How to cite this paper
González-Neira, E & Montoya-Torres, J. (2019). A simheuristic for bi-objective stochastic permutation flow shop scheduling problem.Journal of Project Management, 4(2), 57-80.
Refrences
Armentano, V. A., & Araujo, O. C. B. de. (2006). Grasp with memory-based mechanisms for minimizing total tardiness in single machine scheduling with setup times. Journal of Heuristics, 12(6), 427–446.
Armentano, V. A., & de França Filho, M. F. (2007). Minimizing total tardiness in parallel machine scheduling with setup times: An adaptive memory-based GRASP approach. European Journal of Operational Research, 183(1), 100–114.
Arora, D., & Agarwal, G. (2016). Meta-heuristic approaches for flowshop scheduling problems: a review. International Journal of Advanced Operations Management, 8(1), 1.
Arroyo, J. E. C., & de Souza Pereira, A. A. (2011). A GRASP heuristic for the multi-objective permutation flowshop scheduling problem. The International Journal of Advanced Manufacturing Technology, 55(5–8), 741–753.
Aytug, H., Lawley, M. a., McKay, K., Mohan, S., & Uzsoy, R. (2005). Executing production schedules in the face of uncertainties: A review and some future directions. European Journal of Operational Research, 161(1), 86–110.
Azadeh, A., Jeihoonian, M., Shoja, B. M., & Seyedmahmoudi, S. H. (2012). An integrated neural network–simulation algorithm for performance optimisation of the bi-criteria two-stage assembly flow-shop scheduling problem with stochastic activities. International Journal of Production Research, 50(24), 7271–7284.
Baker, K. R., & Altheimer, D. (2012). Heuristic solution methods for the stochastic flow shop problem. European Journal of Operational Research, 216(1), 172–177.
Behnamian, J., & Fatemi Ghomi, S. M. T. (2014). Multi-objective fuzzy multiprocessor flowshop scheduling. Applied Soft Computing, 21, 139–148.
Celano, G., Costa, A., & Fichera, S. (2003). An evolutionary algorithm for pure fuzzy flowshop scheduling problems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(06), 655–669.
Chassaing, M., Fontanel, J., Lacomme, P., Ren, L., Tchernev, N., & Villechenon, P. (2014). A GRASP×ELS approach for the job-shop with a web service paradigm packaging. Expert Systems with Applications, 41(2), 544–562.
Ciavotta, M., Minella, G., & Ruiz, R. (2013). Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study. European Journal of Operational Research, 227(2), 301–313.
Croes, G. A. (1958). A method for solving traveling-salesman Problems. Operations Research, 6(6), 791–812.
Damodaran, P., Vélez-Gallego, M. C., & Maya, J. (2011). A GRASP approach for makespan minimization on parallel batch processing machines. Journal of Intelligent Manufacturing, 22(5), 767–777.
Davoudpour, H., & Ashrafi, M. (2009). Solving multi-objective SDST flexible flow shop using GRASP algorithm. The International Journal of Advanced Manufacturing Technology, 44(7–8), 737–747.
Ebrahimi, M., Fatemi Ghomi, S. M. T. M. T., & Karimi, B. (2014). Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates. Applied Mathematical Modelling, 38(9–10), 2490–2504.
Elyasi, A., & Salmasi, N. (2013). Stochastic scheduling with minimizing the number of tardy jobs using chance constrained programming. Mathematical and Computer Modelling, 57(5–6), 1154–1164.
Fazayeli, M., Aleagha, M.-R., Bashirzadeh, R., & Shafaei, R. (2016). A hybrid meta-heuristic algorithm for flowshop robust scheduling under machine breakdown uncertainty. International Journal of Computer Integrated Manufacturing, 29(7), 709–719.
Feo, T. A., & Resende, M. G. . (1989). A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters, 8(2), 67–71.
Fernandez-Viagas, V., Ruiz, R., & Framinan, J. M. (2017). A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation. European Journal of Operational Research, 257(3), 707–721.
Festa, P., & Resende, M. G. C. (2009). An annotated bibliography of GRASP-Part II: Applications. International Transactions in Operational Research, 16(2), 131–172.
Forst, F. G. (1995). Bicriterion stochastic scheduling on one or more machines. European Journal of Operational Research, 80(2), 404–409.
