An Integrated Approach of Fuzzy AHP-TOPSIS for Multi-Criteria Decision-Making in Industrial Robot Selection
Abstract
:1. Introduction
2. Materials and Methods
2.1. Fuzzy Numbers
2.2. Fuzzy AHP
2.3. Fuzzy TOPSIS
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Criteria | Alternative | DM 1 | DM 2 | DM 3 | DM 4 | DM 5 |
---|---|---|---|---|---|---|
MW | Robot 1 | P | P | P | VP | VP |
Robot 2 | VP | VP | VP | VP | VP | |
Robot 3 | P | P | P | P | P | |
Robot 4 | G | G | G | G | MG | |
Robot 5 | MG | MG | MG | G | MG | |
Robot 6 | F | F | F | MP | MP | |
Robot 7 | VG | VG | VG | G | VG | |
Robot 8 | MP | MP | P | MP | P |
Criteria | Alternative | DM 1 | DM 2 | DM 3 | DM 4 | DM 5 |
---|---|---|---|---|---|---|
VL | Robot 1 | VG | VG | G | VG | VG |
Robot 2 | F | F | MP | F | MP | |
Robot 3 | G | G | VG | G | G | |
Robot 4 | P | VP | VP | VP | P | |
Robot 5 | P | P | P | P | VP | |
Robot 6 | VP | VP | VP | VP | VP | |
Robot 7 | MP | MP | MP | P | P | |
Robot 8 | MG | MG | MG | G | MG |
Criteria | Alternative | DM 1 | DM 2 | DM 3 | DM 4 | DM 5 |
---|---|---|---|---|---|---|
PL | Robot 1 | MG | G | MG | MG | MG |
Robot 2 | P | P | P | P | VP | |
Robot 3 | VP | VP | P | P | P | |
Robot 4 | G | VG | G | VG | G | |
Robot 5 | VG | VG | VG | VG | G | |
Robot 6 | F | F | F | F | MP | |
Robot 7 | P | P | P | MP | P | |
Robot 8 | MP | MP | MP | MP | MP |
Criteria | Alternative | DM 1 | DM 2 | DM 3 | DM 4 | DM 5 |
---|---|---|---|---|---|---|
MR | Robot 1 | P | P | P | P | VP |
Robot 2 | VP | VP | VP | VP | VP | |
Robot 3 | P | P | P | P | P | |
Robot 4 | MP | MP | MP | MP | F | |
Robot 5 | G | G | G | MG | MG | |
Robot 6 | MG | MG | MG | MG | MG | |
Robot 7 | VG | VG | VG | VG | VG | |
Robot 8 | F | F | F | MG | F |
Criteria | Alternative | DM 1 | DM 2 | DM 3 | DM 4 | DM 5 |
---|---|---|---|---|---|---|
APC | Robot 1 | MG | G | G | MG | MG |
Robot 2 | VG | VG | VG | VG | VG | |
Robot 3 | G | VG | G | G | G | |
Robot 4 | P | P | P | P | P | |
Robot 5 | F | F | F | F | MP | |
Robot 6 | VP | VP | VP | VP | P | |
Robot 7 | MP | MP | MP | MP | F | |
Robot 8 | VP | VP | VP | P | P |
Criteria | Alternative | DM 1 | DM 2 | DM 3 | DM 4 | DM 5 |
---|---|---|---|---|---|---|
CO | Robot 1 | P | P | P | MP | MP |
Robot 2 | MG | G | G | MG | MG | |
Robot 3 | VP | VP | VP | VP | P | |
Robot 4 | F | F | MP | F | MP | |
Robot 5 | MP | MP | MP | MP | P | |
Robot 6 | VG | VG | VG | VG | VG | |
Robot 7 | G | VG | G | G | VG | |
Robot 8 | F | F | MG | MG | F |
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MCDM Method | Criteria for Evaluating Robots | Results | Reference |
---|---|---|---|
Entropy, TOPSIS | Mechanical Weight, Repeatability, Payload, Maximum Reach, Average Power Consumption. | The study determined that Robot-7 is the optimal selection for arc welding tasks. This robot has a mechanical weight of 501 kg, a repeatability of 0.15 mm, a load capacity of 6 kg, a maximum reach of 4368 mm, and a power consumption of 2.5 kW. | [60] |
BW, EDAS | Load Capacity, Repeatability, Velocity Ratio, Degree of Freedom. | The proposed method offers several advantages, including increased consistency and reduced computational requirements. | [61] |
EDAS | Purchase Cost, Load Capacity, Repeatability, Man–Machine Interface, Man–Machine Interface, Vendor’s Service Contract. | Compared with other MCDM methods (such as AHP, TOPSIS, VIKOR, ELECTRE, PROMETHEE, MOORA, WASPAS, GRA, ROV, and OCRA), the EDAS method is simpler and easier to apply in selecting industrial robots. | [62] |
