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How to cite this paper
APA: Madić, M., Kovačević, M & Radovanović, M. (2014). Application of multi-stage Monte Carlo method for solving machining optimization problems. International Journal of Industrial Engineering Computations, 5(4), 647-659.
Chicago/Turabian: Madić, M., Kovačević, M & Radovanović, M. 2014. "Application of multi-stage Monte Carlo method for solving machining optimization problems." International Journal of Industrial Engineering Computations 5, no. 4 (2014): 647-659.
AMA: Madić, M., Kovačević, M & Radovanović, M. Application of multi-stage Monte Carlo method for solving machining optimization problems. International Journal of Industrial Engineering Computations. 2014;5(4):647-659.
References
Besseris, G. J. (2008). Multi-response optimisation using Taguchi method and super ranking concept. Journal of Manufacturing Technology Management,19(8), 1015-1029.
Besseris, G. J. (2008). Multi-response optimisation using Taguchi method and super ranking concept. Journal of Manufacturing Technology Management,19(8), 1015-1029.
Cayda?, U., & Hasçal?k, A. (2008). A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. Journal of materials processing technology, 202(1), 574-582.
Davim, J. P. (2001). A note on the determination of optimal cutting conditions for surface finish obtained in turning using design of experiments. Journal of materials processing technology, 116(2), 305-308.
Dhavlikar, M. N., Kulkarni, M. S., & Mariappan, V. (2003). Combined Taguchi and dual response method for optimization of a centerless grinding operation.Journal of Materials Processing Technology, 132(1), 90-94.
Khayet, M., & Cojocaru, C. (2012). Artificial neural network modeling and optimization of desalination by air gap membrane distillation. Separation and Purification Technology, 86, 171-182.
Kilickap, E., Huseyinoglu, M., & Yardimeden, A. (2011). Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. The International Journal of Advanced Manufacturing Technology, 52(1-4), 79-88.
Kova?evi?, M., Madi?, M., & Radovanovi?, M. (2013). Software prototype for validation of machining optimization solutions obtained with meta-heuristic algorithms. Expert Systems with Applications, 40(17), 6985-6996.
Kroese, D. P., Taimre, T., & Botev, Z. I. (2011). Handbook of Monte Carlo Methods (Vol. 706). John Wiley & Sons.
Madi?, M., Markovi?, D., & Radovanovi?, M. (2013). Comparison of meta-heuristic algorithms for solving machining optimization problems. Facta universitatis-series: Mechanical Engineering, 11(1), 29-44.
Madi?, M., & Radovanovi?, M. (2014a). Possibilities of using Monte Carlo method for solving machining optimization problems. Facta Universitatis, Series: Mechanical Engineering, 12(1), 27-36.
Madi? M, Radovanovi? M (2014b) Optimization of machining processes using pattern search algorithm. International Journal of Industrial Engineering Computations, 5(2), 223–234.
Mosegaard, K., & Sambridge, M. (2002). Monte Carlo analysis of inverse problems. Inverse Problems, 18(3), 29-34.
Mukherjee, I., & Ray, P. K. (2006). A review of optimization techniques in metal cutting processes. Computers & Industrial Engineering, 50(1), 15-34.
Munda, J., & Bhattacharyya, B. (2008). Investigation into electrochemical micromachining (EMM) through response surface methodology based approach. The International Journal of Advanced Manufacturing Technology,35(7-8), 821-832.
Rao, R. V., Pawar, P. J., & Shankar, R. (2008). Multi-objective optimization of electrochemical machining process parameters using a particle swarm optimization algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222(8), 949-958.
Rao, R. V., & Pawar, P. J. (2009). Modelling and optimization of process parameters of wire electrical discharge machining. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(11), 1431-1440.
Venkata Rao, R., & Pawar, P. J. (2010). Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms. Applied soft computing, 10(2), 445-456.
Rao, R. V., & Kalyankar, V. D. (2011). Parameters optimization of advanced machining processes using TLBO algorithm. EPPM, Singapore, 20, 21–31
Venkata Rao, R., & Kalyankar, V. D. (2013). Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26(1), 524-531.
Samanta, S., & Chakraborty, S. (2011). Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 24(6), 946-957.
Sarkar, B. R., Doloi, B., & Bhattacharyya, B. (2006). Parametric analysis on electrochemical discharge machining of silicon nitride ceramics. The International Journal of Advanced Manufacturing Technology, 28(9-10), 873-881.
Savas, V., & Ozay, C. (2008). The optimization of the surface roughness in the process of tangential turn-milling using genetic algorithm. The International Journal of Advanced Manufacturing Technology, 37(3-4), 335-340.
Yang, X. S. (2010). Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons.
Y?ld?z, A. R. (2009). A novel particle swarm optimization approach for product design and manufacturing. The International Journal of Advanced Manufacturing Technology, 40(5-6), 617-628.
Yusup, N., Zain, A. M., & Hashim, S. Z. M. (2012). Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011).Expert Systems with Applications, 39(10), 9909-9927.
