An artificial neural network metamodel to solve semi-expensive simulation optimization problems: A comparative study
Mohammad Behbahani
1
(
Sharif University of Technology, Department of Industrial Engineering
)
S.T. A. Niaki
2
(
Department of Industrial Engineering, Sharif University of Technology
)
Mehran Moazeni
3
(
Sharif University of Technology, Department of Industrial Engineering
)
کلید واژه: Semi-expensive simulation problems, Simulation optimization, Metamodel-based algorithm, Artificial neural network,
چکیده مقاله :
Although a considerable number of problems whose analysis depends on a set of complex mathematical relations exist in the literature due to recent developments in decision-making, still very simplified and unrealistic assumptions are involved in many. Simulation is one of the most powerful tools for dealing with this kind of problem, and it enjoys being free of any restricting assumptions that may generally be considered in a stochastic system. Besides, simulation optimization techniques are categorized into two broad classes: model-based and meta-model-based methods. In the first class, simulation, and optimization components interact, causing an increase in simulation times and costs. To cope with this problem, a third component, defined as a metamodel that estimates the relationships between the inputs and outputs of the system being simulated, comes to the picture in the second-class problems. Furthermore, optimizing semi-expensive simulation optimization problems requires numerous simulation runs in model-based methods. However, as the validation cost increases rapidly in each iteration of the metamodel-based techniques, a new method consisting of two phases has been introduced in the literature to solve semi-expensive simulation optimization problems in less computational time. In the first phase, similar to a model-based algorithm, the simulation output is used directly in the optimization stage. The simulation model is changed to a validated metamodel in the second phase. In this paper, an artificial neural network is employed as the metamodel to compare its performance to the ones of the original algorithm that uses a Kriging metamodel in five widespread test problems as well as an (s, S) inventory problem.
چکیده انگلیسی :
Although a considerable number of problems whose analysis depends on a set of complex mathematical relations exist in the literature due to recent developments in decision-making, still very simplified and unrealistic assumptions are involved in many. Simulation is one of the most powerful tools for dealing with this kind of problem, and it enjoys being free of any restricting assumptions that may generally be considered in a stochastic system. Besides, simulation optimization techniques are categorized into two broad classes: model-based and meta-model-based methods. In the first class, simulation, and optimization components interact, causing an increase in simulation times and costs. To cope with this problem, a third component, defined as a metamodel that estimates the relationships between the inputs and outputs of the system being simulated, comes to the picture in the second-class problems. Furthermore, optimizing semi-expensive simulation optimization problems requires numerous simulation runs in model-based methods. However, as the validation cost increases rapidly in each iteration of the metamodel-based techniques, a new method consisting of two phases has been introduced in the literature to solve semi-expensive simulation optimization problems in less computational time. In the first phase, similar to a model-based algorithm, the simulation output is used directly in the optimization stage. The simulation model is changed to a validated metamodel in the second phase. In this paper, an artificial neural network is employed as the metamodel to compare its performance to the ones of the original algorithm that uses a Kriging metamodel in five widespread test problems as well as an (s, S) inventory problem.
Ahmed, M. A., Alkhamis, T. M. (2009). "Simulation optimization for an emergency department healthcare unit in Kuwait," European Journal of Operational Research, 198 (3): 936-942.
Amouzgar, K., Bandaru, S., & Ng, A. H. (2018). "Radial basis functions with a priori bias as surrogate models: A comparative study, " Engineering Applications of Artificial Intelligence, 71, 28-44.
Backlund, P. B., Shahan, D. W., & Seepersad, C. C. (2012). "A comparative study of the scalability of alternative metamodelling techniques," Engineering Optimization, 44(7), 767-786.
Barton, R. R., Meckesheimer, M. (2006). "Metamodel-based simulation optimization," Handbooks in Operations Research and Management Science, 13: 535-574.
Beşikçi, E. B., Arslan, O., Turan, O., Ölçer, A. (2016). "An artificial neural network based decision support system for energy efficient ship operations," Computers & Operations Research, 66: 393-401.
Biles, W. E., Kleijnen, J. P., Van Beers, W. C., Van Nieuwenhuyse, I. (2007). "Kriging metamodeling in constrained simulation optimization: an explorative study," in 2007 Winter Simulation Conference, pp. 355-362: IEEE.
