Using Computing with Words for Selecting Projects in Field of Fuel Consumption Reduction

Background/Objectives: Nowadays, most organizations acknowledge the role and significance of different energy resources in the supply of present and future needs. As a result, they make sizeable investments and undertake extensive research on strategy and policy making as well as definition of infrastructure projects to reduce energy consumption. According to the diversity of projects and their cost of implementation, ranking the projects with different criteria is currently one of the complex problems in organizations. This paper presents a new solution based on the specific architecture of Perceptual Computer (Per-C) for project selection. Methods/Statistical Analysis: In project selection problem, decision makers express their assessment in the form of the linguistic terms that they are vague and uncertain. Using Per-C method is a new experience in project selection problems that has not been mentioned in any literature so far. The Per-C has three components: an Encoder, which maps words into IT2 FS models; a CWW engine, which operates on the input words and whose outputs are FOU(s); and a Decoder, which maps these FOU(s) into a recommendation. We used the Interval Approach (IA) for Decoding, Linguistic Weighted Average (LWA) method is used in CWW engine and Centroid method is used for Decoding. Findings and Application/Improvements: A real case study from a power plant is used to illustrate the applicability of the approach. Using Per-C method is a new experience in project selection problems that has not been mentioned in any literature so far. The results show the applicability of the proposed methodology clearly.