How to cite this paper
Duarte, M., Cepeda, J., González-Neira, E., Barrera, D., Cortés, V & Rey, G. (2017). Performance evaluation of a GRASP-based approach for stochastic scheduling problems.Uncertain Supply Chain Management, 5(4), 359-368.
Refrences
Alkhamis, T. M., Ahmed, M. A., & Tuan, V. K. (1999). Simulated annealing for discrete optimization with estimation. European Journal of Operational Research, 116(3), 530–544. https://doi.org/10.1016/S0377-2217(98)00112-X
Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2008). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), 239–287. https://doi.org/10.1007/s11047-008-9098-4
Chen, E. J. (2012). Some insights of using common random numbers in selection procedures. Discrete Event Dynamic Systems, 23(3), 241–259. https://doi.org/10.1007/s10626-012-0142-2
Crauwels, H. a. J., Potts, C. N., & Van Wassenhove, L. N. (1998). Local search heuristics for the single machine total weighted tardiness scheduling problem. INFORMS Journal on Computing, 10(3), 341–350. https://doi.org/10.1287/ijoc.10.3.341
Dehghanimohammadabadi, M., & Keyser, T. K. (2015). Tradeoffs between objective measures and execution speed in Iterative Optimization-based Simulation (IOS). In 2015 Winter Simulation Conference (WSC) (pp. 2848–2859). https://doi.org/10.1109/WSC.2015.7408389
Estupiñán, A., Torres, J., Pérez, N., González-Neira, E. M., Barrera, D., Barbosa, J., … Suárez, D. (2015). Mejora en Ocupación, Oportunidad, y Variabilidad en la Programación de un Servicio de Cirugía. In J. Torres & J. G. Villegas (Eds.), I Congreso de la Asociaición Nacional de Investigación Operativa. Bogotá.
Ferreira, J. S. (2012). Multimethodology in metaheuristics. Journal of the Operational Research Society, 64(6), 873–883. https://doi.org/10.1057/jors.2012.88
Figueira, G., & Almada-Lobo, B. (2014). Hybrid simulation-optimization methods: A taxonomy and discussion. Simulation Modelling Practice and Theory, 46, 118–134. https://doi.org/10.1016/j.simpat.2014.03.007
Figueira, G., Furlan, M., & Almada-Lobo, B. (2013). Predictive production planning in an integrated pulp and paper mill. IFAC Proceedings Volumes, 46(9), 371–376. https://doi.org/10.3182/20130619-3-RU-3018.00409
João, J. P., Arroyo, J. E. C., Villadiego, H. M. M., & Gonçalves, L. B. (2014). Hybrid GRASP heuristics to solve an unrelated parallel machine scheduling problem with earliness and tardiness penalties. Electronic Notes in Theoretical Computer Science, 302, 53–72. https://doi.org/10.1016/j.entcs.2014.01.020
Juan, A. A., Barrios, B. B., Vallada, E., Riera, D., & Jorba, J. (2014). A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times. Simulation Modelling Practice and Theory, 46, 101–117. https://doi.org/10.1016/j.simpat.2014.02.005
Juan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72. https://doi.org/10.1016/j.orp.2015.03.001
Kanet, J. J., & Li, X. (2004). A weighted modified due date rule for sequencing to minimize weighted tardiness. Journal of Scheduling, 7(4), 261–276.
Kayvanfar, V., Mahdavi, I., & Komaki, G. M. (2013). Single machine scheduling with controllable processing times to minimize total tardiness and earliness. Computers & Industrial Engineering, 65(1), 166–175. https://doi.org/10.1016/j.cie.2011.08.019
Li, S., Jia, Y., & Wang, J. (2012). A discrete-event simulation approach with multiple-comparison procedure for stochastic resource-constrained project scheduling. The International Journal of Advanced Manufacturing Technology, 63(1–4), 65–76. https://doi.org/10.1007/s00170-011-3885-2
Lu, C.-C., Lin, S.-W., & Ying, K.-C. (2014). Minimizing worst-case regret of makespan on a single machine with uncertain processing and setup times. Applied Soft Computing, 23, 144–151.
