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How to cite this paper
APA: Lee, T., Loong, Y & Tan, S. (2019). A hybrid genetic-gravitational search algorithm for a multi-objective flow shop scheduling problem. International Journal of Industrial Engineering Computations, 10(3), 331-348.
Chicago/Turabian: Lee, T., Loong, Y & Tan, S. 2019. "A hybrid genetic-gravitational search algorithm for a multi-objective flow shop scheduling problem." International Journal of Industrial Engineering Computations 10, no. 3 (2019): 331-348.
AMA: Lee, T., Loong, Y & Tan, S. A hybrid genetic-gravitational search algorithm for a multi-objective flow shop scheduling problem. International Journal of Industrial Engineering Computations. 2019;10(3):331-348.
References
Abraham, A., Grosan, C., & Pedrycz, W. (2008). Engineering Evolutionary Intelligent Systems. Springer Berlin Heidelberg.
Chen, T., Rajendran, C,. & Wu, C.W. (2013). Advanced dispatching rules for large-scale manufacturing systems. The International Journal of Advanced Manufacturing Technology, 67(1-4), 1-3.
Choi, S. H., & Wang, K. (2012). Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach. Computers & Industrial Engineering, 63(2), 362-373. doi:10.1016/j.cie.2012.04.001
El Bouri, A. & Amin, G.R. (2015). A combined OWA–DEA method for dispatching rule selection. Computers & Industrial Engineering, 88, 470-478.
Eldos, T. & Qasim, R.A. (2013). On the performance of gravitational search algorithm. International Journal of Advanced Computer Science and Applications, 4(8), 74-78.
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company.
Jayamohan, M. S., & Rajendran, C. (2000). A comparative analysis of two different approaches to scheduling in flexible flow shops. Production Planning & Control, 11(6), 572-580. doi:10.1080/095372800414133
Joo, B. J., Choi, Y. C., & Xirouchakis, P. (2013). Dispatching rule-based algorithms for a dynamic flexible flow shop scheduling problem with time-dependent process defect rate and quality Feedback. Procedia CIRP, 7, 163-168.
Jungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2008). Algorithm for flexible flow shop problems with unrelated parallel machines, setup times and dual criteria. International Journal of Advance Manufacturing Technology, 37(3-4), 354-370. doi:10.1007/s00170-007-0977-0
Khalouli, S., Ghedjati, F., & Hamzaoui, A. (2010). A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop. Engineering Applications of Artificial Intelligence, 23, 765-771.
Kim, Y.-D., Joo, B.-J., & Shin, J.-H. (2007). Heuristics for a two-stage hybrid flowshop scheduling problem with ready times and a product-mix ratio constraint. Journal of Heuristics, 15(1), 19-42. doi:10.1007/s10732-007-9061-z
Kumar, Y. & Sahoo, G. (2014). A review on gravitational search algorithm and its applications to data clustering & classification. International Journal of Intelligent Systems and Applications, 6(6), 79-93.
Korytkowski, P., Wiśniewski, T., & Rymaszewski, S. (2013a). An evolutionary simulation-based optimization approach for dispatching scheduling. Simulation Modelling Practice and Theory, 35, 69-85. doi:10.1016/j.simpat.2013.03.006
Korytkowski, P., Rymaszewski, S., & Wiśniewski, T. (2013b). Ant colony optimization for job shop scheduling using multi-attribute dispatching rules. The International Journal of Advanced Manufacturing Technology, 67(1-4), 231-241.
Liptak, B.G. (2005). Instrument Engineers' Handbook, Fourth Edition, Volume Two: Process Control and Optimization. CRC Press.
Li, D., Meng, X., Liang, Q., & Zhao, J. (2014). A heuristic-search genetic algorithm for multi-stage hybrid flow shop scheduling with single processing machines and batch processing machines. Journal of Intelligent Manufacturing, 26(5), 873-890.
Li, D. (2014). A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints. Journal of Intelligent Manufacturing, 27(4), 725-739.
Lu, M.-S., & Liu, Y.-J. (2010). Dynamic dispatching for a flexible manufacturing system based on fuzzy logic. International Journal of Advanced Manufacturing Technology, 54(9-12), 1057-1065.
Morita, H., & Shio, N. (2005). Hybrid branch and bound method with genetic algorithm for flexible flowshop scheduling problem. JSME International Journal Series C-Mechanical Systems Machine Elements and Manufacturing, 48(1), 46-52.
