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An Artificial Neural Network Meta-Model for Solving Semi Expensive Simulation Optimization Problems

Behbahani, Mohammad | 2018

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 51469 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Akhavan Niaki, Taghi
  7. Abstract:
  8. 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 the field of decision making, still very simplified and unrealistic assumptions are involved in many. Simulation is one of the most powerful tools to deal with this kind of problems and enjoys being free of any restricting assumptions which may generally be considered in a stochastic system. In addition, simulation optimization techniques are categorized into two broad classes of model-based and metamodel-based methods. In the first class, simulation and optimization component interact with each other causing an increase in simulation times and costs. To cope with this problem, a third component defined as metamodel that estimates the relationships between the inputs and outputs of the system being simulated, comes to the picture in the second class problems. Besides, optimization of semi-expensive simulation optimization problems needs a numerous simulation run in model-based methods. However, as the validation cost increases at a rapid rate in each iteration of the metamodel-based methods, a new metamodel-based method for solving semi-expensive simulation optimization problems has been introduced in the literature which consists of two phases and solve the problem in a less computational time. In the first phase, as a model-based algorithm, the simulation output is used directly in the optimization stage. In the second phase, the simulation model is changed with a validated metamodel. In this thesis, an artificial neural network is employed as the metamodel and its performance is compared with the original algorithm that uses a Kriging metamodel
  9. Keywords:
  10. Simulation Optimization ; Metamodel-Based Algorithm ; Artificial Neural Network ; Semi-Expensive Simulation

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