TOPSIS-ARAS, COPRAS-ARAS | Load Capacity, Repeatability Error, Handling Coefficient, Velocity, Cost. | Based on the evaluation, Robot-12 achieved the highest rating and was identified as the optimal choice. This study validates the effectiveness of the hybrid models TOPSIS-ARAS and COPRAS-ARAS in enhancing the accuracy of rankings and delivering consistent and dependable results in the selection of industrial robots. | [63] |
SAW, TOPSIS, LINMAP, VIKOR, ELECTRE-III and NFM | Load Capacity, Repeatability Error, Cost, Vendor’s Service Quality, Programming Flexibility. | In case study 1, Robot 2 and Robot 3 emerged as the top choices for pick-and-place tasks. Case study 2 revealed that Robot 1 and Robot 3 received the highest ratings among the considered robots. In case study 3, Robot 2 was consistently identified as the optimal selection among the four robots considered by most MCDM methods. | [64] |
CRITIC, MABAC | Load Capacity, Memory Capacity, Manipulator Reach, Maximum Tip Speed, Repeatability. | Robot R3 attained the highest ranking, signifying its suitability for pick-and-place operations in flexible manufacturing systems. Among the evaluated robots, Robot R1 received the lowest ranking. The study also conducted a comparison of the ranking results with other MCDM methods to validate the accuracy and reliability of the proposed method. | [65] |
QFD, MPR | Payload Capacity, Workspace, Accuracy, Repeatability, Life Expectancy, Programmable Flexibility, Safety and Security, Purchase Cost, Maintenance Cost, Operation Cost. | The key criterion in the selection of an industrial robot is load capacity, and the most critical technical requirement is the drive system. | [66] |
COCOSO, TOPSIS, VIKOR, MOORA | Load Capacity, Repeatability, Maximum Tip Speed, Memory Capacity, Manipulator Reach. | According to the COCOSO method, R3 emerges as the best robot based on the MW, SD, and CRITIC weight distribution methods. However, R1 is considered the best robot according to the EM method, and R3 is favored according to the AHP method. | [67] |
SWARA, CoCoSo | Payload, Mechanical weight, Repeatability, Reach, Cost, Power Consumption. | The Fanuc P-350iA/45 robot has been selected as the most suitable robot for painting applications. These results have also been compared and cross-referenced with other popular MCDM methods such as TOPSIS, VIKOR, COPRAS, PROMETHEE, and MOORA, demonstrating a high degree of similarity in the ranking patterns among these methods, affirming the effectiveness of the SWARA-CoCoSo method. | [68] |
Rough-MABAC | Payload, Horizontal Reach, Vertical Reach, Repeatability, Weight, Power Rating, Cost, Flexibility, Safety, Welding Performance, Maintainability, Ease of Programming. | The research findings indicate that Robot A6 is the most suitable choice, ranking at the top of the list, followed by Robots A3, A13, A10, A5, A9, A4, A11, A1, A14, A7, A12, A8, and finally Robot A2. The robots are categorized into two main groups, efficient and inefficient, based on their positions in the approximate boundary regions. | [69] |
PIPRECIA-TOPSIS | Payload, Weight of Robot, Repeatability, Reach. | The PIPRECIA technique identifies payload as the most crucial criterion based on a predefined priority order, and the TOPSIS method recommends the FANUC 100iD/10L model as the best arc welding robot. | [70] |
