How to cite this paper
Pur, B., Noori, S & Atashgah, R. (2013). A novel modeling approach for job shop scheduling problem under uncertainty.Management Science Letters , 3(11), 2725-2736.
Refrences
Alves, M. J. & Almeida, M. (2007). MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem. Computers and Operations Research, 34, 3458 – 3470.
Anscombe, F. & Aumann, R. J. (1963). A definition of subjective probability. Annals of Mathematical Statistics, 34, 199–205.
Baker, K. R. & Trietsch , D. (2009). Principles of Sequencing and Scheduling. John Wiley & Sons, Hoboken, New Jersey.
Birge, J.R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York.
Brucker, P. (2007). Scheduling Algorithms. Springer, Berlin.
Charnes, A. & Cooper, W.W. (1959). Chance-constrained programming. Management Science, 6, 73–79.
Cheng, R., Gen, M., & Tsujimura, Y. (1996). A tutorial survey of job-shop scheduling problems using genetic algorithms. Computers & Industrial Engineering, 30(4), 983–997.
Feller, W. (1950). An Introduction to Probability Theory and Its Applications. Vol. 1. John Wiley & Sons, New York.
Friedman, M., & Savage, L. J. (1948). The utility analysis of choices involving risk. Journal of Political Economy, 56, 279–304.
Ghorbani, S. & Rabbani, M. (2009). A new multi-objective algorithm for a project selection problem. Advances in Engineering Software, 40, 9–14.
Gintis, H. (2009). Game Theory Evolving, 2nd Ed. Princeton University Press, Princeton.
Gonçalvesa, J. F., Mendes, J. J. M., & Resende, M. G. C. (2005). A hybrid genetic algorithm for the job shop scheduling problem. European Journal of Operational Research, 167 (1), 77–95.
Gourgand, M., Grangeon, N., & Norre, S. (2003). A contribution to the stochastic flow shop scheduling problem. European Journal of Operational Research, 151(2), 415 –33.
Gu, M. & Lu, X. (2009). Preemptive stochastic online scheduling on two uniform machines. Information Processing Letters, 109, 369–75.
Gua, J., Gub, M., Caoa, C., & Gu, X. (2010). A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem. Computers & Operations Research, 37, 927 - 937.
Huang, R. (2010). Multi-objective job-shop scheduling with lot-splitting production. International Journal of Production Economics, 124, 206–213.
Lei, D. (2008). Pareto archive particle swarm optimization for multiobjective fuzzy job shop scheduling problems. International Journal of Advanced Manufacturing Technology, 37, 157–165.
Lenstra, J. K., Rinnooy Kan, A. H. G., & Brucker, P. (1977). Complexity of machine scheduling problems. Annals of Discrete Mathematics, 1, 343–362.
Lin, F. (2008). Solving the knapsack problem with imprecise weight coefficients using genetic algorithms. European Journal of Operational Research, 185, 133–145.
Li, Y.F., Xie, M., & Goh, T.N. (2009). A study of project selection and feature weighting for analogy based software cost estimation. Journal of Systems and Software, 82, 241–252.
Liu, B. (2007). Theory and Practice of Uncertain Programming. 2nd ed. Tsinghua University, Beijing.
Lopez, P. & Roubellat, F. (2008). Production Scheduling. John Wiley & Sons, Hoboken, New Jersey.
Neumann, V., Morgenstern, J. & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press, Princeton.
Pezzellaa, F., Morgantia, G. & Ciaschetti, G. (2008). A genetic algorithm for the flexible job-shop scheduling problem. Computers & Operations Research, 35(10), 3202–3212.
Qian, B., Wang, L., Huang, D., & Wang, X. (2008). Scheduling multi-objective job shops using a memetic algorithm based on differential evolution. International Journal Advanced Manufacturing Technology, 35, 1014–1027.
Qing-dao-er-ji, R. & Wang, Y. (2012). A new hybrid genetic algorithm for job shop scheduling problem. Computers & Operations Research, 39(10), 2291–2299.
Savage, J. (1954). The Foundations of Statistics. John Wiley & Sons, New York.
Xhafa, F., & Abraham, A. (2008). Metaheuristics for Scheduling in Industrial and Manufacturing Applications. Springer, Berlin.
