How to cite this paper
Baba, M., Achcar, J., Moala, F., Oikawa, S & Piratelli, C. (2013). A useful empirical Bayesian method to analyse industrial data from saturated factorial designs.International Journal of Industrial Engineering Computations , 4(3), 337-344.
Refrences
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Bouquard, J.L., Billaut, J.C., Kubzin, M.A., & Strusevich, V.A. (2005). Two-machine flow-shop scheduling problems with no-wait jobs. Operations Research Letters, (33) 255–262.
Espinouse, M.L., Formanowicz, P., & Penz, B. (1999). Minimizing the makespan in the two-machine no-wait flow-shop with limited machine availability. Computers and Industrial Engineering, (37) 497–500.
Fink, A., & Vob, S. (2003). Solving the continuous flow-shop scheduling problem by meta-heuristics. European Journal of Operational Research, (151) 400–414.
Freisleben, B., & Merz, P. (1996). A genetic local search algorithm for solving symmetric and asymmetric travelling salesman problems. Proceedings of IEEE International Conference on Evolutionary Computation, 159–164.
Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering design. John Wiley & Sons, New York.
Grabowski, J., & Pempera, J. (2005). Some local search algorithms for no-wait flow-shop problem with makespan criterion. Computers and Operations Research, (32), 2197–2212.
Goldberg, D.E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Massachusetts.
Hansancebi, O., & Erbatur, F. (2000). Evaluation of crossover techniques in genetic algorithm based optimum structural design. Computer and Structures, (78), 435–448.
Holland, J. (1975). Adaptation in natural and artificial systems. University of Michigan Press: Ann Habor.
Johnson, S.M. (1954). Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly, (1) 61–68.
Kamburowski, J. (1997). The nature of simplicity of Johnson’s algorithm. Omega – International Journal of Management Science, (25), 581–584.
Kido, T., Kitano, H., & Nakanishi, M. (1993). A Hybrid Search for Genetic Algorithms: Combining Genetic Algorithms, TABU Search, and Simulated Annealing. Proceedings of the 5th International Conference on Genetic Algorithms, San Mateo, CA, 641.
Kumar, A., Prakash, A., Shankar, R., & Tiwari, M.K. (2006). Psycho-Clonal algorithm based approach to solve continuous flow-shop scheduling problem. Expert Systems with Applications, (31), 504–514.
Murata, T., Ishibuchi, H., & Tanaka, H. (1996). Multi-objective genetic algorithm and its applications to flow-shop scheduling. Computers and Industrial Engineering, (30), 957–968.
Murata, T., Ishibuchi, H., & Tanaka, H. (1996). Genetic algorithms for flow-shop scheduling problems. Computer & Industrial Engineering, (30), 1061-1071.
Ogbu F.A., & Smith D.K. (1990). The application of the simulated annealing algorithms to the solution of the n / m / Cmax flow-shop problem. Computers & Operations Research, (17), 243–253.
Osman, I.H., & Potts, C.N. (1989). Simulated annealing for permutation flow-shop scheduling. OMEGA, International Journal of Management Science, (17), 551–557.
Oulamara, A. (2007). Makespan minimization in a no-wait flow-shop problem with two batching machines. Computers and Operations Research, (34), 1033–1050.
Ponnambalam, S.G., Jagannathan, H., Kataria, M., & Gadicherla, A. (2004). A TSP-GA multi-objective algorithm for flow-shop scheduling. International Journal of Advanced Manufacturing Technology, (23), 909–915.
Pongcharoen, P., Hicks, C., Braiden, P.M., & Stewardson, D.J. (2002). Determining optimum genetic algorithm parameters for scheduling the manufacturing and assembly of complex products. International Journal of Production Economics, (78), 311–322.
Rahimi-Vahed, A.R., & Mirghorbani, S.M. (2007). A multi-objective particle swarm for a flow-shop scheduling problem. Journal of Combinatorial Optimization, (13), 79–102.
