How to cite this paper
Frutos, M., Tohmé, F., Delbianco, F & Miguel, F. (2016). An alternative hybrid evolutionary technique focused on allocating machines and sequencing operations.International Journal of Industrial Engineering Computations , 7(4), 585-596.
Refrences
Agnetis, A., Flamini, M., Nicosia, G. & Pacifici, A. (2001). A job-shop problem with one additional resource type. Journal of Scheduling, 14(3), 225-237.
Armentano, V. A. & Scrich, C. R. (2000). Tabu search for minimizing total tardiness in a job-Shop. International Journal Production Economics, 63(2), 131-140.
Bihlmaier, R., Koberstein, A. & Obst, R. (2009). Modeling and optimizing of strategic and tactical production planning in the automotive industry under uncertainty. OR Spectrum, 31(2), 311-336.
Bleuler, S., Laumanns, M., Thiele, L. & Zitzler, E. (2003). PISA: A platform and programming language independent interface for search algorithms. Evolutionary Multi-Criterion Optimization, 2632, 494-508.
Brandimarte P. (1993). Routing and scheduling in a flexible job-shop by tabu search. Annals of Operations Research, 41(1), 157-183.
Chao-Hsien, J. & Han-Chiang, H. (2009). A hybrid genetic algorithm for no-wait job-shop scheduling problems. Expert Systems with Applications, 36 (3), 5800-5806.
Chinyao, L. & Yuling, Y. (2009). Genetic algorithm-based heuristics for an open shop scheduling problem with setup, processing, and removal times separated. Robotics and Computer-Integrated Manufacturing, 25(2), 314-322.
Coello Coello, C. A., Lamont, G. B. & Veldhuizen, D. A. (2006). Evolutionary Algorithms For Solving Multi-Objective Problems. Genetic and Evolutionary Computation. New York, Springer-Verlag.
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGAII. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
Della Croce, F., Grosso, A. & Salassa, F. (2014). A matheuristic approach for the two-machine total completion time flow-shop problem. Annals of Operations Research, 213(1), 67-78.
Fattahi, P., Saidi, M. & Jolai, F. (2007). Mathematical modeling and heuristic approaches to flexible job-shop scheduling problems. Jounal of Intelligent Manufacturing, 8(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, 745-765.
Frutos, M. & Tohmé, F. (2015). Choice of a PISA selector in a hybrid algorithmic structure for the FJSSP. Decision Science Letters, 4(1), 247-260.
Frutos, M. & Tohmé, F. (2009). Desarrollo de un procedimiento genético diseñado para programar la producción en un sistema de manufactura tipo job-shop. Proc. VI Congreso Español sobre Meta-heurísticas, Algoritmos Evolutivos y Bioinspirados, Málaga, Spain, 23-30.
Goldberg, D. E. (1989). Genetic Algorithms In Search. Optimization and Machine Learning. Massachusetts, Addison Wesley.
Hansmann, R. S., Rieger, T. & Zimmermann, U. T. (2014). Flexible job shop scheduling with blockages. Mathematical Methods of Operations Research, 79(2), 135-161.
Heckman, I. & Beck, J. C. (2011). Understanding the behavior of solution-guided search for job-shop scheduling. Journal of Scheduling, 14 (2), 121-140.
Heinonen, J. & Pettersson, F. (2007). Hybrid ant colony optimization and visibility studies applied to a job-shop scheduling problem. Applied Mathematics and Computation, 187(2), 989-998.
Ho, N. B., Tay, J. C. & Lai, E. M. (2007). An effective architecture for learning and evolving flexible job-shop schedules. European Journal of Operational Research, 179(2), 316-333.
Ho, N. B. & Tay, J. C. (2005). Evolving dispatching rules for solving the flexible job-shop problem. Proc. IEEE Congress on Evolutionary Computation, 3, 2848-2855.
Kacem, I, Hammadi, S. & Borne, P. (2002). Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man, and Cybernetics, 32(1), 1-13.
Knowles, J., Thiele, L. & Zitzler, E. (2005). A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK Computer Engineering and Networks Laboratory.
