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.
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