Multi-criteria Decision-making Methods Based On Duplex Linguistic Information

Linguistic multi-criteria decision-making (MCDM) problem is an important research topic in the nowadays MCDM theory. It has widely application background in many fields. Although the linguistic MCDM research has achieved fruitful results, the existing methods still have many unsolved problems in both theory and application aspects. Especially, they are very immature in handling the uncertainty in linguistic information. Thereby, the duplex linguistic (DL) sets are introduced in the thesis. Moreover, the uncertain MCDM problems under duplex linguistic environment are studied systematically. It includes:(1) Defined the DL set, which can be used to express the evaluation for an alternative with respect to a criterion and the confidence on such evaluation simultaneously. Comparing to the classical linguistic variable, the DL set can comprise the uncertainty from different sources.(2) Defined the dominance relation between DL sets, and established the outranking relation between alternatives based on such dominances. Integrating the outranking relations after pairwise comparing the alternatives, the partial order of alternatives is reached.(3) Based on the outranking relation above, a DL multi-criteria classifying and rating method is proposed. By comparing the alternatives to a group of virtual reference alternatives, the evaluated alternatives are classified into the proper groups. This method is suitable for solving the problem that involves too many alternatives to compare them pairwise. An urban tree species selection was conducted by using this method.(4) The semantic dominance (SD) technique is proposed. The SD rules about five typical semantics structures were proved, and the properties of SD were studied. The DL MCDM procedure based on SD was introduced to find all the "non-inferior" alternatives.(5) For obtaining the preferences about the alternatives as much as possible, and avoiding assigning the semantics to the linguistic variables artificially, a semantic programming model was introduced to set the semantics to the linguistic variables. The incomplete preference involving in the linguistic variables are expressed by the constraints of the model. Two DL MCDM methods based on semantic programming are introduced.(6) For handling the DL MCDM problems with incomplete weights, two methods based on the computing with expanded linguistic variables are proposed. One of them regards the decision as a game between decision makers and the nature, and set the weights with a matrix game. The other method sets the weights to maximize the deviation of the model.(7) The intuitionistic normal cloud model is proposed for solving the DL MCDM problems. The decision information is regarded as the drop sets of the clouds that evaluate the alternatives. Parameters of these clouds then can be estimated from such drops. Further, the drops of these clouds are generated by using the cloud-generating algorithm. The statistical results of the drop score can be used to rank the clouds.(8) The methods proposed are used to select the alternative fuel bus species. These methods reveal more detail of the preference that involves in the DL decision information. They help the decision maker to understand the problem and the factors that affect the preferences, thus make decision more reasonable.