Investigations on Benefits Generated By Using Fuzzy Numbers in A TOPSIS Model Developed For Automated Guided Vehicle Selection Problem

Abstract

Selection of the appropriate automated guided vehicle (AGV) for a manufacturing company is a very important but at the same time a complex problem because of the availability of wide-ranging alternatives and similarities among AGVs. Although, the available studies in the literature developed various fuzzy models, they do not propose any approaches to measure the benefits generated by incorporating fuzziness in their selection models. This paper aims to fill this gap by trying to quantify the level of benefit provided by employing the fuzzy numbers in the multi attribute decision making (MADM) models. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used as the MADM approach to rank the AGV in this paper. In the paper, by increasing the fuzziness level steadily in the fuzzy numbers, the obtained AGV rankings are compared with the ranking obtained with the crisp values. The statistical significance of the differences between the ranks is calculated using Spearman’s rank-correlation coefficient. It can be observed from the results that as the vagueness and imprecision increases, fuzzy numbers instead of crisp numbers should be used. On the other hand, in situations where there is a low level of fuzziness or the average value of the fuzzy number can be guessed, using crisp numbers will be more than adequate.