Framinan, J. M., Gupta, J. N. D., & Leisten, R. (2004). A review and classification of heuristics for permutation flow-shop scheduling with makespan objective. Journal of the Operational Research Society, 55(12), 1243–1255.
Framinan, J. M., & Perez-Gonzalez, P. (2015). On heuristic solutions for the stochastic flowshop scheduling problem. European Journal of Operational Research, 246(2), 413–420.
Garey, M. R., & Johnson, D. S. (1977). Two-processor scheduling with start-times and deadlines. SIAM Journal on Computing, 6(3), 416–426.
González-Neira, E. M., García-Cáceres, R. G., Caballero-Villalobos, J. P., Molina-Sánchez, L. P., & Montoya-Torres, J. R. (2016). Stochastic flexible flow shop scheduling problem under quantitative and qualitative decision criteria. Computers & Industrial Engineering, 101, 128–144.
González-Neira, E. M., Montoya-Torres, J. R., & Barrera, D. (2017). Flow-shop scheduling problem under uncertainties: Review and trends. International Journal of Industrial Engineering Computations, 8(4), 399–426.
Gourgand, M., Grangeon, N., & Norre, S. (2000). A review of the static stochastic flow-shop scheduling problem. Journal of Decision Systems, 9(2), 1–31.
Gupta, J. N. D., & Stafford, E. F. (2006). Flowshop scheduling research after five decades. European Journal of Operational Research, 169(3), 699–711.
Han, Y., Gong, D., Jin, Y., & Pan, Q. (2017). Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns. IEEE Transactions on Cybernetics, 1–14.
Huang, C.-S., Huang, Y.-C., & Lai, P.-J. (2012). Modified genetic algorithms for solving fuzzy flow shop scheduling problems and their implementation with CUDA. Expert Systems with Applications, 39(5), 4999–5005.
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.
Karimi, N., Zandieh, M., & Karamooz, H. R. (2010). Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach. Expert Systems with Applications, 37(6), 4024–4032.
Knowles, J. D., & Corne, D. W. (2000). Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2), 149–172.
Li, Z., & Ierapetritou, M. (2008). Process scheduling under uncertainty: Review and challenges. Computers & Chemical Engineering, 32(4–5), 715–727.
Liefooghe, A., Basseur, M., Humeau, J., Jourdan, L., & Talbi, E.-G. (2012). On optimizing a bi-objective flowshop scheduling problem in an uncertain environment. Computers & Mathematics with Applications, 64(12), 3747–3762.
Lin, J. T., & Chen, C.-M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing. Simulation Modelling Practice and Theory, 51, 100–114.
Martí, R., Campos, V., Resende, M. G. C., & Duarte, A. (2015). Multiobjective GRASP with Path Relinking. European Journal of Operational Research, 240(1), 54–71.
Minella, G., Ruiz, R., & Ciavotta, M. (2008). A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem. INFORMS Journal on Computing, 20(3), 451–471.
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.
Mou, J., Li, X., Gao, L., & Yi, W. (2015). An effective L-MONG algorithm for solving multi-objective flow-shop inverse scheduling problems. Journal of Intelligent Manufacturing.
Nagano, M. S., & Miyata, H. H. (2016). Review and classification of constructive heuristics mechanisms for no-wait flow shop problem. The International Journal of Advanced Manufacturing Technology, 86(5–8), 2161–2174.
Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91–95.
Niño-Navarrete, A. M., & Caballero-Villalobos, J. P. (2009). Evaluación de funciones de utilidad de GRASP en la programación de producción para minimizar la tardanza total ponderada en una máquina. Revista Ingeniería, 14(2), 51–58.
Pan, Q.-K., & Ruiz, R. (2013). A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime. Computers & Operations Research, 40(1), 117–128.
Pinedo, M. L. (2012). Scheduling: Theory, algorithms and systems. Springer (4th ed., Vol. 4). New York: Springer Science & Business Media.
Prabhaharan, G., Khan, B. S. H., & Rakesh, L. (2006). Implementation of grasp in flow shop scheduling. The International Journal of Advanced Manufacturing Technology, 30(11–12), 1126–1131.
Qin, W., Zhang, J., & Song, D. (2015). An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time. Journal of Intelligent Manufacturing.
Rahmani, D., Ramezanian, R., & Mehrabad, M. S. (2014). Multi-objective flow shop scheduling problem with stochastic parameters: fuzzy goal programming approach. International Journal of Operational Research, 21(3), 322–340.
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.