Nguyen, S., Zhang, M., Johnston, M., & Tan, K. C. (2013). Learning iterative dispatching rules for job shop scheduling with genetic programming. International Journal of Advanced Manufacturing Technology, 67(1-4), 85-100.
Pérez, M.A.F. and Raupp, F.M.P. (2014). A Newton-based heuristic algorithm for multi-objective flexible job-shop scheduling problem. Journal of Intelligent Manufacturing, 27(2), 409-416.
Pinedo, M. (2008). Scheduling: Theory, algorithm, and systems (3rd ed.). New York: Springer.
Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232-2248.
Ribas, I., Leisten, R., & Framiñan, J. M. (2010). Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Computers & Operations Research, 37(8), 1439-1454.
Rodriguez, J. A. V., & Salhi, A. (2005, September). Performance of single stage representation genetic algorithms in scheduling flexible flow shops. In 2005 IEEE Congress on Evolutionary Computation (Vol. 2, pp. 1364-1371). IEEE.
Ruiz, R., & Vázquenz-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1-18.
Sabri, N.M., Puteh, M., & Mahmood, M.R. (2013). A review of gravitational search algorithm. International Journal of Advance in Soft Computing, 5(3).
Simon, D. (2013). Evolutionary Optimization Algorithms. Wiley.
Singh, A. & Deep, K. (2015). Real coded genetic algorithm operators embedded in gravitational search algorithm for continuous optimization. International Journal of Intelligent Systems and Applications, 7(12), 1-12.
Spears, W.M. (2000). Evolutionary Algorithms: The Role of Mutation and Recombination. Springer Berlin Heidelberg.
Tian, Y., Li, D., Zhou, P., Guo, R., & Liu, Z. (2018). An ACO-based hyperheuristic with dynamic decision blocks for intercell scheduling. Journal of Intelligent Manufacturing, 29(8), 1905–1921.
Vázquez-Rodríguez, J.A. & Petrovic, S. (2009). A new dispatching rule based genetic algorithm for the multi-objective job shop problem. Journal of Heuristics, 16(6), 771-793.
Wang, K., & Choi, S. H. (2012). A decomposition-based approach to flexible flow shop scheduling under machine breakdown. International Journal of Production Research, 50(1), 215-234. doi:10.1080/00207543.2011.571456
Wang, K., & Choi, S. H. (2014). A holonic approach to flexible flow shop scheduling under stochastic processing times. Computers & Operations Research, 43, 157-168.
Xu, Y. (2013). An effective immune algorithm based on novel dispatching rules for the flexible flow-shop scheduling problem with multiprocessor tasks. The International Journal of Advanced Manufacturing Technology, 67(1-4), 121-135.
Chen, T., Rajendran, C,. & Wu, C.W. (2013). Advanced dispatching rules for large-scale manufacturing systems. The International Journal of Advanced Manufacturing Technology, 67(1-4), 1-3.
Choi, S. H., & Wang, K. (2012). Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach. Computers & Industrial Engineering, 63(2), 362-373. doi:10.1016/j.cie.2012.04.001
El Bouri, A. & Amin, G.R. (2015). A combined OWA–DEA method for dispatching rule selection. Computers & Industrial Engineering, 88, 470-478.
Eldos, T. & Qasim, R.A. (2013). On the performance of gravitational search algorithm. International Journal of Advanced Computer Science and Applications, 4(8), 74-78.
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company.
Jayamohan, M. S., & Rajendran, C. (2000). A comparative analysis of two different approaches to scheduling in flexible flow shops. Production Planning & Control, 11(6), 572-580. doi:10.1080/095372800414133
Joo, B. J., Choi, Y. C., & Xirouchakis, P. (2013). Dispatching rule-based algorithms for a dynamic flexible flow shop scheduling problem with time-dependent process defect rate and quality Feedback. Procedia CIRP, 7, 163-168.
Jungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2008). Algorithm for flexible flow shop problems with unrelated parallel machines, setup times and dual criteria. International Journal of Advance Manufacturing Technology, 37(3-4), 354-370. doi:10.1007/s00170-007-0977-0
Khalouli, S., Ghedjati, F., & Hamzaoui, A. (2010). A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop. Engineering Applications of Artificial Intelligence, 23, 765-771.
