Evaluating location choices using Perceptual Computer approach

The international logistics centers choice problem is very important in International logistics. The location choice problem usually involves both numbers and words in which all of the criteria are weighted using words and the performance evaluations for all sub-criteria are either numbers or words. How to aggregate all of these data without losing information is a very daunting task using a type-1 fuzzy set (T1 FS) approach. This paper applies a new methodology - Perceptual Computer (Per-C)-to help solve this hierarchical multi-criteria decision making problem. Per-C has three components: encoder, computing with words (CWW) engine and decoder. First, the interval approach (IA) is used to obtain the interval type-2 fuzzy set (IT2 FS) word model for the words in a pre-specified vocabulary. Second, a linguistic weighted average (LWA) is used to aggregate all the data including numbers and words modeled by IT2 FSs. Finally, a centroid-based ranking method is used to rank the location choices, and a similarity measure is used to obtain similarities of the location choices. The decision-maker decides the winning location choice as the one with the highest ranking, smallest uncertainty band, and least similarity to other locations.