How to cite this paper
Toroudi, H., Madani, M., Sarlak, F & Kanani, Y. (2017). A multi-objective method for solving assembly line balancing problem.Decision Science Letters , 6(1), 1-10.
Refrences
Andres, C., Miralles, C., & Pastor, R. (2008). Balancing and scheduling tasks in assembly lines with sequence-dependent setup times. European Journal of Operational Research, 187(3), 1212-1223.
Bashiri, M., & AliAskari, E. (2014). A permutation decision making method with multiple weighting vectors of criteria using NSGA-II and MOPSO. Decision Science Letters, 3(2), 197-208.
Bautista, J., & Pereira, J. (2007). Ant algorithms for a time and space constrained assembly line balancing problem. European Journal of Operational Research, 177(3), 2016-2032.
Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168(3), 694-715.
Boysen, N., Fliedner, M., & Scholl, A. (2007). A classification of assembly line balancing problems. European Journal of Operational Research, 183(2), 674-693.
Bukchin Y, Rabinowitch I (2006) A branch-and-bound based solution approach for the mixed-model assembly line-balancing problem for minimizing stations and task duplication costs. European Journal of Operational Research, 174(1):492–508.
Cakir, B., Altiparmak, F., & Dengiz, B. (2011). Multi-objective optimization of a stochastic assembly line balancing: A hybrid simulated annealing algorithm. Computers & Industrial Engineering, 60(3), 376-384.
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.
Chica, M., Cordón, Ó., Damas, S., & Bautista, J. (2015). Interactive preferences in multiobjective ant colony optimisation for assembly line balancing. Soft Computing, 19(10), 2891-2903.
Chica, M., Cordón, O., Damas, S., & Bautista, J. (2011). A new diversity induction mechanism for a multi-objective ant colony algorithm to solve a real-world time and space assembly line balancing problem. Memetic Computing, 3(1), 15-24.
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256-279.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (Vol. 16). John Wiley & Sons.
Driscolla, J., & Abdel-Shafi, A. A. (1985). A simulation approach to evaluating assembly line balancing solutions. International Journal of Production Research, 23(5), 975–985.
Eberhart, R. C., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science (Vol. 1, pp. 39-43).
Ege, Y., Azizoglu, M., & Ozdemirel, N. E. (2009). Assembly line balancing with station paralleling. Computers & Industrial Engineering, 57(4), 1218-1225.
Erel, E., Sarin, S.C., 1998. A survey of the assembly line balancing procedures. Production Planning & Control 9(5), 414–434.
Gamberini, R., Grassi, A., & Rimini, B. (2006). A new multi-objective heuristic algorithm for solving the stochastic assembly line re-balancing problem. International Journal of Production Economics, 102(2), 226-243.
Gutjahr, A.L., & Nemhauser, G.L. (1964). An algorithm for the line balancing problem. Management Science 11(2), 308–315.
Henig, M. I. (1986). Extensions of the dynamic programming method in the deterministic and stochastic assembly-line balancing problems. Computers & Operations Research, 13(4), 443-449.
Intelligence, S. (2007). Particle swarm optimization. MCCAFFREY, James.[online].[cit. 2014-05-20]. Dostupné z: http://msdn. microsoft. com/en-us/magazine/hh335067. aspx.
Kara, Y., Özgüven, C., Seçme, N. Y., & Chang, C. T. (2011). Multi-objective approaches to balance mixed-model assembly lines for model mixes having precedence conflicts and duplicable common tasks. The International Journal of Advanced Manufacturing Technology, 52(5-8), 725-737.
Kennedy, J. (1998, March). The behavior of particles. In International Conference on Evolutionary Programming (pp. 579-589). Springer Berlin Heidelberg.
Kim, Y. K., Kim, Y. J., & Kim, Y. (1996). Genetic algorithms for assembly line balancing with various objectives. Computers & Industrial Engineering, 30(3), 397-409.
Kirkpatrick, S. (1984). Optimization by simulated annealing: Quantitative studies. Journal of Statistical Physics, 34(5-6), 975-986.
Kottas, J. F., & Lau, H. S. (1973). A Cost-Oriented Approach to Stochastic Line Balancing1. AIIE Transactions, 5(2), 164-171.
