基于Orness测度的多阶段不确定语言信息优化集结

研究不确定情境下多阶段语言信息的集结问题. 建立了多阶段不确定语言信息集结的TOPSIS分析框架, 以贴近度思想表征各方案单阶段绩效; 基于决策矩阵信息和Orness测度约束, 建立以相邻阶段方案的综合贴近度离差和最小为优化目标的阶段权重确定模型, 考虑了主观偏好和方案决策信息对阶段权重的综合影响; 设计各方案贴近度范围分布估算模型, 解决多阶段决策过程中的决策风险问题; 建立了Orness测度的参数灵敏度分析模型, 探讨不同取值范围对方案多阶段优选排序的影响. 算例说明了方法的应用步骤和可行性.

Aggregation of multi-stage uncertain linguistic evaluation information based on Orness

A flexible methodology is proposed to determine the aggregation of multi-stage linguistic information in an uncertain situation. More specifically, the frame of TOPSIS is designed to investigate the aggregation principle of multi-stage uncertain linguistic information, in which scheme performance in single stage can be calculated from a nearness perspective. Additionally, based on the decision matrix and Orness, a program is constructed to obtain the proper stage weights by minimizing the sum of deviation of the project nearness between adjacent stages in multi-periods, which concerns the comprehensive influence of both subjective preference and all the evaluation information. Furthermore, a model is constructed to evaluate the range of project nearness and study the risk problems in the process of multi-stage decision-making. Besides, another model is developed to examine the sensitivity of Orness parameter on the selection and ranking in multi-stage decision. Finally, a numerical case is conducted to show the above operations, which can demonstrate the feasibility and effectiveness of the new models and methods.