Resende, M. C., & Ribeiro, C. (2003). Greedy Randomized Adaptive Search Procedures. In F. Glover & G. Kochenberger (Eds.), Handbook of Metaheuristics SE - 8 (Vol. 57, pp. 219–249). Springer US.
Reza Hejazi, S., & Saghafian, S. (2005). Flowshop-scheduling problems with makespan criterion: a review. International Journal of Production Research, 43(14), 2895–2929.
Rossit, D. A., Tohmé, F., & Frutos, M. (2018). The Non-Permutation Flow-Shop scheduling problem: A literature review. Omega, 77, 143–153.
Ruiz, R., & Maroto, C. (2005). A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research, 165(2), 479–494.
Sun, Y., Zhang, C., Gao, L., & Wang, X. (2011). Multi-objective optimization algorithms for flow shop scheduling problem: a review and prospects. The International Journal of Advanced Manufacturing Technology, 55(5–8), 723–739.
Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2), 278–285.
Temiz, I., & Erol, S. (2007). Multiobjective genetic algorithm for fuzzy flowshop scheduling problem (Bulanik akiş ti̇pi̇ çi̇zelgeleme problemi̇ i̇çi̇n çok amaçli geneti̇k algori̇tma). Journal of the Faculty of Engineering and Architecture of Gazi University, 22(4), 855–862. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-38549127324&partnerID=tZOtx3y1
Vallada, E., Ruiz, R., & Minella, G. (2008). Minimising total tardiness in the m-machine flowshop problem: A review and evaluation of heuristics and metaheuristics. Computers & Operations Research, 35(4), 1350–1373.
Yenisey, M. M., & Yagmahan, B. (2014). Multi-objective permutation flow shop scheduling problem: Literature review, classification and current trends. Omega (Vol. 45). Pergamon Press.
Yenisey, M. M., & Yagmahan, B. (2014). Multi-objective permutation flow shop scheduling problem: Literature review, classification and current trends. Omega, 45(C), 119–135.
Ying, K. (2015). Scheduling the two-machine flowshop to hedge against processing time uncertainty. Journal of the Operational Research Society, 66(9), 1413–1425.
Zhou, Q., & Cui, X. (2008). Research on multiobjective flow shop scheduling with stochastic processing times and machine breakdowns. In 2008 IEEE International Conference on Service Operations and Logistics, and Informatics (Vol. 22, pp. 1718–1724). IEEE.
Armentano, V. A., & de França Filho, M. F. (2007). Minimizing total tardiness in parallel machine scheduling with setup times: An adaptive memory-based GRASP approach. European Journal of Operational Research, 183(1), 100–114.
Arora, D., & Agarwal, G. (2016). Meta-heuristic approaches for flowshop scheduling problems: a review. International Journal of Advanced Operations Management, 8(1), 1.
Arroyo, J. E. C., & de Souza Pereira, A. A. (2011). A GRASP heuristic for the multi-objective permutation flowshop scheduling problem. The International Journal of Advanced Manufacturing Technology, 55(5–8), 741–753.
Aytug, H., Lawley, M. a., McKay, K., Mohan, S., & Uzsoy, R. (2005). Executing production schedules in the face of uncertainties: A review and some future directions. European Journal of Operational Research, 161(1), 86–110.
Azadeh, A., Jeihoonian, M., Shoja, B. M., & Seyedmahmoudi, S. H. (2012). An integrated neural network–simulation algorithm for performance optimisation of the bi-criteria two-stage assembly flow-shop scheduling problem with stochastic activities. International Journal of Production Research, 50(24), 7271–7284.
Baker, K. R., & Altheimer, D. (2012). Heuristic solution methods for the stochastic flow shop problem. European Journal of Operational Research, 216(1), 172–177.
Behnamian, J., & Fatemi Ghomi, S. M. T. (2014). Multi-objective fuzzy multiprocessor flowshop scheduling. Applied Soft Computing, 21, 139–148.
Celano, G., Costa, A., & Fichera, S. (2003). An evolutionary algorithm for pure fuzzy flowshop scheduling problems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(06), 655–669.
Chassaing, M., Fontanel, J., Lacomme, P., Ren, L., Tchernev, N., & Villechenon, P. (2014). A GRASP×ELS approach for the job-shop with a web service paradigm packaging. Expert Systems with Applications, 41(2), 544–562.
