Building Optimum Reliable Systems through Value-at-Risk Criterion-based Two Stage Fuzzy Random Optimization Method

点击翻译按钮获取中文摘要

In this research, to deal with complicated decision making problems from a risk framework under hybrid uncertainty consisting randomness and fuzziness, Value-at-Risk (VaR) measure-based fuzzy random optimization models are defined. In building the models, synthesized heuristic algorithms are proposed and particle swarm optimization algorithm is used in VaR simulation to solve the models. The proposed models enables us to treat risk perspective of 2 stage optimization problems and solve the problems under hybrid uncertain environment, which are hard to solve till now. Furthermore, such proposed algorithms are capable of approximate computation of VaR criterion fuzzy random coefficients. Such proposed models are uniquely built with wide applications as two stage VaR models which design and solve real world problems.