Ravindran, D., Noorul Haq, A., Selvakuar, S.J., & Sivaraman, R. (2005). Flow-shop scheduling with multiple objective of minimizing makespan and total flow time. International Journal of Advance Manufacturing Technology, (25), 1007–1012.
Roach, A., & Nagi, R. (1996). A hybrid GA-SA algorithm for just-in-time scheduling of multi-level assemblies. Computers & Industrial Engineering, (30), 1047–1060.
Spieksma, F.C.R., & Woeginger, G.J. (2005). The no-wait flow-shop paradox. Operation Research Letter, (33), 603–608.
Su, L.-H., & Lee, Y.-Y. (2008). The two-machine flow-shop no-wait scheduling problem with a single server to minimize the total completion time. Computers and Operations Research, (35), 2952-2963.
Tavakkoli-Moghaddam, R., Rahimi-Vahed, A.R., & Mirzaei, A.H. (2007). Solving a bi-criteria permutation flow-shop problem using immune algorithm. Proceedings of the First IEEE Symposium on Computational Intelligence, vol. 1, Honolulu, Hawaii, (April 2007), pp. 4
Thornton, H.W., & Hunsucker, J.L. (2004). A new heuristic for minimal makespan in flow-shops with multiple processors and no intermediate storage. European Journal of Operational Research, (152), 96–114.
Toktas, B., Azizoglu, M., & Koksalan, S.K. (2004). Two-machine flow-shop scheduling with two criteria: Maximum earliness and makespan. European Journal of Operational Research, (157), 286–295.
Wang, J.B. (2007). Flow-shop scheduling problems with decreasing linear deterioration under dominant machines. Computers and Operations Research, (34), 2043–2058.
Wang, L. (2005). A hybrid genetic algorithm-neural network strategy for simulation optimization. Applied Mathematics and Computation, (170), 1329-1343.
Yamada, T., & Reeves, C.R. (1998). Solving the sum permutation flow-shop scheduling problem by genetic local search. Proceedings of IEEE International Conference on Evolutionary Computation, 230- 234.
Bouquard, J.L., Billaut, J.C., Kubzin, M.A., & Strusevich, V.A. (2005). Two-machine flow-shop scheduling problems with no-wait jobs. Operations Research Letters, (33) 255–262.
Espinouse, M.L., Formanowicz, P., & Penz, B. (1999). Minimizing the makespan in the two-machine no-wait flow-shop with limited machine availability. Computers and Industrial Engineering, (37) 497–500.
Fink, A., & Vob, S. (2003). Solving the continuous flow-shop scheduling problem by meta-heuristics. European Journal of Operational Research, (151) 400–414.
Freisleben, B., & Merz, P. (1996). A genetic local search algorithm for solving symmetric and asymmetric travelling salesman problems. Proceedings of IEEE International Conference on Evolutionary Computation, 159–164.
Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering design. John Wiley & Sons, New York.
Grabowski, J., & Pempera, J. (2005). Some local search algorithms for no-wait flow-shop problem with makespan criterion. Computers and Operations Research, (32), 2197–2212.
Goldberg, D.E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Massachusetts.
Hansancebi, O., & Erbatur, F. (2000). Evaluation of crossover techniques in genetic algorithm based optimum structural design. Computer and Structures, (78), 435–448.
Holland, J. (1975). Adaptation in natural and artificial systems. University of Michigan Press: Ann Habor.
Johnson, S.M. (1954). Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly, (1) 61–68.
Kamburowski, J. (1997). The nature of simplicity of Johnson’s algorithm. Omega – International Journal of Management Science, (25), 581–584.
Kido, T., Kitano, H., & Nakanishi, M. (1993). A Hybrid Search for Genetic Algorithms: Combining Genetic Algorithms, TABU Search, and Simulated Annealing. Proceedings of the 5th International Conference on Genetic Algorithms, San Mateo, CA, 641.
