[1] Groover, M. P. (1980). Automation, production systems, and computer-aided manufacturing (Vol. 1). Englewood Cliffs, NJ: Prentice-Hall.
[2] Salveson, M. E. (1955). The assembly line balancing problem. The journal of industrial engineering, 6(3), 18-25.
[3] Gutjahr, A. L., & Nemhauser, G. L. (1964). An algorithm for the line balancing problem. Management science, 11(2), 308-315.
[4] Falkenauer, E., & Delchambre, A. (1992, May). A genetic algorithm for bin packing and line balancing. Proceedings of the 1992 IEEE international conference on robotics and automation (pp. 1186-1192). Nice, France, France: IEEE.
[5] Ajenblit, D. A., & Wainwright, R. L. (1998, May). Applying genetic algorithms to the U-shaped assembly line balancing problem. Proceedings of the 1998 IEEE international conference on evolutionary computation. IEEE world congress on computational intelligence (Cat. No. 98TH8360) (pp. 96-101). Anchorage, AK, USA, USA: IEEE.
[6] Miltenburg, G. J., & Wijngaard, J. (1994). The U-line line balancing problem. Management science, 40(10), 1378-1388.
[7] Urban, T. L. (1998). Note. Optimal balancing of U-shaped assembly lines. Management science, 44(5), 738-741.
[8] Scholl, A., & Klein, R. (1999). ULINO: Optimally balancing U-shaped JIT assembly lines. International journal of production research, 37(4), 721-736.
[9] Özcan, U., Kellegöz, T., & Toklu, B. (2011). A genetic algorithm for the stochastic mixed-model U-line balancing and sequencing problem. International journal of production research, 49(6), 1605-1626.
[10] Jonnalagedda, V., & Dabade, B. (2014). Application of simple genetic algorithm to U-shaped assembly line balancing problem of type II. IFAC proceedings volumes, 47(3), 6168-6173.
[11] Alavidoost, M. H., Tarimoradi, M., & Zarandi, M. F. (2015). Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems. Applied soft computing, 34, 655-677.
[12] Nilakantan, J. M., Ponnambalam, S. G., & Nielsen, P. (2018). Energy-Efficient straight robotic assembly line using metaheuristic algorithms. Soft computing: theories and applications (pp. 803-814). Singapore: Springer.
[13] Rubinovitz, J., Bukchin, J., & Lenz, E. (1993). RALB–A heuristic algorithm for design and balancing of robotic assembly lines. CIRP annals, 42(1), 497-500.
[14] Nilakantan, J. M., Nielsen, I., Ponnambalam, S. G., & Venkataramanaiah, S. (2017). Differential evolution algorithm for solving RALB problem using cost-and time-based models. The international journal of advanced manufacturing technology, 89(1-4), 311-332.
[15] Rubinovitz, J., & Levitin, G. (1995). Genetic algorithm for assembly line balancing. International journal of production economics, 41(1-3), 343-354.
[16] Kim, H., & Park, S. (1995). A strong cutting plane algorithm for the robotic assembly line balancing problem. International journal of production research, 33(8), 2311-2323.
[17] Delice, Y., Aydoğan, E. K., Özcan, U., & İlkay, M. S. (2017). A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing. Journal of intelligent manufacturing, 28(1), 23-36.
[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.
[19] 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.
[20] Kara, Y., Ozcan, U., & Peker, A. (2007). An approach for balancing and sequencing mixed-model JIT U-lines. The international journal of advanced manufacturing technology, 32(11-12), 1218-1231.
[21] Kucukkoc, I., & Zhang, D. Z. (2014). Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines. International journal of production research, 52(12), 3665-3687.
[22] Simaria, A. S., & Vilarinho, P. M. (2009). 2-ANTBAL: An ant colony optimization algorithm for balancing two-sided assembly lines. Computers & industrial engineering, 56(2), 489-506.
[23] Aghajani, M., Ghodsi, R., & Javadi, B. (2014). Balancing of robotic mixed-model two-sided assembly line with robot setup times. The international journal of advanced manufacturing technology, 74(5-8), 1005-1016.
