Keywords: stochastic programming; progressive hedging; parallel computing; steel production; heat transfer; phase change
@article{10_14736_kyb_2017_6_1047,
author = {Klime\v{s}, Lubom{\'\i}r and Popela, Pavel and Mauder, Tom\'a\v{s} and \v{S}t\v{e}tina, Josef and Charv\'at, Pavel},
title = {Two-stage stochastic programming approach to a {PDE-constrained} steel production problem with the moving interface},
journal = {Kybernetika},
pages = {1047--1070},
year = {2017},
volume = {53},
number = {6},
doi = {10.14736/kyb-2017-6-1047},
mrnumber = {3758934},
zbl = {06861640},
language = {en},
url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2017-6-1047/}
}
TY - JOUR AU - Klimeš, Lubomír AU - Popela, Pavel AU - Mauder, Tomáš AU - Štětina, Josef AU - Charvát, Pavel TI - Two-stage stochastic programming approach to a PDE-constrained steel production problem with the moving interface JO - Kybernetika PY - 2017 SP - 1047 EP - 1070 VL - 53 IS - 6 UR - http://geodesic.mathdoc.fr/articles/10.14736/kyb-2017-6-1047/ DO - 10.14736/kyb-2017-6-1047 LA - en ID - 10_14736_kyb_2017_6_1047 ER -
%0 Journal Article %A Klimeš, Lubomír %A Popela, Pavel %A Mauder, Tomáš %A Štětina, Josef %A Charvát, Pavel %T Two-stage stochastic programming approach to a PDE-constrained steel production problem with the moving interface %J Kybernetika %D 2017 %P 1047-1070 %V 53 %N 6 %U http://geodesic.mathdoc.fr/articles/10.14736/kyb-2017-6-1047/ %R 10.14736/kyb-2017-6-1047 %G en %F 10_14736_kyb_2017_6_1047
Klimeš, Lubomír; Popela, Pavel; Mauder, Tomáš; Štětina, Josef; Charvát, Pavel. Two-stage stochastic programming approach to a PDE-constrained steel production problem with the moving interface. Kybernetika, Tome 53 (2017) no. 6, pp. 1047-1070. doi: 10.14736/kyb-2017-6-1047
[1] Alquarashi, A., Etemadi, A. H., Khodaei, A.: Treatment of uncertainty for next generation power systems: State-of-the-art in stochastic optimization. Electr. Power Syst. Res. 141 (2016), 233-245. | DOI
[2] Barttfeld, M., Alleborn, N., Durst, F.: Dynamic optimization of multiple-zone air impingement drying process. Comput. Chem. Engrg. 30 (2006), 467-489. | DOI
[3] Birge, J. R., Louveaux, F.: Introduction to Stochastic Programming. Springer, New York 2011. | MR
[4] Brimacombe, J. K., Sorimachi, K.: Crack formation in continuous-casting of steel. Metal. Trans. B. Proc. Metal. 8 (1977), 489-505. | DOI
[5] Carvalho, E. P., Martínez, J., Martínez, J. M., Pisnitchenko, F.: On optimization strategies for parameter estimation in models governed by partial differential equations. Math. Comput. Simul. 114 (2015), 14-24. | DOI | MR
[6] Carrasco, M., Ivorra, B., Ramos, A. M.: Stochastic topology design optimization for continuous elastic materials. Comput. Meth. Appl. Mech. Engrg. 289 (2015), 131-154. | DOI | MR
[7] Carpentier, P. L., Gendreau, M., Bastin, F.: Long-term management of a hydroelectric multireservoir system under uncertainty using the progressive hedging algorithm. Water Resour. Res. 49 (2013), 2812-2827. | DOI
[8] Cheng, Y. M., Li, D. Z., Li, N., Lee, Y. Y., Au, S. K.: Solution of some engineering partial differential equations governed by the minimal of a functional by global optimization method. J. Mech. 29 (2013), 507-516. | DOI
[9] Drud, A.: CONOPT - A GRG code for large sparse dynamic nonlinear optimization problems. Math. Program. 31 (1985), 153-191. | DOI | MR
[10] Gade, D., Ryan, G. Hackebeil. S. M., Watson, J.-P., Wets, R. J.-B., Woodruff, D. L.: Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs. Math. Prog. 157 (2016), 47-67. | DOI | MR
[11] Gonçalves, R. E. C., Finardi, E. C., Silva, E. L. da: Applying different decomposition schemes using the progressive hedging algorithm to the operation planning problem of a hydrothermal system. Electr. Power Syst. Res. 83 (2012), 19-27. | DOI
[12] Gul, S., Denton, B. T., Fowler, J. W.: A progressive hedging approach for surgery planning under uncertainty. INFORMS J. Comput. 27 (2015), 755-772. | DOI | MR
[13] Ikeda, S., Ooka, R.: A new optimization strategy for the operating schedule of energy systems under uncertainty of renewable energy sources and demand changes. Energ. Build. 125 (2016), 75-85. | DOI
[14] Bergman, T. L., Lavine, A. S., Incropera, F. P., Dewitt, D. P.: Fundamentals of Heat and Mass Transfer. Seventh edition. Wiley, New York 2011.