Kottas, J. F., & Lau, H. S. (1981). A stochastic line balancing procedure. International Journal of Production Research, 9(2), 177–193.
Lapierre, S. D., Ruiz, A., & Soriano, P. (2006). Balancing assembly lines with tabu search. European Journal of Operational Research, 168(3), 826-837.
Levitin, G., Rubinovitz, J., & Shnits, B. (2006). A genetic algorithm for robotic assembly line balancing. European Journal of Operational Research, 168(3), 811-825.
Low, C., Hsu, C. J., & Su, C. T. (2010). A modified particle swarm optimization algorithm for a single-machine scheduling problem with periodic maintenance. Expert Systems with Applications, 37(9), 6429-6434.
McMullen, P. R., & Tarasewich, P. (2003). Using ant techniques to solve the assembly line balancing problem. IIE Transactions, 35(7), 605-617.
Miralles, C., García-Sabater, J. P., Andrés, C., & Cardós, M. (2008). Branch and bound procedures for solving the assembly line worker assignment and balancing problem: Application to sheltered work centres for disabled. Discrete Applied Mathematics, 156(3), 352-367.
Nearchou, A. C. (2011). Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization. International Journal of Production Economics, 129(2), 242-250.
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.
Osman, I. H., & Potts, C. N. (1989). Simulated annealing for permutation flow-shop scheduling. Omega, 17(6), 551-557.
Özcan, U. (2010). Balancing stochastic two-sided assembly lines: A chance-constrained, piecewise-linear, mixed integer program and a simulated annealing algorithm. European Journal of Operational Research, 205(1), 81-97.
Pinto, P., Dannenbring, D. G., & Khumawala, B. M. (1975). A branch and bound algorithm for assembly line balancing with paralleling. International Journal of Production Research, 13(2), 183-196.
Ponnambalam, S. G., Aravindan, P., & Naidu, G. M. (2000). A multi-objective genetic algorithm for solving assembly line balancing problem. International Journal of Advanced Manufacturing Technology, 16(5), 341-352.
Ramezanian, R., & Ezzatpanah, A. (2015). Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem. Computers & Industrial Engineering, 87, 74-80.
Reeve, N. R., & Thomas, W. H. (1973). Balancing stochastic assembly lines. AIIE Transactions, 5(3), 223–229.
Rubinovitz, J., & Levitin, G. (1995). Genetic algorithm for assembly line balancing. International Journal of Production Economics, 41(1), 343-354.
Salveson, M. E. (1955). The assembly line balancing problem. Journal of Industrial Engineering, 6(3), 18-25.
Sarin, S. C., Erel, E., & Dar-El, E. M. (1999). A methodology for solving single-model, stochastic assembly line balancing problem. Omega, 27(5), 525-535.
Scholl, A., & Becker, C. (2006). State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. European Journal of Operational Research, 168(3), 666-693.
Scholl, A., & Scholl, A. (1999). Balancing and sequencing of assembly lines. Heidelberg: Physica-Verlag.
Seyed-Alagheband, S.A., Fatemi Ghomi, S.M.T., Zandieh, M., 2011. A simulated annealing algorithm for balancing the assembly line type II problem with sequence-dependent setup times between tasks. International Journal of Production Research 49(3), 805–825.
Shin, D. (1990). An efficient heuristic for solving stochastic assembly line balancing problems. Computers and Industrial Engineering, 18(3), 285–295.
Silverman, F. N., & Carter, J. C. (1986). A cost-based methodology for stochastic line balancing with intermittent line stoppages. Management Science, 32(4), 455-463.
Simaria, A. S., & Vilarinho, P. M. (2001). The simple assembly line balancing problem with parallel workstations-a simulated annealing approach. International Journal of Industrual Engineering-Theory and Applications, 8(3), 230-240.
Sungur, B., & Yavuz, Y. (2015). Assembly line balancing with hierarchical worker assignment. Journal of Manufacturing Systems, 37, 290-298.
Tasan, S. O., & Tunali, S. (2008). A review of the current applications of genetic algorithms in assembly line balancing. Journal of intelligent manufacturing, 19(1), 49-69.
