How to cite this paper
Frutos, M & Tohmé, F. (2015). Choice of a PISA selector in a hybrid algorithmic structure for the FJSSP.Decision Science Letters , 4(2), 247-260.
Refrences
Adams, J., Balas, E. & Zawack, D. (1998). The shifting bottleneck procedure for Job Shop Scheduling. Management Science, 34 (3), 391-401.
Al-Hinai, N. & ElMekkawy, T. Y. (2011) Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm. International Journal of Production Economics, 132 (2), 279-291.
Armentano, V. A. & Scrich, C. R. (2000). Tabu Search for minimizing total tardiness in a Job-Shop. International Journal Production Economics, 63, 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. In: Proceedings of Evolutionary Multi-Criterion Optimization, Springer-Verlag, Berlin, 494-508.
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.
Chiang, T. C. & Lin, H. J. (2013). A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. International Journal of Production Economics, 141 (1), 87-98.
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, Springer-Verlag, NY.
Cortés Rivera, D., Coello Coello, C. A. & Cortés, N. C. (2003). Use of an artificial immune system for Job-Shop Scheduling. Lecture Notes in Computer Science, 2787, 1-10.
De Giovanni, L. & Pezzella, F. (2010). An improved genetic algorithm for the distributed and flexible Jobshop Scheduling problem. European Journal of Operational Research, 200 (2), 395-408.
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 metaheuristic approach for the two-machine total completion time flow-shop problem. Annals of Operations Research, 213, 67-78.
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., Méndez, M., Tohmé, F. & Broz, D. (2013). Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints. The Scientific World Journal: Bioinspired Computation and Its Applications in Operation Management, 2013, 1-9.
Gao, J., Sun, L. & Gen, M. (2008). A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers and Operations Research, 35, 2892-2907.
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.
Kacem, I., Hammadi, S. & Borne, P. (2002). Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man and Cybernetics, Part C, 32, 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.
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.
Liu, C. H., Chen, L. S. & Lin, P. S. (2013). Lot streaming multiple jobs with values exponentially deteriorating over time in a job-shop environment. International Journal of Production Research, 51 (1), 202-214.
M?nch, L. & Zimmermann, J. (2011). A computational study of a shifting bottleneck heuristic for multi-product complex job shops. Production Planning & Control, 22 (1), 25-40.
Panwalker, S. & Iskander, W. (1977). A survey of scheduling rules. Operations Research, 25 (1), 45-61.
Papadimitriou, C. H. (1994). Computational Complexity. Addison Wesley, NY.
Park, B. J., Choi, H. R. & Kim, H. S. A. (2003). Hybrid genetic algorithm for the Job-Shop Scheduling problems. Computers and Industrial Engineering, 45 (1), 597-613.
Rabiee, M., Zandieh, M. & Ramezani, P. (2012). Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches. International Journal of Production Research, 50 (24), 7327-7342.
Shin, J. G., Kwon, O. H. & Ryu, C. (2008). Heuristic and metaheuristic spatial planning of assembly blocks with process schedules in an assembly shop using differential evolution. Production Planning & Control, 19 (6), 605-615.
Srinivas, N. (1994). Multi-objetive optimization using nondominated sorting in genetic algorithms. Master’s Thesis. Indian Institute of Technology, Kuanpur, India.
Storer, R. H., Wu, S. D. & Vaccari, R. (1992). New search spaces for sequencing instances with application to Job-Shop Scheduling. Management Science, 38, 1495-1509.
T`kindt, V. & Billaut, J. C. (2006). Multicriteria Scheduling. Theory, Models and Algorithms, Springer-Verlag, Berlin.
Tsai, C. F. & Lin, F. C. (2003). A new hybrid heuristic technique for solving Job-Shop Scheduling problems. In: Proceedings of the Second IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IEEE, Kharkov, Ukraine, 53-58.
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.
Varadharajan, T. K. & Rajendran, C. (2005). A multi-objective simulated-annealing algorithm for scheduling in flowshops to minimize the makespan and total flowtime of jobs. European Journal of Operational Research, 167, 772-795.
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. In: Proceedings of the 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.
Zitzler, E., Laumanns, M. & Thiele, L. (2002). SPEAII: improving the strength Pareto evolutionary algorithm for multiobjective optimization. In Giannakoglou, Tsahalis, Periaux, Papailiou, and Fogarty (eds), Evolutionary Methods for Design, Optimisation and Control, CIMNE, Barcelona, Spain, 19-26.