Li, J., Pan, Q., Xie, S. & Wang, S. (2011). A hybrid artificial bee colony algorithm for flexible job shop scheduling problems. International Journal of Computers Communications & Control, 6(2), 286-296.
Li, J., Pan, Q. & Chen, J. (2012). An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Journal of Production Research, 50(4), 1063-1078.
Li, J., Pan, Q. & Gao, K. (2011). Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling pProblems. International Journal of Advanced Manufacturing Technology, 55(9), 1159-1169.
Lin, Y., Pfund, M. & Fowler, J. (2011). Heuristics for minimizing regular performance measures in unrelated parallel machine scheduling problems. Computers & Operations Research, 38(6), 901-916.
Mesghouni, K., Hammadi, S. & Borne, P. (1997). Evolution programs for job-shop scheduling. Proc. IEEE International Conference on Systems, Man, and Cybernetics, 1, 720-725.
Nazarathy, Y. & Weiss, G. (2010). A fluid approach to large volume job shop scheduling. Journal of Scheduling, 13(5), 509-529.
Nidhiry, N. M. & Saravanan, R. (2012). Evaluation of genetic algorithm approach for scheduling optimization of flexible manufacturing systems. International Journal of Engineering Research and Applications, 2(4), 437-446.
Nowicki, E. & Smutnicki, C. (2005). An advanced tabu search algorithm for the job shop problem. Journal of Scheduling, 8(2), 145-159.
Papadimitriou, C. H. (1994). Computational Complexity. USA, Addison Wesley.
Pezzella, F., Morganti, G. & Ciaschetti, G. (2008). A genetic algorithm for the flexible job-shop scheduling problem. Journal Computers and Operations Research, 35(10), 3202-3212.
Tay, J. C. & Wibowo, D. (2004). An effective chromosome representation for evolving flexible job-shop schedules. Proc. GECCO 2004, LNCS 3103, 210-221.
Tsai, C. F. & Lin, F. C. (2003). A new hybrid heuristic technique for solving job-shop scheduling problems. Proc. Second IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications.
Ullman, J. D. (1975). NP-complete scheduling problems. Journal of Computer System Sciences, 10, 384-393.
Van Laarhoven, P. J. M., Aarts, E. H. L. & Lenstra, J. K. (1992). Job-shop scheduling by simulated annealing. Operations Research, 40(1), 113-125.
Wu, C. G., Xing, X. L., Lee, H. P, Zhou, C. G. & Liang, Y. C. (2004). Genetic algorithm application on the job-shop scheduling problem. Proc. 2004 International Conference Machine Learning and Cybernetics, 4, 2102-2106.
Xing, L. N., Chen, Y. W. & Yang, K. W. (2011). Multi-population interactive coevolutionary algorithm for flexible job-shop scheduling problems. Computational Optimization and Applications, 48, 139-155.
Xiong, J., Tan, X., Yang, K., Xing, L. & Chen, Y. (2012). A hybrid multiobjective evolutionary approach for flexible job shop scheduling problems. Mathematical Problems in Engineering, 1, 1-27.
Yang, S., Wang, D., Chai, T. & Kendall, G. (2010). An improved constraint satisfaction adaptive neural network for job-shop scheduling. Journal of Scheduling, 13(1), 17-38.
Yazdani, M., Zandieh, M. & Amiri, M. (2010). Flexible job-shop scheduling with parallel variable neighborhood search algorithm. Expert Systems with Applications: An International Journal, 37(1), 678-687.
Yuan, Y. & Xu, H. (2015). Multiobjective flexible job shop scheduling using memetic algorithms. IEEE Transactions on Automation Science and Engineering, 12 (1), 336-353.
Zhang, G. & Gen, M. (2005). Multistaged-based genetic algorithm for flexible job-shop scheduling problem. Complexity International, 11, 223-232.
Zitzler, E. & Künzli, S. (2004). Indicator-based selection in multiobjective search. Proc. Conference on Parallel Problem Solving from Nature (PPSN VIII), LNCS 3242, 832-842.
