systems, 100, 71-81.
Rahman, H.F., Janardhanan, M.N., & Ponnambalam, S.G. (2023). Energy aware semi-automatic assembly line balancing problem considering ergonomic risk and uncertain processing time. Expert Systems with Applications, 231, 120737.
Razali, M.M., Kamarudin, N.H., Ab. Rashid, M.F.F., & Mohd Rose, A.N. (2019). Recent trend in mixed-model assembly line balancing optimization using soft computing approaches. Engineering Computations, 36(2), 622-645.
Saif, U., Guan, Z., Liu, W. Wang, B., & Zhang, C. (2014). Multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line. International Journal of Advanced Manufacturing Technology, 75, 1809-1827.
Samouei, P., & Sobhishoja, M. (2023). Robust counterpart mathematical models for balancing, sequencing, and assignment of robotic U-shaped assembly lines with considering failures and setup times. OPSEARCH, 60, 87-124.
Simaria, A.S., & Vilarinho, P.M. (2004). A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II. Computers & Industrial Engineering, 47, 391-407.
Soysal-Kurt, H., İşleyen, S.K., Gökçen, H. (2024). Balancing and sequencing of mixed-model parallel robotic assembly lines considering energy consumption. Flexible Services and Manufacturing Journal, Online first.
Sun, X., Guo, S., Guo, J., Du, B., Yang, Z., & Wang, K. (2024). A Pareto-based hybrid genetic simulated annealing algorithm for multi-objective hybrid production line balancing problem considering disassembly and assembly. International Journal of Production Research, 62(13):4809-4830.
Sungur, B., & Yavuz, Y. (2015). Assembly line balancing with hierarchical worker assignment. Journal of Manufacturing Systems, 37, 290-298.
Tanhaie, F., Rabbani, M., & Manavizadeh, N. (2020). Simultaneous balancing and worker assignment problem for mixed-model assembly lines in a make-to-order environment considering control points and assignment restrictions. Journal of Modelling in Management, 15(1), 1-34.
Tonge, F. M. (1961). A heuristic program for assembly line balancing. Prentice-Hall, New Jersey.
Zhang, B., Xu, L., & Zhang, J. (2020). A multi-objective cellular genetic algorithm for energy-oriented balancing and sequencing problem of mixed-model assembly line. Journal of Cleaner Production, 244, 118845.
Zhang, J.H., Li, A.P., & Liu, X.M. (2019). Hybrid genetic algorithm for a type-II robust mixed-model assembly line balancing problem with interval task times. Advances in Manufacturing, 7, 117-132.
Zhang, Z., Tang, Q., & Chica, M. (2021). A robust MILP and gene expression programming based on heuristic rules for mixed-model multi-manned assembly line balancing. Applied Soft Computing, 109, 107513.