Kumar, A., Prakash, A., Shankar, R., & Tiwari, M.K. (2006). Psycho-Clonal algorithm based approach to solve continuous flow-shop scheduling problem. Expert Systems with Applications, (31), 504–514.
Murata, T., Ishibuchi, H., & Tanaka, H. (1996). Multi-objective genetic algorithm and its applications to flow-shop scheduling. Computers and Industrial Engineering, (30), 957–968.
Murata, T., Ishibuchi, H., & Tanaka, H. (1996). Genetic algorithms for flow-shop scheduling problems. Computer & Industrial Engineering, (30), 1061-1071.
Ogbu F.A., & Smith D.K. (1990). The application of the simulated annealing algorithms to the solution of the n / m / Cmax flow-shop problem. Computers & Operations Research, (17), 243–253.
Osman, I.H., & Potts, C.N. (1989). Simulated annealing for permutation flow-shop scheduling. OMEGA, International Journal of Management Science, (17), 551–557.
Oulamara, A. (2007). Makespan minimization in a no-wait flow-shop problem with two batching machines. Computers and Operations Research, (34), 1033–1050.
Ponnambalam, S.G., Jagannathan, H., Kataria, M., & Gadicherla, A. (2004). A TSP-GA multi-objective algorithm for flow-shop scheduling. International Journal of Advanced Manufacturing Technology, (23), 909–915.
Pongcharoen, P., Hicks, C., Braiden, P.M., & Stewardson, D.J. (2002). Determining optimum genetic algorithm parameters for scheduling the manufacturing and assembly of complex products. International Journal of Production Economics, (78), 311–322.
Rahimi-Vahed, A.R., & Mirghorbani, S.M. (2007). A multi-objective particle swarm for a flow-shop scheduling problem. Journal of Combinatorial Optimization, (13), 79–102.
Ravindran, D., Noorul Haq, A., Selvakuar, S.J., & Sivaraman, R. (2005). Flow-shop scheduling with multiple objective of minimizing makespan and total flow time. International Journal of Advance Manufacturing Technology, (25), 1007–1012.
Roach, A., & Nagi, R. (1996). A hybrid GA-SA algorithm for just-in-time scheduling of multi-level assemblies. Computers & Industrial Engineering, (30), 1047–1060.
Spieksma, F.C.R., & Woeginger, G.J. (2005). The no-wait flow-shop paradox. Operation Research Letter, (33), 603–608.
Su, L.-H., & Lee, Y.-Y. (2008). The two-machine flow-shop no-wait scheduling problem with a single server to minimize the total completion time. Computers and Operations Research, (35), 2952-2963.
Tavakkoli-Moghaddam, R., Rahimi-Vahed, A.R., & Mirzaei, A.H. (2007). Solving a bi-criteria permutation flow-shop problem using immune algorithm. Proceedings of the First IEEE Symposium on Computational Intelligence, vol. 1, Honolulu, Hawaii, (April 2007), pp. 4
Thornton, H.W., & Hunsucker, J.L. (2004). A new heuristic for minimal makespan in flow-shops with multiple processors and no intermediate storage. European Journal of Operational Research, (152), 96–114.
Toktas, B., Azizoglu, M., & Koksalan, S.K. (2004). Two-machine flow-shop scheduling with two criteria: Maximum earliness and makespan. European Journal of Operational Research, (157), 286–295.
Wang, J.B. (2007). Flow-shop scheduling problems with decreasing linear deterioration under dominant machines. Computers and Operations Research, (34), 2043–2058.
Wang, L. (2005). A hybrid genetic algorithm-neural network strategy for simulation optimization. Applied Mathematics and Computation, (170), 1329-1343.
Yamada, T., & Reeves, C.R. (1998). Solving the sum permutation flow-shop scheduling problem by genetic local search. Proceedings of IEEE International Conference on Evolutionary Computation, 230- 234.