Zitzler, E., Laumanns, M. & Thiele, L. (2002). SPEAII: Improving the strength pareto evolutionary algorithm for multi-objective optimization. Evolutionary Methods for Design, Optimisations and Control, 19-26.
Armentano, V. A. & Scrich, C. R. (2000). Tabu search for minimizing total tardiness in a job-Shop. International Journal Production Economics, 63(2), 131-140.
Bihlmaier, R., Koberstein, A. & Obst, R. (2009). Modeling and optimizing of strategic and tactical production planning in the automotive industry under uncertainty. OR Spectrum, 31(2), 311-336.
Bleuler, S., Laumanns, M., Thiele, L. & Zitzler, E. (2003). PISA: A platform and programming language independent interface for search algorithms. Evolutionary Multi-Criterion Optimization, 2632, 494-508.
Brandimarte P. (1993). Routing and scheduling in a flexible job-shop by tabu search. Annals of Operations Research, 41(1), 157-183.
Chao-Hsien, J. & Han-Chiang, H. (2009). A hybrid genetic algorithm for no-wait job-shop scheduling problems. Expert Systems with Applications, 36 (3), 5800-5806.
Chinyao, L. & Yuling, Y. (2009). Genetic algorithm-based heuristics for an open shop scheduling problem with setup, processing, and removal times separated. Robotics and Computer-Integrated Manufacturing, 25(2), 314-322.
Coello Coello, C. A., Lamont, G. B. & Veldhuizen, D. A. (2006). Evolutionary Algorithms For Solving Multi-Objective Problems. Genetic and Evolutionary Computation. New York, Springer-Verlag.
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGAII. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
Della Croce, F., Grosso, A. & Salassa, F. (2014). A matheuristic approach for the two-machine total completion time flow-shop problem. Annals of Operations Research, 213(1), 67-78.
Fattahi, P., Saidi, M. & Jolai, F. (2007). Mathematical modeling and heuristic approaches to flexible job-shop scheduling problems. Jounal of Intelligent Manufacturing, 8(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, 745-765.
Frutos, M. & Tohmé, F. (2015). Choice of a PISA selector in a hybrid algorithmic structure for the FJSSP. Decision Science Letters, 4(1), 247-260.
Frutos, M. & Tohmé, F. (2009). Desarrollo de un procedimiento genético diseñado para programar la producción en un sistema de manufactura tipo job-shop. Proc. VI Congreso Español sobre Meta-heurísticas, Algoritmos Evolutivos y Bioinspirados, Málaga, Spain, 23-30.
Goldberg, D. E. (1989). Genetic Algorithms In Search. Optimization and Machine Learning. Massachusetts, Addison Wesley.
Hansmann, R. S., Rieger, T. & Zimmermann, U. T. (2014). Flexible job shop scheduling with blockages. Mathematical Methods of Operations Research, 79(2), 135-161.
Heckman, I. & Beck, J. C. (2011). Understanding the behavior of solution-guided search for job-shop scheduling. Journal of Scheduling, 14 (2), 121-140.
Heinonen, J. & Pettersson, F. (2007). Hybrid ant colony optimization and visibility studies applied to a job-shop scheduling problem. Applied Mathematics and Computation, 187(2), 989-998.
Ho, N. B., Tay, J. C. & Lai, E. M. (2007). An effective architecture for learning and evolving flexible job-shop schedules. European Journal of Operational Research, 179(2), 316-333.
Ho, N. B. & Tay, J. C. (2005). Evolving dispatching rules for solving the flexible job-shop problem. Proc. IEEE Congress on Evolutionary Computation, 3, 2848-2855.
Kacem, I, Hammadi, S. & Borne, P. (2002). Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man, and Cybernetics, 32(1), 1-13.
Knowles, J., Thiele, L. & Zitzler, E. (2005). A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK Computer Engineering and Networks Laboratory.
Li, J., Pan, Q., Xie, S. & Wang, S. (2011). A hybrid artificial bee colony algorithm for flexible job shop scheduling problems. International Journal of Computers Communications & Control, 6(2), 286-296.