Yusup, N., Sarkheyli, A., Zain, A. M., Hashim, S. Z. M., & Ithnin, N. (2013). Estimation of optimal machining control parameters using artificial bee colony.Journal of Intelligent Manufacturing, 1-10.
Zain, A. M., Haron, H., & Sharif, S. (2011). Genetic algorithm and simulated annealing to estimate optimal process parameters of the abrasive waterjet machining. Engineering with computers, 27(3), 251-259.
Besseris, G. J. (2008). Multi-response optimisation using Taguchi method and super ranking concept. Journal of Manufacturing Technology Management,19(8), 1015-1029.
Cayda?, U., & Hasçal?k, A. (2008). A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. Journal of materials processing technology, 202(1), 574-582.
Davim, J. P. (2001). A note on the determination of optimal cutting conditions for surface finish obtained in turning using design of experiments. Journal of materials processing technology, 116(2), 305-308.
Dhavlikar, M. N., Kulkarni, M. S., & Mariappan, V. (2003). Combined Taguchi and dual response method for optimization of a centerless grinding operation.Journal of Materials Processing Technology, 132(1), 90-94.
Khayet, M., & Cojocaru, C. (2012). Artificial neural network modeling and optimization of desalination by air gap membrane distillation. Separation and Purification Technology, 86, 171-182.
Kilickap, E., Huseyinoglu, M., & Yardimeden, A. (2011). Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. The International Journal of Advanced Manufacturing Technology, 52(1-4), 79-88.
Kova?evi?, M., Madi?, M., & Radovanovi?, M. (2013). Software prototype for validation of machining optimization solutions obtained with meta-heuristic algorithms. Expert Systems with Applications, 40(17), 6985-6996.
Kroese, D. P., Taimre, T., & Botev, Z. I. (2011). Handbook of Monte Carlo Methods (Vol. 706). John Wiley & Sons.
Madi?, M., Markovi?, D., & Radovanovi?, M. (2013). Comparison of meta-heuristic algorithms for solving machining optimization problems. Facta universitatis-series: Mechanical Engineering, 11(1), 29-44.
Madi?, M., & Radovanovi?, M. (2014a). Possibilities of using Monte Carlo method for solving machining optimization problems. Facta Universitatis, Series: Mechanical Engineering, 12(1), 27-36.
Madi? M, Radovanovi? M (2014b) Optimization of machining processes using pattern search algorithm. International Journal of Industrial Engineering Computations, 5(2), 223–234.
Mosegaard, K., & Sambridge, M. (2002). Monte Carlo analysis of inverse problems. Inverse Problems, 18(3), 29-34.
Mukherjee, I., & Ray, P. K. (2006). A review of optimization techniques in metal cutting processes. Computers & Industrial Engineering, 50(1), 15-34.
Munda, J., & Bhattacharyya, B. (2008). Investigation into electrochemical micromachining (EMM) through response surface methodology based approach. The International Journal of Advanced Manufacturing Technology,35(7-8), 821-832.
Rao, R. V., Pawar, P. J., & Shankar, R. (2008). Multi-objective optimization of electrochemical machining process parameters using a particle swarm optimization algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222(8), 949-958.
Rao, R. V., & Pawar, P. J. (2009). Modelling and optimization of process parameters of wire electrical discharge machining. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(11), 1431-1440.
Venkata Rao, R., & Pawar, P. J. (2010). Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms. Applied soft computing, 10(2), 445-456.
Rao, R. V., & Kalyankar, V. D. (2011). Parameters optimization of advanced machining processes using TLBO algorithm. EPPM, Singapore, 20, 21–31
Venkata Rao, R., & Kalyankar, V. D. (2013). Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26(1), 524-531.
Samanta, S., & Chakraborty, S. (2011). Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 24(6), 946-957.
Sarkar, B. R., Doloi, B., & Bhattacharyya, B. (2006). Parametric analysis on electrochemical discharge machining of silicon nitride ceramics. The International Journal of Advanced Manufacturing Technology, 28(9-10), 873-881.
Savas, V., & Ozay, C. (2008). The optimization of the surface roughness in the process of tangential turn-milling using genetic algorithm. The International Journal of Advanced Manufacturing Technology, 37(3-4), 335-340.
Yang, X. S. (2010). Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons.
Y?ld?z, A. R. (2009). A novel particle swarm optimization approach for product design and manufacturing. The International Journal of Advanced Manufacturing Technology, 40(5-6), 617-628.
Yusup, N., Zain, A. M., & Hashim, S. Z. M. (2012). Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011).Expert Systems with Applications, 39(10), 9909-9927.
Yusup, N., Sarkheyli, A., Zain, A. M., Hashim, S. Z. M., & Ithnin, N. (2013). Estimation of optimal machining control parameters using artificial bee colony.Journal of Intelligent Manufacturing, 1-10.
Zain, A. M., Haron, H., & Sharif, S. (2011). Genetic algorithm and simulated annealing to estimate optimal process parameters of the abrasive waterjet machining. Engineering with computers, 27(3), 251-259.