Ciavotta, M., Minella, G., & Ruiz, R. (2013). Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study. European Journal of Operational Research, 227(2), 301–313.
Croes, G. A. (1958). A method for solving traveling-salesman Problems. Operations Research, 6(6), 791–812.
Damodaran, P., Vélez-Gallego, M. C., & Maya, J. (2011). A GRASP approach for makespan minimization on parallel batch processing machines. Journal of Intelligent Manufacturing, 22(5), 767–777.
Davoudpour, H., & Ashrafi, M. (2009). Solving multi-objective SDST flexible flow shop using GRASP algorithm. The International Journal of Advanced Manufacturing Technology, 44(7–8), 737–747.
Ebrahimi, M., Fatemi Ghomi, S. M. T. M. T., & Karimi, B. (2014). Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates. Applied Mathematical Modelling, 38(9–10), 2490–2504.
Elyasi, A., & Salmasi, N. (2013). Stochastic scheduling with minimizing the number of tardy jobs using chance constrained programming. Mathematical and Computer Modelling, 57(5–6), 1154–1164.
Fazayeli, M., Aleagha, M.-R., Bashirzadeh, R., & Shafaei, R. (2016). A hybrid meta-heuristic algorithm for flowshop robust scheduling under machine breakdown uncertainty. International Journal of Computer Integrated Manufacturing, 29(7), 709–719.
Feo, T. A., & Resende, M. G. . (1989). A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters, 8(2), 67–71.
Fernandez-Viagas, V., Ruiz, R., & Framinan, J. M. (2017). A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation. European Journal of Operational Research, 257(3), 707–721.
Festa, P., & Resende, M. G. C. (2009). An annotated bibliography of GRASP-Part II: Applications. International Transactions in Operational Research, 16(2), 131–172.
Forst, F. G. (1995). Bicriterion stochastic scheduling on one or more machines. European Journal of Operational Research, 80(2), 404–409.
Framinan, J. M., Gupta, J. N. D., & Leisten, R. (2004). A review and classification of heuristics for permutation flow-shop scheduling with makespan objective. Journal of the Operational Research Society, 55(12), 1243–1255.
Framinan, J. M., & Perez-Gonzalez, P. (2015). On heuristic solutions for the stochastic flowshop scheduling problem. European Journal of Operational Research, 246(2), 413–420.
Garey, M. R., & Johnson, D. S. (1977). Two-processor scheduling with start-times and deadlines. SIAM Journal on Computing, 6(3), 416–426.
González-Neira, E. M., García-Cáceres, R. G., Caballero-Villalobos, J. P., Molina-Sánchez, L. P., & Montoya-Torres, J. R. (2016). Stochastic flexible flow shop scheduling problem under quantitative and qualitative decision criteria. Computers & Industrial Engineering, 101, 128–144.
González-Neira, E. M., Montoya-Torres, J. R., & Barrera, D. (2017). Flow-shop scheduling problem under uncertainties: Review and trends. International Journal of Industrial Engineering Computations, 8(4), 399–426.
Gourgand, M., Grangeon, N., & Norre, S. (2000). A review of the static stochastic flow-shop scheduling problem. Journal of Decision Systems, 9(2), 1–31.
Gupta, J. N. D., & Stafford, E. F. (2006). Flowshop scheduling research after five decades. European Journal of Operational Research, 169(3), 699–711.
Han, Y., Gong, D., Jin, Y., & Pan, Q. (2017). Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns. IEEE Transactions on Cybernetics, 1–14.
Huang, C.-S., Huang, Y.-C., & Lai, P.-J. (2012). Modified genetic algorithms for solving fuzzy flow shop scheduling problems and their implementation with CUDA. Expert Systems with Applications, 39(5), 4999–5005.
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.
Karimi, N., Zandieh, M., & Karamooz, H. R. (2010). Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach. Expert Systems with Applications, 37(6), 4024–4032.
Knowles, J. D., & Corne, D. W. (2000). Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2), 149–172.
Li, Z., & Ierapetritou, M. (2008). Process scheduling under uncertainty: Review and challenges. Computers & Chemical Engineering, 32(4–5), 715–727.
Liefooghe, A., Basseur, M., Humeau, J., Jourdan, L., & Talbi, E.-G. (2012). On optimizing a bi-objective flowshop scheduling problem in an uncertain environment. Computers & Mathematics with Applications, 64(12), 3747–3762.