Cheng, K., Lu, Z., Ling, C., & Zhou, S. (2020). "Surrogate-assisted global sensitivity analysis: an overview. Structural and Multidisciplinary Optimization," 1-27.
Dengiz, B., Alabas-Uslu, C., Dengiz, O. (2009). "A tabu search algorithm for the training of neural networks," Journal of the Operational Research Society, 60 (2): 282-291.
Díaz-Manríquez, A., Toscano, G., & Coello, C. A. C. (2017). "Comparison of metamodeling techniques in evolutionary algorithms," Soft Computing, 21(19), 5647-5663.
Jones, D. R., Schonlau, M., Welch, W. J. (1998). "Efficient global optimization of expensive black-box functions," Journal of Global Optimization, 13(4): 455-492.
Moghaddam, S., Mahlooji, H. (2017). "A new metamodel-based method for solving semi-expensive simulation optimization problems," Communications in Statistics-Simulation and Computation, 46 (6): 4795-4811.
Mohammad Nezhad, A., Mahlooji, H. (2014). "An artificial neural network metamodel for constrained simulation optimization," Journal of the Operational Research Society, 65 (8): 1232-1244.
Molga, M., Smutnicki, C. (2005). "Test functions for optimization needs," https://www.robertmarks.org/Classes/ENGR5358/Papers/functions.pdf
Nguyen, A.-T., Reiter, S., Rigo, P. (2014). "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, 113: 1043-1058.
Østergård, T., Jensen, R. L., Maagaard, S. E. (2018). "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, 211: 89-103.
Rigoni, E., Lovison, A. (2007). "Automatic sizing of neural networks for function approximation," in 2007 IEEE International Conference on Systems, Man and Cybernetics, pp. 2005-2010: IEEE.
Roy, A., & Chakraborty, S. (2020). "Support Vector Regression Based Metamodel by Sequential Adaptive Sampling for Reliability Analysis of Structures," Reliability Engineering & System Safety, 106948.
Shan, S., Wang, G. G. (2010). "Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions," Structural and Multidisciplinary Optimization, 41 (2): 219-241.
Shi, L., Ólafsson, S. (2000). "Nested partitions method for global optimization," Operations Research, 48 (3): 390-407.
Tsai, S. C., Fu, S. Y. (2014). "Genetic-algorithm-based simulation optimization considering a single stochastic constraint," European Journal of Operational Research, 236 (1): 113-125.
Vicario, G., Craparotta, G., Pistone, G. (2016). "Metamodels in computer experiments: Kriging versus artificial neural networks," Quality and Reliability Engineering International, 32 (6): 2055-2065.
Wang, L. (2005). "A hybrid genetic algorithm–neural network strategy for simulation optimization," Applied Mathematics and Computation, 170 (2): 1329-1343.
Wang, G. G., Shan, S. (2007). "Review of metamodeling techniques in support of engineering design optimization," Journal of Mechanical Design, 129 (4): 370-380.
Wang, X., Wang, G. G., Song, B., Wang, P., Wang, Y. (2019). "A Novel evolutionary sampling assisted optimization method for high dimensional expensive problems," IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2019.2890818.
Willmes, L., Back, T., Jin, Y., Sendhoff, B. (2003). "Comparing neural networks and kriging for fitness approximation in evolutionary optimization," in the 2003 Congress on Evolutionary Computation, CEC'03., vol. 1, pp. 663-670: IEEE.
Xu, J., Huang, E., Chen, C.-H., Lee, L. H. (2015). "Simulation optimization: A review and exploration in the new era of cloud computing and big data," Asia-Pacific Journal of Operational Research, 32 (3): 1550019.
Yuan, R., Guangchen, B. (2009). "Comparison of neural network and Kriging method for creating simulation-optimization metamodels," in 2009 eighth IEEE international conference on dependable, autonomic and secure computing, 2009, pp. 815-821: IEEE.
Zhao, D., & Xue, D. (2010). "A comparative study of metamodeling methods considering sample quality merits," Structural and Multidisciplinary Optimization, 42(6), 923-938.
