Fuzzy Optimum Model of Semi-Structural Decision for Lectotype Optimization of Offshore Platforms

In the process of concept design of offshore platforms, it is necessary to select the best from feasible alternatives through comparison and filter. The criterion set, used to evaluate and select the satisfying alternative, consists of many qualitative and quantitative factors. Therefore, the selection is a problem of multicriteria and semi- structural decision- making. Different from traditional methods in semi- structural decision- making, a new framework and methodology is presented in this paper for evaluation of offshore platform alternatives. First, the criterion set is established for the evaluation of alternatives. Next, the approach is studied to construct the relative membership degree matrix, in which both qualitative and quantitative factors are consistent with the uniform calculating standard. And then a new weight-assessing method is developed for calculation of the weights based on the relative membership degree matrix. Finally, a multi-hierarchy fuzzy optimum model is adopted to select t     

复杂系统模糊模式识别动态规划模型研究

将模糊识别集合算子和非结构决策分析方法引入模糊动态规划中,推导出了两种求解复杂系统多目标多阶段决策问题的方法:级别特征值最小法和阶段模糊识别递推法。这两种方法具有不同的递推方程,可以应用于不同的情况,从而形成了模糊模式识别动态规划模型。此模型可以有效地获取和集成决策者的经验知识和偏好。此模型已应用到实践中,并取得良好效果。     

Group Decision Making Based Fuzzy Pattern Recognition Model for Lectotype Optimization of Offshore Platforms 1

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Group Decision Making-Based Fuzzy Pattern Recognition Model for Lectotype Optimization of Offshore Platforms

This paper develops a fuzzy pattern recognition model for group decision making to solve the problem of lectotype optimization of offshore platforms. The lack of data and the inexact or incomplete information for criteria are the main cause of uncertainty in the evaluation process, therefore it is necessary to integrate the judgments from different decision makers with different experience, knowledge and preference. This paper first uses a complementary principle-based pairwise comparison method to obtain the subjective weight of the criteria from each decision maker. A fuzzy pattern recognition model is then developed to integrate the judgments from all the decision makers and the information from the criteria, under the supervision of the subjective weights. Finally a case study is given to show the efficiency and robustness of the proposed model.