Li, J., Pan, Q. & Chen, J. (2012). An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Journal of Production Research, 50(4), 1063-1078.
Li, J., Pan, Q. & Gao, K. (2011). Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling pProblems. International Journal of Advanced Manufacturing Technology, 55(9), 1159-1169.
Lin, Y., Pfund, M. & Fowler, J. (2011). Heuristics for minimizing regular performance measures in unrelated parallel machine scheduling problems. Computers & Operations Research, 38(6), 901-916.
Mesghouni, K., Hammadi, S. & Borne, P. (1997). Evolution programs for job-shop scheduling. Proc. IEEE International Conference on Systems, Man, and Cybernetics, 1, 720-725.
Nazarathy, Y. & Weiss, G. (2010). A fluid approach to large volume job shop scheduling. Journal of Scheduling, 13(5), 509-529.
Nidhiry, N. M. & Saravanan, R. (2012). Evaluation of genetic algorithm approach for scheduling optimization of flexible manufacturing systems. International Journal of Engineering Research and Applications, 2(4), 437-446.
Nowicki, E. & Smutnicki, C. (2005). An advanced tabu search algorithm for the job shop problem. Journal of Scheduling, 8(2), 145-159.
Papadimitriou, C. H. (1994). Computational Complexity. USA, Addison Wesley.
Pezzella, F., Morganti, G. & Ciaschetti, G. (2008). A genetic algorithm for the flexible job-shop scheduling problem. Journal Computers and Operations Research, 35(10), 3202-3212.
Tay, J. C. & Wibowo, D. (2004). An effective chromosome representation for evolving flexible job-shop schedules. Proc. GECCO 2004, LNCS 3103, 210-221.
Tsai, C. F. & Lin, F. C. (2003). A new hybrid heuristic technique for solving job-shop scheduling problems. Proc. Second IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications.
Ullman, J. D. (1975). NP-complete scheduling problems. Journal of Computer System Sciences, 10, 384-393.
Van Laarhoven, P. J. M., Aarts, E. H. L. & Lenstra, J. K. (1992). Job-shop scheduling by simulated annealing. Operations Research, 40(1), 113-125.
Wu, C. G., Xing, X. L., Lee, H. P, Zhou, C. G. & Liang, Y. C. (2004). Genetic algorithm application on the job-shop scheduling problem. Proc. 2004 International Conference Machine Learning and Cybernetics, 4, 2102-2106.
Xing, L. N., Chen, Y. W. & Yang, K. W. (2011). Multi-population interactive coevolutionary algorithm for flexible job-shop scheduling problems. Computational Optimization and Applications, 48, 139-155.
Xiong, J., Tan, X., Yang, K., Xing, L. & Chen, Y. (2012). A hybrid multiobjective evolutionary approach for flexible job shop scheduling problems. Mathematical Problems in Engineering, 1, 1-27.
Yang, S., Wang, D., Chai, T. & Kendall, G. (2010). An improved constraint satisfaction adaptive neural network for job-shop scheduling. Journal of Scheduling, 13(1), 17-38.
Yazdani, M., Zandieh, M. & Amiri, M. (2010). Flexible job-shop scheduling with parallel variable neighborhood search algorithm. Expert Systems with Applications: An International Journal, 37(1), 678-687.
Yuan, Y. & Xu, H. (2015). Multiobjective flexible job shop scheduling using memetic algorithms. IEEE Transactions on Automation Science and Engineering, 12 (1), 336-353.
Zhang, G. & Gen, M. (2005). Multistaged-based genetic algorithm for flexible job-shop scheduling problem. Complexity International, 11, 223-232.
Zitzler, E. & Künzli, S. (2004). Indicator-based selection in multiobjective search. Proc. Conference on Parallel Problem Solving from Nature (PPSN VIII), LNCS 3242, 832-842.
Zitzler, E., Laumanns, M. & Thiele, L. (2002). SPEAII: Improving the strength pareto evolutionary algorithm for multi-objective optimization. Evolutionary Methods for Design, Optimisations and Control, 19-26.