BWM, G-BWM | Velocity, Repeatability, Load Capacity, Cost, Quality, Memory Capacity, Manipulator Reach. | The results indicate that Robot 2 is the best robot. The G-BWM (group best–worst method) demonstrates greater effectiveness compared to the G-AHP (Group Analytic Hierarchy Process) method due to its lower overall violation and deviation, as well as requiring fewer comparisons, resulting in reduced computational requirements. | [71] |
MCGDM-IP | Cost, Handling Coefficient, Load Capacity, Repeatability, Velocity. | Robot R11 achieved the highest ranking among the evaluated robots, while Robot R4 received the lowest ranking. The MCGDM-IP method improved the satisfaction level of the group by 2.12% compared to the simple additive weighting (SAW) method. | [72] |
CODAS, COPRAS, COCOSO, MABAC, VIKOR | Payload, Speed, Reach, Mechanical Weight, Repeatability, Cost, Power Consumption. | The results indicate that the HY1010A-143 robot is evaluated as the most suitable for painting applications according to four out of the five methods used. The KF121 robot is evaluated as the least suitable for painting applications by all of the MCDM methods. | [73] |
AHP | General Criteria, Structure/Architecture Criteria, Reliability Criteria, Application Criteria, Performance Criteria, Safety Criteria. | The AHP method is applied to evaluate the cobots based on the predefined criteria. The cobot with the highest overall priority weight (A1) is considered the most suitable based on the given criteria and AHP evaluation. | [74] |
WSM, WPM, WASPAS, MOORA, MULTIMOORA | Load Capacity, Maximum Tip Speed, Repeatability, Memory Capacity, Manipulator Reach. | The results indicate that among the applied MCDM methods, the MULTIMOORA (MOORA with Complete Multiplicative Form) method is the most robust and less affected by changes in the criteria weights. The robot ranking results show that the Cybotech V15 Electric Robot (R3) is often the best choice in most of the methods. | [75] |
COPRAS | Repeatability Error, Load Capacity, Maximum Tip Speed, Memory Capacity, Manipulator Reach. | The Cincinnati Milacrone T3-726 Robot (A2) achieved the highest ranking with a Qi value of 0.1946 and a Ui value of 100.00, securing first position. The COPRAS method has been demonstrated to be effective in the evaluation and selection of industrial robots, aligning well with the results from previous studies. | [76] |
AHP | Load Capacity, Reach, Weight, Repeatability, Power Consumption, Dexterity, Service | Based on the AHP method, the robot structure R2 is selected as the most optimal choice. | [77] |
GRA | Load Capacity, Repeatability Error, Velocity Ratio, Degrees of Freedom. | Robot R3 achieved the highest score with a grey relational grade of 0.9434 and was ranked first. | [78] |
AHP | Technical Criteria: Movement, Shaft Speed, Reach, Repeatability, Allowable Moment, Load: Robot Mass, Robot Reach, Vertical Reach, Horizontal reach Other Criteria: Capacity, Cost, Flexibility, Mounting Type, Welding Type. | Among the analyzed 15 industrial robots, the robot with code A4 achieved the highest weight of approximately 16%, followed by A5 with approximately 15%, and A2 and A9 both scoring ≈ 10%. Robot A4 excelled in criteria such as repeatability (C1.2), robot weight (C2.2), and power (C3.1), obtaining the highest score in these aspects. | [79] |
