Development of fuzzy based intelligent decision model to optimize the blind spots in heavy transport vehicles

Abstract

Statistics reveals that the visual problems are the prime reasons for a larger number of road accidents. The blind spot is the major problem related to vision. The aim of this study is to develop a fuzzy-based multi criteria decision-making model for optimizing the area of the blind spot in the front and sides of a heavy transport vehicle. To achieve this, the statistical tool ANOVA (Analysis of Variance) and multi criteria optimization techniques like TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), FAHP (Fuzzy Analytical Hierarchy Process) and GRA (Grey Relational Analysis) were also used in this problem This paper consists of three modules: first, the blind spots of the existing body structure dimension used in heavy vehicles were studied and the optimal design parameters were determined by using ANOVA and TOPSIS methodologies; next, the weights of the design parameters were calculated using FAHP method. Finally, GRA-based Multi Criteria Decision Making (MCDM) approach has been used to rank the vehicle body structures. The proposed model has been implemented in a transport corporation to compare four different types of body structures and concluded that the body structure which was built by an outsourced body builder is having a smaller area of blind spot and optimal design parameters as well.