Zitzler, E. & Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions Evolutionary Computation, 3 (3), 257-271.
Al-Hinai, N. & ElMekkawy, T. Y. (2011) Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm. International Journal of Production Economics, 132 (2), 279-291.
Armentano, V. A. & Scrich, C. R. (2000). Tabu Search for minimizing total tardiness in a Job-Shop. International Journal Production Economics, 63, 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. In: Proceedings of Evolutionary Multi-Criterion Optimization, Springer-Verlag, Berlin, 494-508.
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.
Chiang, T. C. & Lin, H. J. (2013). A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. International Journal of Production Economics, 141 (1), 87-98.
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, Springer-Verlag, NY.
Cortés Rivera, D., Coello Coello, C. A. & Cortés, N. C. (2003). Use of an artificial immune system for Job-Shop Scheduling. Lecture Notes in Computer Science, 2787, 1-10.
De Giovanni, L. & Pezzella, F. (2010). An improved genetic algorithm for the distributed and flexible Jobshop Scheduling problem. European Journal of Operational Research, 200 (2), 395-408.
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 metaheuristic approach for the two-machine total completion time flow-shop problem. Annals of Operations Research, 213, 67-78.
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., Méndez, M., Tohmé, F. & Broz, D. (2013). Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints. The Scientific World Journal: Bioinspired Computation and Its Applications in Operation Management, 2013, 1-9.
Gao, J., Sun, L. & Gen, M. (2008). A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers and Operations Research, 35, 2892-2907.
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.
Kacem, I., Hammadi, S. & Borne, P. (2002). Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man and Cybernetics, Part C, 32, 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.
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.
Liu, C. H., Chen, L. S. & Lin, P. S. (2013). Lot streaming multiple jobs with values exponentially deteriorating over time in a job-shop environment. International Journal of Production Research, 51 (1), 202-214.
M?nch, L. & Zimmermann, J. (2011). A computational study of a shifting bottleneck heuristic for multi-product complex job shops. Production Planning & Control, 22 (1), 25-40.
Panwalker, S. & Iskander, W. (1977). A survey of scheduling rules. Operations Research, 25 (1), 45-61.
Papadimitriou, C. H. (1994). Computational Complexity. Addison Wesley, NY.
Park, B. J., Choi, H. R. & Kim, H. S. A. (2003). Hybrid genetic algorithm for the Job-Shop Scheduling problems. Computers and Industrial Engineering, 45 (1), 597-613.
Rabiee, M., Zandieh, M. & Ramezani, P. (2012). Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches. International Journal of Production Research, 50 (24), 7327-7342.
Shin, J. G., Kwon, O. H. & Ryu, C. (2008). Heuristic and metaheuristic spatial planning of assembly blocks with process schedules in an assembly shop using differential evolution. Production Planning & Control, 19 (6), 605-615.
Srinivas, N. (1994). Multi-objetive optimization using nondominated sorting in genetic algorithms. Master’s Thesis. Indian Institute of Technology, Kuanpur, India.
Storer, R. H., Wu, S. D. & Vaccari, R. (1992). New search spaces for sequencing instances with application to Job-Shop Scheduling. Management Science, 38, 1495-1509.
T`kindt, V. & Billaut, J. C. (2006). Multicriteria Scheduling. Theory, Models and Algorithms, Springer-Verlag, Berlin.
Tsai, C. F. & Lin, F. C. (2003). A new hybrid heuristic technique for solving Job-Shop Scheduling problems. In: Proceedings of the Second IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IEEE, Kharkov, Ukraine, 53-58.
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.
Varadharajan, T. K. & Rajendran, C. (2005). A multi-objective simulated-annealing algorithm for scheduling in flowshops to minimize the makespan and total flowtime of jobs. European Journal of Operational Research, 167, 772-795.
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. In: Proceedings of the 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.
Zitzler, E., Laumanns, M. & Thiele, L. (2002). SPEAII: improving the strength Pareto evolutionary algorithm for multiobjective optimization. In Giannakoglou, Tsahalis, Periaux, Papailiou, and Fogarty (eds), Evolutionary Methods for Design, Optimisation and Control, CIMNE, Barcelona, Spain, 19-26.
Zitzler, E. & Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions Evolutionary Computation, 3 (3), 257-271.