Zhang, H., Yan, Q., Liu, Y., & Jiang, Z. (2016). An integer-coded differential evolution algorithm for simple assembly line balancing problem of type 2. Assembly Automation, 36(3).
Bashiri, M., & AliAskari, E. (2014). A permutation decision making method with multiple weighting vectors of criteria using NSGA-II and MOPSO. Decision Science Letters, 3(2), 197-208.
Bautista, J., & Pereira, J. (2007). Ant algorithms for a time and space constrained assembly line balancing problem. European Journal of Operational Research, 177(3), 2016-2032.
Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168(3), 694-715.
Boysen, N., Fliedner, M., & Scholl, A. (2007). A classification of assembly line balancing problems. European Journal of Operational Research, 183(2), 674-693.
Bukchin Y, Rabinowitch I (2006) A branch-and-bound based solution approach for the mixed-model assembly line-balancing problem for minimizing stations and task duplication costs. European Journal of Operational Research, 174(1):492–508.
Cakir, B., Altiparmak, F., & Dengiz, B. (2011). Multi-objective optimization of a stochastic assembly line balancing: A hybrid simulated annealing algorithm. Computers & Industrial Engineering, 60(3), 376-384.
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.
Chica, M., Cordón, Ó., Damas, S., & Bautista, J. (2015). Interactive preferences in multiobjective ant colony optimisation for assembly line balancing. Soft Computing, 19(10), 2891-2903.
Chica, M., Cordón, O., Damas, S., & Bautista, J. (2011). A new diversity induction mechanism for a multi-objective ant colony algorithm to solve a real-world time and space assembly line balancing problem. Memetic Computing, 3(1), 15-24.
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256-279.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (Vol. 16). John Wiley & Sons.
Driscolla, J., & Abdel-Shafi, A. A. (1985). A simulation approach to evaluating assembly line balancing solutions. International Journal of Production Research, 23(5), 975–985.
Eberhart, R. C., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science (Vol. 1, pp. 39-43).
Ege, Y., Azizoglu, M., & Ozdemirel, N. E. (2009). Assembly line balancing with station paralleling. Computers & Industrial Engineering, 57(4), 1218-1225.
Erel, E., Sarin, S.C., 1998. A survey of the assembly line balancing procedures. Production Planning & Control 9(5), 414–434.
Gamberini, R., Grassi, A., & Rimini, B. (2006). A new multi-objective heuristic algorithm for solving the stochastic assembly line re-balancing problem. International Journal of Production Economics, 102(2), 226-243.
Gutjahr, A.L., & Nemhauser, G.L. (1964). An algorithm for the line balancing problem. Management Science 11(2), 308–315.
Henig, M. I. (1986). Extensions of the dynamic programming method in the deterministic and stochastic assembly-line balancing problems. Computers & Operations Research, 13(4), 443-449.
Intelligence, S. (2007). Particle swarm optimization. MCCAFFREY, James.[online].[cit. 2014-05-20]. Dostupné z: http://msdn. microsoft. com/en-us/magazine/hh335067. aspx.
Kara, Y., Özgüven, C., Seçme, N. Y., & Chang, C. T. (2011). Multi-objective approaches to balance mixed-model assembly lines for model mixes having precedence conflicts and duplicable common tasks. The International Journal of Advanced Manufacturing Technology, 52(5-8), 725-737.
Kennedy, J. (1998, March). The behavior of particles. In International Conference on Evolutionary Programming (pp. 579-589). Springer Berlin Heidelberg.
Kim, Y. K., Kim, Y. J., & Kim, Y. (1996). Genetic algorithms for assembly line balancing with various objectives. Computers & Industrial Engineering, 30(3), 397-409.
Kirkpatrick, S. (1984). Optimization by simulated annealing: Quantitative studies. Journal of Statistical Physics, 34(5-6), 975-986.
Kottas, J. F., & Lau, H. S. (1973). A Cost-Oriented Approach to Stochastic Line Balancing1. AIIE Transactions, 5(2), 164-171.
Kottas, J. F., & Lau, H. S. (1981). A stochastic line balancing procedure. International Journal of Production Research, 9(2), 177–193.