Kim, Y.-D., Joo, B.-J., & Shin, J.-H. (2007). Heuristics for a two-stage hybrid flowshop scheduling problem with ready times and a product-mix ratio constraint. Journal of Heuristics, 15(1), 19-42. doi:10.1007/s10732-007-9061-z
Kumar, Y. & Sahoo, G. (2014). A review on gravitational search algorithm and its applications to data clustering & classification. International Journal of Intelligent Systems and Applications, 6(6), 79-93.
Korytkowski, P., Wiśniewski, T., & Rymaszewski, S. (2013a). An evolutionary simulation-based optimization approach for dispatching scheduling. Simulation Modelling Practice and Theory, 35, 69-85. doi:10.1016/j.simpat.2013.03.006
Korytkowski, P., Rymaszewski, S., & Wiśniewski, T. (2013b). Ant colony optimization for job shop scheduling using multi-attribute dispatching rules. The International Journal of Advanced Manufacturing Technology, 67(1-4), 231-241.
Liptak, B.G. (2005). Instrument Engineers' Handbook, Fourth Edition, Volume Two: Process Control and Optimization. CRC Press.
Li, D., Meng, X., Liang, Q., & Zhao, J. (2014). A heuristic-search genetic algorithm for multi-stage hybrid flow shop scheduling with single processing machines and batch processing machines. Journal of Intelligent Manufacturing, 26(5), 873-890.
Li, D. (2014). A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints. Journal of Intelligent Manufacturing, 27(4), 725-739.
Lu, M.-S., & Liu, Y.-J. (2010). Dynamic dispatching for a flexible manufacturing system based on fuzzy logic. International Journal of Advanced Manufacturing Technology, 54(9-12), 1057-1065.
Morita, H., & Shio, N. (2005). Hybrid branch and bound method with genetic algorithm for flexible flowshop scheduling problem. JSME International Journal Series C-Mechanical Systems Machine Elements and Manufacturing, 48(1), 46-52.
Nguyen, S., Zhang, M., Johnston, M., & Tan, K. C. (2013). Learning iterative dispatching rules for job shop scheduling with genetic programming. International Journal of Advanced Manufacturing Technology, 67(1-4), 85-100.
Pérez, M.A.F. and Raupp, F.M.P. (2014). A Newton-based heuristic algorithm for multi-objective flexible job-shop scheduling problem. Journal of Intelligent Manufacturing, 27(2), 409-416.
Pinedo, M. (2008). Scheduling: Theory, algorithm, and systems (3rd ed.). New York: Springer.
Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232-2248.
Ribas, I., Leisten, R., & Framiñan, J. M. (2010). Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Computers & Operations Research, 37(8), 1439-1454.
Rodriguez, J. A. V., & Salhi, A. (2005, September). Performance of single stage representation genetic algorithms in scheduling flexible flow shops. In 2005 IEEE Congress on Evolutionary Computation (Vol. 2, pp. 1364-1371). IEEE.
Ruiz, R., & Vázquenz-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1-18.
Sabri, N.M., Puteh, M., & Mahmood, M.R. (2013). A review of gravitational search algorithm. International Journal of Advance in Soft Computing, 5(3).
Simon, D. (2013). Evolutionary Optimization Algorithms. Wiley.
Singh, A. & Deep, K. (2015). Real coded genetic algorithm operators embedded in gravitational search algorithm for continuous optimization. International Journal of Intelligent Systems and Applications, 7(12), 1-12.
Spears, W.M. (2000). Evolutionary Algorithms: The Role of Mutation and Recombination. Springer Berlin Heidelberg.
Tian, Y., Li, D., Zhou, P., Guo, R., & Liu, Z. (2018). An ACO-based hyperheuristic with dynamic decision blocks for intercell scheduling. Journal of Intelligent Manufacturing, 29(8), 1905–1921.
Vázquez-Rodríguez, J.A. & Petrovic, S. (2009). A new dispatching rule based genetic algorithm for the multi-objective job shop problem. Journal of Heuristics, 16(6), 771-793.
Wang, K., & Choi, S. H. (2012). A decomposition-based approach to flexible flow shop scheduling under machine breakdown. International Journal of Production Research, 50(1), 215-234. doi:10.1080/00207543.2011.571456
Wang, K., & Choi, S. H. (2014). A holonic approach to flexible flow shop scheduling under stochastic processing times. Computers & Operations Research, 43, 157-168.
Xu, Y. (2013). An effective immune algorithm based on novel dispatching rules for the flexible flow-shop scheduling problem with multiprocessor tasks. The International Journal of Advanced Manufacturing Technology, 67(1-4), 121-135.