Lin, J. T., & Chen, C.-M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing. Simulation Modelling Practice and Theory, 51, 100–114.
Martí, R., Campos, V., Resende, M. G. C., & Duarte, A. (2015). Multiobjective GRASP with Path Relinking. European Journal of Operational Research, 240(1), 54–71.
Minella, G., Ruiz, R., & Ciavotta, M. (2008). A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem. INFORMS Journal on Computing, 20(3), 451–471.
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.
Mou, J., Li, X., Gao, L., & Yi, W. (2015). An effective L-MONG algorithm for solving multi-objective flow-shop inverse scheduling problems. Journal of Intelligent Manufacturing.
Nagano, M. S., & Miyata, H. H. (2016). Review and classification of constructive heuristics mechanisms for no-wait flow shop problem. The International Journal of Advanced Manufacturing Technology, 86(5–8), 2161–2174.
Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91–95.
Niño-Navarrete, A. M., & Caballero-Villalobos, J. P. (2009). Evaluación de funciones de utilidad de GRASP en la programación de producción para minimizar la tardanza total ponderada en una máquina. Revista Ingeniería, 14(2), 51–58.
Pan, Q.-K., & Ruiz, R. (2013). A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime. Computers & Operations Research, 40(1), 117–128.
Pinedo, M. L. (2012). Scheduling: Theory, algorithms and systems. Springer (4th ed., Vol. 4). New York: Springer Science & Business Media.
Prabhaharan, G., Khan, B. S. H., & Rakesh, L. (2006). Implementation of grasp in flow shop scheduling. The International Journal of Advanced Manufacturing Technology, 30(11–12), 1126–1131.
Qin, W., Zhang, J., & Song, D. (2015). An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time. Journal of Intelligent Manufacturing.
Rahmani, D., Ramezanian, R., & Mehrabad, M. S. (2014). Multi-objective flow shop scheduling problem with stochastic parameters: fuzzy goal programming approach. International Journal of Operational Research, 21(3), 322–340.
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.
Resende, M. C., & Ribeiro, C. (2003). Greedy Randomized Adaptive Search Procedures. In F. Glover & G. Kochenberger (Eds.), Handbook of Metaheuristics SE - 8 (Vol. 57, pp. 219–249). Springer US.
Reza Hejazi, S., & Saghafian, S. (2005). Flowshop-scheduling problems with makespan criterion: a review. International Journal of Production Research, 43(14), 2895–2929.
Rossit, D. A., Tohmé, F., & Frutos, M. (2018). The Non-Permutation Flow-Shop scheduling problem: A literature review. Omega, 77, 143–153.
Ruiz, R., & Maroto, C. (2005). A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research, 165(2), 479–494.
Sun, Y., Zhang, C., Gao, L., & Wang, X. (2011). Multi-objective optimization algorithms for flow shop scheduling problem: a review and prospects. The International Journal of Advanced Manufacturing Technology, 55(5–8), 723–739.
Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2), 278–285.
Temiz, I., & Erol, S. (2007). Multiobjective genetic algorithm for fuzzy flowshop scheduling problem (Bulanik akiş ti̇pi̇ çi̇zelgeleme problemi̇ i̇çi̇n çok amaçli geneti̇k algori̇tma). Journal of the Faculty of Engineering and Architecture of Gazi University, 22(4), 855–862. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-38549127324&partnerID=tZOtx3y1
Vallada, E., Ruiz, R., & Minella, G. (2008). Minimising total tardiness in the m-machine flowshop problem: A review and evaluation of heuristics and metaheuristics. Computers & Operations Research, 35(4), 1350–1373.
Yenisey, M. M., & Yagmahan, B. (2014). Multi-objective permutation flow shop scheduling problem: Literature review, classification and current trends. Omega (Vol. 45). Pergamon Press.
Yenisey, M. M., & Yagmahan, B. (2014). Multi-objective permutation flow shop scheduling problem: Literature review, classification and current trends. Omega, 45(C), 119–135.
Ying, K. (2015). Scheduling the two-machine flowshop to hedge against processing time uncertainty. Journal of the Operational Research Society, 66(9), 1413–1425.
Zhou, Q., & Cui, X. (2008). Research on multiobjective flow shop scheduling with stochastic processing times and machine breakdowns. In 2008 IEEE International Conference on Service Operations and Logistics, and Informatics (Vol. 22, pp. 1718–1724). IEEE.