[15] Klimeš, L.: Stochastic Programming Algorithms. Master Thesis. Brno University of Technology, 2010.
[16] Klimeš, L., Popela, P.: An implementation of progressive hedging algorithm for engineering problem. In: Proc. 16th International Conference on Soft Computing MENDEL, Brno 2010, pp. 459-464.
[17] Klimeš, L., Popela, P., Štětina, J.: Decomposition approach applied to stochastic optimization of continuous steel casting. In: Proc. 17th International Conference on Soft Computing MENDEL, Brno 2011, pp. 314-319.
[18] Klimeš, L., Mauder, T., Štětina, J.: Stochastic approach and optimal control of continuous steel casting process by using progressive hedging algorithm. In: Proc. 20th International Conference on Materials and Metallurgy METAL, Brno 2011, pp. 146-151.
[19] Marca, M. La, Armbruster, D., Herty, M., Ringhofer, C.: Control of continuum models of production systems. IEEE Trans. Automat. Control 55 (2010), 2511-2526. | DOI | MR
[20] Lamghari, A., Dimitrakopoulos, R.: Progressive hedging applied as a metaheuristic to schedule production in open-pit mines accounting for reserve uncertainty. Eur. J. Oper. Res. 253 (2016), 843-855. | DOI | MR
[21] Liu, J., Liu, C.: Optimization of mold inverse oscillation control parameters in continuous casting process. Mater. Manuf. Process. 30 (2015), 563-568. | DOI
[22] Mills, K. C., Ramirez-Lopez, P., Lee, P. D., Santillana, B., Thomas, B. G., Morales, R.: Looking into continuous casting mould. Ironmak. Steelmak. 41 (2014), 242-249. | DOI
[23] Mauder, T., Kavička, F., Štětina, J., Franěk, Z., Masarik, M.: A mathematical & stochatic modelling of the concasting of steel slabs. In: Proc. International Conference on Materials and Metallurgy, Hradec nad Moravicí 2009, pp. 41-48.
[24] Mauder, T., Novotný, J.: Two mathematical approaches for optimal control of the continuous slab casting process. In: Proc. 16th International Conference on Soft Computing MENDEL, Brno 2010, pp. 41-48.
[25] Rockafellar, R. T., Wets, R. J.-B.: Scenarios and policy aggregation in optimization under uncertainty. Math. Oper. Res. 16 (1991), 119-147. | DOI | MR
[26] Ruszczynski, A., Shapiro, A.: Stochastic Programming Models. Handbooks in Operations Research and Management Science, Volume 10: Stochastic Programming, Elsevier, Amsterdam 2003. | DOI | MR
[27] Shioura, A., Shakhlevich, N. V., Strusevich, V. A.: Application of submodular optimization to single machine scheduling with controllable processing times subject to release dates and deadlines. INFORMS J. Comput. 28 (2016), 148-161. | DOI | MR
[28] Stefanescu, D. M.: Science and Engineering of Casting Solidification. Second edition. Springer, New York 2009.
[29] Štětina, J., Klimeš, L., Mauder, T.: Minimization of surface defects by increasing the surface temperature during the straightening of a continuously cast slab. Mater. Tehnol. 47 (2013), 311-316.
[30] Ugail, H., Wilson, M. J.: Efficient shape parametrisation for automatic design optimisation using a partial differential equation formulation. Comput. Struct. 81 (2003), 2601-2609. | DOI
[31] Varaiya, P., Wets, R. J.-B.: Stochastic dynamic optimization approaches and computation. In: Proc. 13th International Symposium on Mathematical Programming, Tokio 1989, pp. 309-331. | DOI | MR
[32] Veliz, F. B., Watson, J. P., Weintraub, A., Wets, R. J.-B., Woodruff, D. L.: Stochastic optimization models in forest planning: a progressive hedging solution approach. Ann. Oper. Res. 232 (2015), 259-274. | DOI | MR
[33] Waanders, B. G. V., Carnes, B. R.: Optimization under adaptive error control for finite element based simulations. Comput. Mech. 47 (2011), 49-63. | DOI | MR
[34] Wets, R. J.-B.: The aggretation principle in scenario analysis and stochastic optimization. In: Algorithms and Model Formulations in Mathematical Programming (S. W. Wallace, ed.), Springer, Berlin 1989. | DOI | MR
[35] Yang, Z., Qui, H. L., Luo, X. W., Shen, D.: Simulating schedule optimization problem in steelmaking continuous casting process. Int. J. Simul. Model. 14 (2015), 710-718. | DOI
[36] Yang, J., Ji, Z. P., Liu, S., Jia, Q.: Multi-objective optimization based on pareto optimum in secondary cooling and EMS of continuous casting. In: Proc. International Conference on Advanced Robotics and Mechatronics (ICARM), Macau 2016, pp. 283-287. | DOI
[37] Žampachová, E., Popela, P., Mrázek, M.: Optimum beam design via stochastic programming. Kybernetika 46 (2010), 571-582. | MR
[38] Zarandi, M. H. F., Dorry, F., Moghadam, F. S.: Steelmaking-continuous casting scheduling problem with interval type 2 fuzzy random due dates. In: Proc. IEEE Conference on Norbert Wiener in the 21st Century (21CW), Boston 2014. | DOI
Cité par Sources :