Lu, C.-C., Ying, K.-C., & Lin, S.-W. (2014). Robust single machine scheduling for minimizing total flow time in the presence of uncertain processing times. Computers & Industrial Engineering, 74, 102–110. https://doi.org/10.1016/j.cie.2014.04.013
Molina-Sánchez, L. P., & González-Neira, E. M. (2016). GRASP to minimize total weighted tardiness in a permutation flow shop environment. International Journal of Industrial Engineering Computations, 7(1), 161–176. https://doi.org/10.5267/j.ijiec.2015.6.004
Nakayama, M. K. (2007). Fixed-width multiple-comparison procedures using common random numbers for steady-state simulations. European Journal of Operational Research, 182(3), 1330–1349. https://doi.org/10.1016/j.ejor.2006.09.045
Niño, M. A., & Caballero, J. P. (2009). Evaluación de funciones de utilidad de GRASP en la progra m ación de producción paraminimizar la tardanz a total ponderada en una máq uina. Ingeniería, 14(2), 51–58.
Osman, I. H., Belouadah, H., Fleszar, K., & Saffar, M. (2009). Hybrid of the weighted minimum slack and shortest processing time dispatching rules for the total weighted tardiness single machine scheduling problem with availability constraints, (August), 10–12.
Rajkumar, M., Asokan, P., Anilkumar, N., & Page, T. (2011). A GRASP algorithm for flexible job-shop scheduling problem with limited resource constraints. International Journal of Production Research, 49(8), 2409–2423. https://doi.org/10.1080/00207541003709544
Rajkumar, M., Asokan, P., & Vamsikrishna, V. (2010). A GRASP algorithm for flexible job-shop scheduling with maintenance constraints. International Journal of Production Research, 48(22), 6821–6836. https://doi.org/10.1080/00207540903308969
Ribas, I., & Companys, R. (2015). Efficient heuristic algorithms for the blocking flow shop scheduling problem with total flow time minimization. Computers & Industrial Engineering, 87, 30–39.
Salmasnia, A., Khatami, M., Kazemzadeh, R. B., & Zegordi, S. H. (2014). Bi-objective single machine scheduling problem with stochastic processing times. Top. https://doi.org/10.1007/s11750-014-0337-9
Sarin, S. C., Sherali, H. D., & Liao, L. (2013). Minimizing conditional-value-at-risk for stochastic scheduling problems. Journal of Scheduling, 17(1), 5–15. https://doi.org/10.1007/s10951-013-0349-6
Smith, W. E. (1956). Various optimizers for single-stage production. Naval Research Logistics Quarterly, 3(1–2), 59–66. https://doi.org/10.1002/nav.3800030106
Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2008). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), 239–287. https://doi.org/10.1007/s11047-008-9098-4
Chen, E. J. (2012). Some insights of using common random numbers in selection procedures. Discrete Event Dynamic Systems, 23(3), 241–259. https://doi.org/10.1007/s10626-012-0142-2
Crauwels, H. a. J., Potts, C. N., & Van Wassenhove, L. N. (1998). Local search heuristics for the single machine total weighted tardiness scheduling problem. INFORMS Journal on Computing, 10(3), 341–350. https://doi.org/10.1287/ijoc.10.3.341
Dehghanimohammadabadi, M., & Keyser, T. K. (2015). Tradeoffs between objective measures and execution speed in Iterative Optimization-based Simulation (IOS). In 2015 Winter Simulation Conference (WSC) (pp. 2848–2859). https://doi.org/10.1109/WSC.2015.7408389
Estupiñán, A., Torres, J., Pérez, N., González-Neira, E. M., Barrera, D., Barbosa, J., … Suárez, D. (2015). Mejora en Ocupación, Oportunidad, y Variabilidad en la Programación de un Servicio de Cirugía. In J. Torres & J. G. Villegas (Eds.), I Congreso de la Asociaición Nacional de Investigación Operativa. Bogotá.
