7. Linguistic MADM with Unknown Weight Information

Excerpt

The complexity and uncertainty of objective things and the fuzziness of human thought result in decision making with linguistic information in many real life situations. For example, when evaluating the “comfort” or “design” of a car, linguistic labels like “good”, “fair”, “poor” are usually used, and evaluating a car’s speed, linguistic labels like “very fast”, “fast”, “slow” can be used. Therefore, the investigation on the MADM problems in which the evaluation information on alternatives is expressed in linguistic labels is an interesting and important research topic, which has achieved fruitful research results in recent years. In this chapter, we introduce some linguistic information aggregation operators, such as the generalized induced ordered weighted averaging (GIOWA) operator, the extended ordered weighted averaging (EOWA) operator, the extended weighted averaging (EWA) operator, and linguistic hybrid aggregation (LHA) operator, etc. Based on these aggregation operators, we also introduce some methods for solving the MADM problems in which the weight information on attributes is unknown completely, and the attribute values are expressed in linguistic labels, and illustrate them with some practical examples. …