[24] Rabbani, M., Mousavi, Z., & Farrokhi-Asl, H. (2016). Multi-objective metaheuristics for solving a type II robotic mixed-model assembly line balancing problem. Journal of industrial and production engineering, 33(7), 472-484.
[25] Çil, Z. A., Mete, S., & Ağpak, K. (2017). Analysis of the type II robotic mixed-model assembly line balancing problem. Engineering optimization, 49(6), 990-1009.
[26] Levitin, G., Rubinovitz, J., & Shnits, B. (2006). A genetic algorithm for robotic assembly line balancing. European journal of operational research, 168(3), 811-825.
[27] Gao, J., Sun, L., Wang, L., & Gen, M. (2009). An efficient approach for type II robotic assembly line balancing problems. Computers & industrial engineering, 56(3), 1065-1080.
[28] Yoosefelahi, A., Aminnayeri, M., Mosadegh, H., & Ardakani, H. D. (2012). Type II robotic assembly line balancing problem: an evolution strategies algorithm for a multi-objective model. Journal of manufacturing systems, 31(2), 139-151.
[29] Daoud, S., Chehade, H., Yalaoui, F., & Amodeo, L. (2014). Solving a robotic assembly line balancing problem using efficient hybrid methods. Journal of heuristics, 20(3), 235-259.
[30] Nilakantan, J. M., Huang, G. Q., & Ponnambalam, S. G. (2015). An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems. Journal of cleaner production, 90, 311-325.
[31] Nilakantan, J. M., Ponnambalam, S. G., & Huang, G. Q. (2015). Minimizing energy consumption in a U-shaped robotic assembly line. 2015 international conference on advanced mechatronic systems (ICAMechS) (pp. 119-124). IEEE.
[32] Nilakantan, M. J., Ponnambalam, S. G., & Jawahar, N. (2016). Design of energy efficient RAL system using evolutionary algorithms. Engineering computations, 33(2), 580-602.
[33] Manavizadeh, N., Hosseini, N. S., Rabbani, M., & Jolai, F. (2013). A Simulated Annealing algorithm for a mixed model assembly U-line balancing type-I problem considering human efficiency and Just-In-Time approach. Computers & industrial engineering, 64(2), 669-685.
[34] Mukund Nilakantan, J., & Ponnambalam, S. G. (2016). Robotic U-shaped assembly line balancing using particle swarm optimization. Engineering optimization, 48(2), 231-252.
[35] Li, Z., Tang, Q., & Zhang, L. (2016). Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm. Journal of cleaner production, 135, 508-522.
[36] Chan, J. K., & Shaw, L. (1993). Modeling repairable systems with failure rates that depend on age and maintenance. IEEE transactions on reliability, 42(4), 566-571.
[37] Charnes, A., & Cooper, W. W. (1957). Management models and industrial applications of linear programming. Management science, 4(1), 38-91.
[38] Ignizio, J. P. (1985). Introduction to linear goal programming. Sage Publications.
[39] Lee, S. M. (1972). Goal programming for decision analysis(pp. 252-260). Philadelphia: Auerbach Publishers.
[40] Li, H. L. (1996). An efficient method for solving linear goal programming problems. Journal of optimization theory and applications, 90(2), 465-469.
[41] Pal, B. B., Moitra, B. N., & Maulik, U. (2003). A goal programming procedure for fuzzy multiobjective linear fractional programming problem. Fuzzy sets and systems, 139(2), 395-405.
[42] Schott, J. R. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization (Master’s Thesis, Boston, MA: Department of Aeronautics and Astronautics, Massachusetts Institute of Technology).
[43] Kalyanmoy, D. (2001). Multi objective optimization using evolutionary algorithms (pp. 124-124). John Wiley and Sons.
Kao, E. P. (1976). A preference `order dynamic program for stochastic assembly line balancing. Management science, 22(10), 1097-1104.