Ferreira, J. S. (2012). Multimethodology in metaheuristics. Journal of the Operational Research Society, 64(6), 873–883. https://doi.org/10.1057/jors.2012.88
Figueira, G., & Almada-Lobo, B. (2014). Hybrid simulation-optimization methods: A taxonomy and discussion. Simulation Modelling Practice and Theory, 46, 118–134. https://doi.org/10.1016/j.simpat.2014.03.007
Figueira, G., Furlan, M., & Almada-Lobo, B. (2013). Predictive production planning in an integrated pulp and paper mill. IFAC Proceedings Volumes, 46(9), 371–376. https://doi.org/10.3182/20130619-3-RU-3018.00409
João, J. P., Arroyo, J. E. C., Villadiego, H. M. M., & Gonçalves, L. B. (2014). Hybrid GRASP heuristics to solve an unrelated parallel machine scheduling problem with earliness and tardiness penalties. Electronic Notes in Theoretical Computer Science, 302, 53–72. https://doi.org/10.1016/j.entcs.2014.01.020
Juan, A. A., Barrios, B. B., Vallada, E., Riera, D., & Jorba, J. (2014). A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times. Simulation Modelling Practice and Theory, 46, 101–117. https://doi.org/10.1016/j.simpat.2014.02.005
Juan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72. https://doi.org/10.1016/j.orp.2015.03.001
Kanet, J. J., & Li, X. (2004). A weighted modified due date rule for sequencing to minimize weighted tardiness. Journal of Scheduling, 7(4), 261–276.
Kayvanfar, V., Mahdavi, I., & Komaki, G. M. (2013). Single machine scheduling with controllable processing times to minimize total tardiness and earliness. Computers & Industrial Engineering, 65(1), 166–175. https://doi.org/10.1016/j.cie.2011.08.019
Li, S., Jia, Y., & Wang, J. (2012). A discrete-event simulation approach with multiple-comparison procedure for stochastic resource-constrained project scheduling. The International Journal of Advanced Manufacturing Technology, 63(1–4), 65–76. https://doi.org/10.1007/s00170-011-3885-2
Lu, C.-C., Lin, S.-W., & Ying, K.-C. (2014). Minimizing worst-case regret of makespan on a single machine with uncertain processing and setup times. Applied Soft Computing, 23, 144–151.
Lu, C.-C., Ying, K.-C., & Lin, S.-W. (2014). Robust single machine scheduling for minimizing total flow time in the presence of uncertain processing times. Computers & Industrial Engineering, 74, 102–110. https://doi.org/10.1016/j.cie.2014.04.013
Molina-Sánchez, L. P., & González-Neira, E. M. (2016). GRASP to minimize total weighted tardiness in a permutation flow shop environment. International Journal of Industrial Engineering Computations, 7(1), 161–176. https://doi.org/10.5267/j.ijiec.2015.6.004
Nakayama, M. K. (2007). Fixed-width multiple-comparison procedures using common random numbers for steady-state simulations. European Journal of Operational Research, 182(3), 1330–1349. https://doi.org/10.1016/j.ejor.2006.09.045
Niño, M. A., & Caballero, J. P. (2009). Evaluación de funciones de utilidad de GRASP en la progra m ación de producción paraminimizar la tardanz a total ponderada en una máq uina. Ingeniería, 14(2), 51–58.
Osman, I. H., Belouadah, H., Fleszar, K., & Saffar, M. (2009). Hybrid of the weighted minimum slack and shortest processing time dispatching rules for the total weighted tardiness single machine scheduling problem with availability constraints, (August), 10–12.
Rajkumar, M., Asokan, P., Anilkumar, N., & Page, T. (2011). A GRASP algorithm for flexible job-shop scheduling problem with limited resource constraints. International Journal of Production Research, 49(8), 2409–2423. https://doi.org/10.1080/00207541003709544
Rajkumar, M., Asokan, P., & Vamsikrishna, V. (2010). A GRASP algorithm for flexible job-shop scheduling with maintenance constraints. International Journal of Production Research, 48(22), 6821–6836. https://doi.org/10.1080/00207540903308969
Ribas, I., & Companys, R. (2015). Efficient heuristic algorithms for the blocking flow shop scheduling problem with total flow time minimization. Computers & Industrial Engineering, 87, 30–39.
Salmasnia, A., Khatami, M., Kazemzadeh, R. B., & Zegordi, S. H. (2014). Bi-objective single machine scheduling problem with stochastic processing times. Top. https://doi.org/10.1007/s11750-014-0337-9
Sarin, S. C., Sherali, H. D., & Liao, L. (2013). Minimizing conditional-value-at-risk for stochastic scheduling problems. Journal of Scheduling, 17(1), 5–15. https://doi.org/10.1007/s10951-013-0349-6
Smith, W. E. (1956). Various optimizers for single-stage production. Naval Research Logistics Quarterly, 3(1–2), 59–66. https://doi.org/10.1002/nav.3800030106