关键词: 语言随机多准则决策, 不完全信息, 云模型, 理想方案, 相对满意度, 城市公交线网优化

Abstract: To solve the linguistic-stochastic multi-criterion decision-making problems with incomplete information, a solving method based on one-dimension normal clouds and relative satisfaction degrees was designed. In the method, uncertain linguistic evaluation labels of the alternatives under criteria were transformed into approximate one-dimension normal clouds according to the normal distribution law and the golden section ratio method. By the numerical characteristics variation method, the clouds distances between alternatives and ideal alternative(positive or negative)were measured under the criteria. Through the operation law of interval numbers, the weighted distances between alternatives and ideal alternative(positive or negative)were calculated. The relative satisfaction degrees of the alternatives were gotten, and the ranking order of the alternatives was defined. The urban public transportation network optimization was taken as an example to show the validity and the feasibility of proposed method.

Key words: linguistic-stochastic multi-criterion decision-making, incomplete information, cloud model, ideal alternative, relative satisfaction degree, urban public transportation network optimization