Page 55 - POSTER FYP MAC-OGOS 2025

NURUL FATIN INSYIRAH WAN NURUL HUSNA

BINTI MOHD SAIFULLIZAM BINTI WAN NORDIN K242/56

SELECTION OF ROBOT USING FUZZY AHP

ABSTRACT

Selecting the most suitable robot for industrial application is a complex multi-criteria decision-making (MCDM) problem that involves qualitative and

quantitative elements. This research utilized Fuzzy Analytic Hierarchy Process (FAHP) to handle uncertain ties and subjectivity in decision-making. This

method efficiently manages imprecise judgments and emphasizes key selection criteria by integrating fuzzy logic with the traditional AHP framework. In this

research, the criteria and alternatives for the robot selection were adopted from previous research. This research develops a hierarchical model, gathers

committee evaluations, and utilizes FAHP to calculate the weight of criteria and rank alternatives. It followed a structured nine-step procedure that includes

fuzzy pairwise comparisons, geometric mean computation, relative fuzzy weight derivation, defuzzification, and normalization. A case study illustrates how

the proposed method is applicable in real-world industrial situations. FAHP improves the accuracy of decisions by reducing human bias and uncertainty. The

final rankings provide a clear overview of the best choice of the robot based on the chosen criteria. This method helps industries make informed choices,

enhance performance, and lower operational risks.

PROBLEM STATEMENT OBJECTIVES

Traditional multi-criteria decision-making (MCDM) methods that To apply Fuzzy Analytical Hierarchy Process (FAHP) in the selection of

including AHP are often used to solve this problem but it often lead to robot.

inconsistent results. To identify the weight for each criteria.

No uniform methodologies to integrate fuzzy logic with AHP methodology To rank the best criteria and robot using Fuzzy AHP

specifically to the field of robotics.

METHODOLOGY & IMPLEMENTATION

Adoption of data Pairwise Check Calculation Calculation of Calculation Deffuzification

from previous comparisons Consistency of average geometric of Fuzzy and Ranking

research Ratio mean Weight Normalization

RESULTS & DISCUSSION

SUMMARY OF WEIGHT AND NORMALIZATION OF ALTERNATIVES

These values are summed and arranged in

descending order based on their total scores

to determine the ranking of the best robot.

The most important criterion was the Man

machine interface (C1), with the highest

weight of 0.5529.

FINAL RANKING OF ALTERNATIVES

Robot 2 (R2) ranked as the best robot, with the

highest score of 0.3784, followed by Robot 3

(R3) and Robot 1 (R1).

Previous research identified Robot 1 (R1) as

the best alternative. This difference

emphasizes how methodological variations in

Fuzzy TOPSIS and Fuzzy AHP can affect

decisions.

CONCLUSION RECOMMENDATION

The FAHP method was used successfully to solve the problem of robot Involve multiple decision-makers to reduce bias and increase reliability.

selection. Man-machine interface (C1) is identified as the most important

criteria and Vendor’s Service Contract (C2) is the least important. The Collects expert input from multiple stakeholders such as operations

most preferred robot according to the implementation of the FAHP managers and engineers.

method are Robot 2 (R2). In summary, FAHP successfully determine the

relative importance of criteria and ranking all the robot alternatives Use primary data from domain-specific sectors such as healthcare or

based on several criteria. All research objectives were achieved. logistics robots to enhance applicability.