Development of metamodel-based robust simulation optimization for complex systems under uncertainty

Computer simulations can help a rapid investigation of various alternative designs to decrease the required time to improve the system. Because of the complexity for analyzing complex systems in way of mathematical formulation, a simulation optimization has been an interest in analyzing and studying the behavior of complex systems in the real world of engineering problems. One of the main difficulties of existing model–based simulation optimization methods is dealing with large number of required simulation evaluation (also called simulation experiments or computer experiments) which causes of costly computational time. In addition, in order to improve the validity of optimal results, uncertainty as a source of variability in the model’s output(s) need to be considered while this importance mostly has been ignored in designing of existing simulation optimization models. Under uncertainty, simulation running with stochastic output is complex in terms of computational time and/or cost, therefore the limited number of simulations is desirable. However, the accuracy of simulation result strongly depends on the reality of computer coding and discrepancy between simulation model and actual physical system. Most existing simulation optimization methods need to be improved in such a way to handle conflicting of multiple responses and constraints. This research generally aims to develop the black-box simulation optimization technique to be applicable in stochastic complex systems under effect of uncertainty with the least optimization computational burden (number of simulation experiments). This research develops a new distribution-free method for uncertainty management with unknown distribution of uncertainty. This research also aims to show the applicability and validity of proposed metamodel-based robust simulation optimization method in practical engineering design problems such as direct speed control of DC motor and PID tuning under uncertainty. For this purpose, metamodeling techniques are used for global approximation of complex simulation model. The statistical terminology of Taguchi crossed array design is replaced by global modern metamodels. A distribution-free method is suggested to tackle the lack of information about possible probability distribution of uncertainty scenarios in the model. Results of this research confirmed the validity and applicability of the proposed methodology dealing with practical stochastic complex engineering design problems in three terms; reducing computational time, enhancing flexibility, and improving the applicability. The proposed method can reduce the number of function evaluations for PID tuning under uncertainty to 50 simulation runs compared to more than 1000 function evaluations in common model based method. Compared to classical Ziegler Nichols method, the proposed method shows the better performance which is more than 10% for PID tuning under uncertainty. The proposed distribution–free method applied in economic order quantity problem shows the same accuracy compared to studies in literature whereby this study does not need to estimate distribution of uncertainty.

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