How to cite this paper
Sadaghiani, J., Boroujerdi, S., Mirhabibi, M & Sadaghiani, P. (2014). A Pareto archive floating search procedure for solving multi-objective flexible job shop scheduling problem.Decision Science Letters , 3(2), 157-168.
Refrences
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Baykaso?lu, A., & S?nmez, A. ?. (2004). Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems. Journal of Intelligent Manufacturing, 15(6), 777-785.
Chen, H., Ihlow, J., & Lehmann, C. (1999). A genetic algorithm for flexible job-shop scheduling. In Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on (Vol. 2, pp. 1120-1125). IEEE.
Dauzère-Pérès, S., & Paulli, J. (1997). An integrated approach for modeling and solving the general multi-processor job-shop scheduling problem using tabu search. Annals of Operations Research, 70, 281–306.
Deb, K. (2001), Multi-Objective Optimization using Evolutionary Algorithms. Indian Institute of Technology, Kanpur, India.
Fattahi, P., Mehrabad, M. S., & Jolai, F. (2007). Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. Journal of Intelligent Manufacturing, 18(3), 331-342.
Frutos, M., Olivera, A. C., & Tohmé, F. (2010). A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem. Annals of Operations Research, 181(1), 745-765.
Gao, J., Gen, M., Sun, L., & Zhao, X. (2007). A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Computers & Industrial Engineering, 53(1), 149-162.
Garey, M.R., Johnson, D.S., Sethi, R., (1976). The complexity of ?ow shop and job shop scheduling. Mathematics of Operations Research, 1, 117–129.
Gen, M., Gao, J., & Lin, L. (2009). Multistage-based genetic algorithm for flexible job-shop scheduling problem. In Intelligent and Evolutionary Systems(pp. 183-196). Springer Berlin Heidelberg.
Ho, N. B., Tay, J. C., & Lai, E. M. K. (2007). An effective architecture for learning and evolving flexible job-shop schedules. European Journal of Operational Research, 179(2), 316-333.
Hurink, E., Jurisch, B., & Thole, M. (1994). Tabu search for the job shop scheduling problem with multi-purpose machines. Operations Research Spectrum, 15, 205–215.
Kacem, I., Hammadi, S., & Borne, P. (2002). Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 32(1), 1-13.
Li, J. Q., Pan, Q. K., & Gao, K. Z. (2011). Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. The International Journal of Advanced Manufacturing Technology, 55(9-12), 1159-1169.
Mesghouni, K., Hammadi, S., & Borne, P. (1997, October). Evolution programs for job-shop scheduling. In Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on (Vol. 1, pp. 720-725).
Moradi, E., Fatemi Ghomi, S. M. T., & Zandieh, M. (2011). Bi-objective optimization research on integrated fixed time interval preventive maintenance and production for scheduling flexible job-shop problem. Expert systems with applications, 38(6), 7169-7178.
Paulli, J. (1995). A hierarchical approach for the FMS scheduling problem. European Journal of Operational Research, 86(1), 32–42.
Saidi-Mehrabad, M., & Fattahi, P. (2007). Flexible job shop scheduling with tabu search algorithm. International Journal of Advanced Manufacturing Technology, 32, 563-570.
Tay, J. C., & Ho, N. B. (2008). Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering, 54(3), 453-473.
Unachak, P., & Adviser-Goodman, E. (2010). An adaptive representation for a genetic algorithm in solving flexible job-shop scheduling and rescheduling problems. Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILSOPHY Computer Science.
Xia, W., & Wu, Z. (2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 48(2), 409-425.
Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. Evolutionary Computation, IEEE Transactions on, 3(4), 257-271.
Baykaso?lu, A., & S?nmez, A. ?. (2004). Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems. Journal of Intelligent Manufacturing, 15(6), 777-785.
Chen, H., Ihlow, J., & Lehmann, C. (1999). A genetic algorithm for flexible job-shop scheduling. In Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on (Vol. 2, pp. 1120-1125). IEEE.
Dauzère-Pérès, S., & Paulli, J. (1997). An integrated approach for modeling and solving the general multi-processor job-shop scheduling problem using tabu search. Annals of Operations Research, 70, 281–306.
Deb, K. (2001), Multi-Objective Optimization using Evolutionary Algorithms. Indian Institute of Technology, Kanpur, India.
Fattahi, P., Mehrabad, M. S., & Jolai, F. (2007). Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. Journal of Intelligent Manufacturing, 18(3), 331-342.
Frutos, M., Olivera, A. C., & Tohmé, F. (2010). A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem. Annals of Operations Research, 181(1), 745-765.
Gao, J., Gen, M., Sun, L., & Zhao, X. (2007). A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Computers & Industrial Engineering, 53(1), 149-162.
Garey, M.R., Johnson, D.S., Sethi, R., (1976). The complexity of ?ow shop and job shop scheduling. Mathematics of Operations Research, 1, 117–129.
Gen, M., Gao, J., & Lin, L. (2009). Multistage-based genetic algorithm for flexible job-shop scheduling problem. In Intelligent and Evolutionary Systems(pp. 183-196). Springer Berlin Heidelberg.
Ho, N. B., Tay, J. C., & Lai, E. M. K. (2007). An effective architecture for learning and evolving flexible job-shop schedules. European Journal of Operational Research, 179(2), 316-333.
Hurink, E., Jurisch, B., & Thole, M. (1994). Tabu search for the job shop scheduling problem with multi-purpose machines. Operations Research Spectrum, 15, 205–215.
Kacem, I., Hammadi, S., & Borne, P. (2002). Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 32(1), 1-13.
Li, J. Q., Pan, Q. K., & Gao, K. Z. (2011). Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. The International Journal of Advanced Manufacturing Technology, 55(9-12), 1159-1169.
Mesghouni, K., Hammadi, S., & Borne, P. (1997, October). Evolution programs for job-shop scheduling. In Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on (Vol. 1, pp. 720-725).
Moradi, E., Fatemi Ghomi, S. M. T., & Zandieh, M. (2011). Bi-objective optimization research on integrated fixed time interval preventive maintenance and production for scheduling flexible job-shop problem. Expert systems with applications, 38(6), 7169-7178.
Paulli, J. (1995). A hierarchical approach for the FMS scheduling problem. European Journal of Operational Research, 86(1), 32–42.
Saidi-Mehrabad, M., & Fattahi, P. (2007). Flexible job shop scheduling with tabu search algorithm. International Journal of Advanced Manufacturing Technology, 32, 563-570.
Tay, J. C., & Ho, N. B. (2008). Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering, 54(3), 453-473.
Unachak, P., & Adviser-Goodman, E. (2010). An adaptive representation for a genetic algorithm in solving flexible job-shop scheduling and rescheduling problems. Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILSOPHY Computer Science.
Xia, W., & Wu, Z. (2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 48(2), 409-425.
Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. Evolutionary Computation, IEEE Transactions on, 3(4), 257-271.