Lapierre, S. D., Ruiz, A., & Soriano, P. (2006). Balancing assembly lines with tabu search. European Journal of Operational Research, 168(3), 826-837.
Levitin, G., Rubinovitz, J., & Shnits, B. (2006). A genetic algorithm for robotic assembly line balancing. European Journal of Operational Research, 168(3), 811-825.
Low, C., Hsu, C. J., & Su, C. T. (2010). A modified particle swarm optimization algorithm for a single-machine scheduling problem with periodic maintenance. Expert Systems with Applications, 37(9), 6429-6434.
McMullen, P. R., & Tarasewich, P. (2003). Using ant techniques to solve the assembly line balancing problem. IIE Transactions, 35(7), 605-617.
Miralles, C., García-Sabater, J. P., Andrés, C., & Cardós, M. (2008). Branch and bound procedures for solving the assembly line worker assignment and balancing problem: Application to sheltered work centres for disabled. Discrete Applied Mathematics, 156(3), 352-367.
Nearchou, A. C. (2011). Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization. International Journal of Production Economics, 129(2), 242-250.
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.
Osman, I. H., & Potts, C. N. (1989). Simulated annealing for permutation flow-shop scheduling. Omega, 17(6), 551-557.
Özcan, U. (2010). Balancing stochastic two-sided assembly lines: A chance-constrained, piecewise-linear, mixed integer program and a simulated annealing algorithm. European Journal of Operational Research, 205(1), 81-97.
Pinto, P., Dannenbring, D. G., & Khumawala, B. M. (1975). A branch and bound algorithm for assembly line balancing with paralleling. International Journal of Production Research, 13(2), 183-196.
Ponnambalam, S. G., Aravindan, P., & Naidu, G. M. (2000). A multi-objective genetic algorithm for solving assembly line balancing problem. International Journal of Advanced Manufacturing Technology, 16(5), 341-352.
Ramezanian, R., & Ezzatpanah, A. (2015). Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem. Computers & Industrial Engineering, 87, 74-80.
Reeve, N. R., & Thomas, W. H. (1973). Balancing stochastic assembly lines. AIIE Transactions, 5(3), 223–229.
Rubinovitz, J., & Levitin, G. (1995). Genetic algorithm for assembly line balancing. International Journal of Production Economics, 41(1), 343-354.
Salveson, M. E. (1955). The assembly line balancing problem. Journal of Industrial Engineering, 6(3), 18-25.
Sarin, S. C., Erel, E., & Dar-El, E. M. (1999). A methodology for solving single-model, stochastic assembly line balancing problem. Omega, 27(5), 525-535.
Scholl, A., & Becker, C. (2006). State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. European Journal of Operational Research, 168(3), 666-693.
Scholl, A., & Scholl, A. (1999). Balancing and sequencing of assembly lines. Heidelberg: Physica-Verlag.
Seyed-Alagheband, S.A., Fatemi Ghomi, S.M.T., Zandieh, M., 2011. A simulated annealing algorithm for balancing the assembly line type II problem with sequence-dependent setup times between tasks. International Journal of Production Research 49(3), 805–825.
Shin, D. (1990). An efficient heuristic for solving stochastic assembly line balancing problems. Computers and Industrial Engineering, 18(3), 285–295.
Silverman, F. N., & Carter, J. C. (1986). A cost-based methodology for stochastic line balancing with intermittent line stoppages. Management Science, 32(4), 455-463.
Simaria, A. S., & Vilarinho, P. M. (2001). The simple assembly line balancing problem with parallel workstations-a simulated annealing approach. International Journal of Industrual Engineering-Theory and Applications, 8(3), 230-240.
Sungur, B., & Yavuz, Y. (2015). Assembly line balancing with hierarchical worker assignment. Journal of Manufacturing Systems, 37, 290-298.
Tasan, S. O., & Tunali, S. (2008). A review of the current applications of genetic algorithms in assembly line balancing. Journal of intelligent manufacturing, 19(1), 49-69.
Zhang, H., Yan, Q., Liu, Y., & Jiang, Z. (2016). An integer-coded differential evolution algorithm for simple assembly line balancing problem of type 2. Assembly Automation, 36(3).