Yusof, R., Khalid, M., Teck Hui, G., Yusof, S. & Othman, M. (2011). Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm. Applied Soft Computing, 11(8), 5782–5792.
Anscombe, F. & Aumann, R. J. (1963). A definition of subjective probability. Annals of Mathematical Statistics, 34, 199–205.
Baker, K. R. & Trietsch , D. (2009). Principles of Sequencing and Scheduling. John Wiley & Sons, Hoboken, New Jersey.
Birge, J.R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York.
Brucker, P. (2007). Scheduling Algorithms. Springer, Berlin.
Charnes, A. & Cooper, W.W. (1959). Chance-constrained programming. Management Science, 6, 73–79.
Cheng, R., Gen, M., & Tsujimura, Y. (1996). A tutorial survey of job-shop scheduling problems using genetic algorithms. Computers & Industrial Engineering, 30(4), 983–997.
Feller, W. (1950). An Introduction to Probability Theory and Its Applications. Vol. 1. John Wiley & Sons, New York.
Friedman, M., & Savage, L. J. (1948). The utility analysis of choices involving risk. Journal of Political Economy, 56, 279–304.
Ghorbani, S. & Rabbani, M. (2009). A new multi-objective algorithm for a project selection problem. Advances in Engineering Software, 40, 9–14.
Gintis, H. (2009). Game Theory Evolving, 2nd Ed. Princeton University Press, Princeton.
Gonçalvesa, J. F., Mendes, J. J. M., & Resende, M. G. C. (2005). A hybrid genetic algorithm for the job shop scheduling problem. European Journal of Operational Research, 167 (1), 77–95.
Gourgand, M., Grangeon, N., & Norre, S. (2003). A contribution to the stochastic flow shop scheduling problem. European Journal of Operational Research, 151(2), 415 –33.
Gu, M. & Lu, X. (2009). Preemptive stochastic online scheduling on two uniform machines. Information Processing Letters, 109, 369–75.
Gua, J., Gub, M., Caoa, C., & Gu, X. (2010). A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem. Computers & Operations Research, 37, 927 - 937.
Huang, R. (2010). Multi-objective job-shop scheduling with lot-splitting production. International Journal of Production Economics, 124, 206–213.
Lei, D. (2008). Pareto archive particle swarm optimization for multiobjective fuzzy job shop scheduling problems. International Journal of Advanced Manufacturing Technology, 37, 157–165.
Lenstra, J. K., Rinnooy Kan, A. H. G., & Brucker, P. (1977). Complexity of machine scheduling problems. Annals of Discrete Mathematics, 1, 343–362.
Lin, F. (2008). Solving the knapsack problem with imprecise weight coefficients using genetic algorithms. European Journal of Operational Research, 185, 133–145.
Li, Y.F., Xie, M., & Goh, T.N. (2009). A study of project selection and feature weighting for analogy based software cost estimation. Journal of Systems and Software, 82, 241–252.
Liu, B. (2007). Theory and Practice of Uncertain Programming. 2nd ed. Tsinghua University, Beijing.
Lopez, P. & Roubellat, F. (2008). Production Scheduling. John Wiley & Sons, Hoboken, New Jersey.
Neumann, V., Morgenstern, J. & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press, Princeton.
Pezzellaa, F., Morgantia, G. & Ciaschetti, G. (2008). A genetic algorithm for the flexible job-shop scheduling problem. Computers & Operations Research, 35(10), 3202–3212.
Qian, B., Wang, L., Huang, D., & Wang, X. (2008). Scheduling multi-objective job shops using a memetic algorithm based on differential evolution. International Journal Advanced Manufacturing Technology, 35, 1014–1027.
Qing-dao-er-ji, R. & Wang, Y. (2012). A new hybrid genetic algorithm for job shop scheduling problem. Computers & Operations Research, 39(10), 2291–2299.
Savage, J. (1954). The Foundations of Statistics. John Wiley & Sons, New York.
Xhafa, F., & Abraham, A. (2008). Metaheuristics for Scheduling in Industrial and Manufacturing Applications. Springer, Berlin.
Yusof, R., Khalid, M., Teck Hui, G., Yusof, S. & Othman, M. (2011). Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm. Applied Soft Computing, 11(8), 5782–5792.