No. | Criteria | Units | Symbol |
---|---|---|---|
1 | Mechanical Weight | Kg | MW |
2 | Velocity | m/s | VL |
3 | Payload | Kg | PL |
4 | Maximum Reach | Mm | MR |
5 | Average Power Consumption | Kw | APC |
6 | Cost | $ | CO |
Alternative | MW | VL | PL | MR | APC | CO |
---|---|---|---|---|---|---|
Robot 1 | 145 | 1.33 | 12 | 1441 | 1.0 | 722 |
Robot 2 | 27 | 1.11 | 8 | 911 | 0.5 | 485 |
Robot 3 | 170 | 1.26 | 4 | 1500 | 0.6 | 965 |
Robot 4 | 272 | 0.65 | 20 | 1650 | 3.4 | 671 |
Robot 5 | 250 | 0.04 | 25 | 2409 | 2 | 690 |
Robot 6 | 230 | 0.25 | 10 | 1925 | 5.6 | 325 |
Robot 7 | 501 | 1.01 | 6 | 4368 | 2.5 | 400 |
Robot 8 | 215 | 1.21 | 8 | 1801 | 5.05 | 690 |
Linguistic Terms | Scale of Fuzzy Number | Units |
---|---|---|
Absolutely strong (AS) | (2, 2.5, 3) | |
Very strong (VS) | (1.5, 2, 2.5) | |
Fairly strong (FS) | (1, 1.5, 2) | |
Slightly strong (SS) | (1, 1, 1.5) | |
Equal (E) | (1, 1, 1) | |
Slightly weak (SW) | (2/3, 1, 1) | |
Fairly weak (FW) | (0.5, 2/3, 1) | |
Very weak (VW) | (0.4, 0.5, 2/3) | |
Absolutely weak (AW) | (1/3, 0.4, 0.5) |
Experts | Age | Education | Experience in the Field (Years) |
---|---|---|---|
Decision maker 1 (DM 1) | 58 | Associate Professor of Mechanical Engineering | >15 |
Decision maker 2 (DM 2) | 62 | Associate Professor of Robotics Engineering | >20 |
Decision maker 3 (DM 3) | 58 | Associate Professor of Manufacturing Processes | >25 |
Decision maker 4 (DM 4) | 65 | Professor of Management Science and Engineering Management | >20 |
Decision maker 5 (DM 5) | 66 | Professor of Mechatronics Engineering | >30 |
Criteria | DM 1 | DM 2 | DM 3 | DM 4 | DM 5 |
---|---|---|---|---|---|
MW | FS | FS | FS | VS | FS |
VL | AS | VS | VS | AS | VS |
PL | AS | AS | AS | AS | AS |
MR | VS | VS | VS | VS | AS |
APC | VW | VW | VW | FW | VW |
CO | FW | FW | FW | FW | FW |
Criteria | High Priority | Equal | Low Priority | Criteria | ||||||
---|---|---|---|---|---|---|---|---|---|---|
A | B |
Criteria | MW | VL | PL | MR | APC | CO |
---|---|---|---|---|---|---|
MW | (1, 1, 1) | (1, 15/14, 30/19) | (1, 15/13, 5/3) | (15/14, 30/19, 25/12) | (1, 8/5, 21/10) | (1, 11/10, 8/5) |
VL | (19/30, 14/15, 1) | (1, 1, 1) | (1, 11/10, 8/5) | (1, 6/5, 17/10) | (8/5, 21/10, 13/5) | (1, 8/5, 21/10) |
PL | (3/5, 13/15, 1) | (5/8, 10/11, 1) | (1, 1, 1) | (1, 15/14, 30/19) | (13/10, 9/5, 13/10) | (13/10, 9/5, 23/10) |
MR | (12/25, 19/30, 14/15) | (10/17, 5/6, 1) | (19/30, 14/15, 1) | (1, 1, 1) | (1, 7/5, 19/10) | (1, 13/10, 9/5) |
APC | (10/21, 5/8, 1) | (5/13, 10/21, 5/8) | (10/13, 5/9, 10/13) | (10/19, 5/7, 1) | (1, 1, 1) | (1, 7/5, 19/10) |
CO | (5/8, 10/11, 1) | (10/21, 5/8, 1) | (10/23, 5/9, 10/13) | (5/9, 10/13, 1) | (10/19, 5/7, 1) | (1, 1, 1) |
Value | |
---|---|
(0.132, 0.2, 0.33) | |
(0.131, 0.206, 0.3190) | |
(0.121, 0.192, 0.263) | |
(0.098, 0.16, 0.247) | |
(0.085, 0.121, 0.2) | |
(0.076, 0.121, 0.195) |
Linguistic Terms | Fuzzy Core |
---|---|
Very poor (VP) | (0, 0, 1) |
Poor (P) | (0, 1, 3) |
Medium poor (MP) | (1, 3, 5) |
Fair (F) | (3, 5, 7) |
Medium good (MG) | (5, 7, 9) |
Good (G) | (7, 9, 10) |
Very good (VG) | (9, 10, 10) |
Alternative | MW | VL | PL | MR | APC | CO |
---|---|---|---|---|---|---|
Robot 1 | (0, 0.6, 2.2) | (8.6, 9.8, 10) | (5.4, 7.4, 9.2) | (0, 0.8, 2.6) | (5.8, 7.8, 9.4) | (0, 0.6, 2.2) |
Robot 2 | (0, 0, 1) | (2.2, 4.2, 6.2) | (0, 0.8, 2.6) | (0, 0, 1) | (9, 10, 10) | (5.8, 7.8, 9.4) |
Robot 3 | (0, 1, 3) | (7.4, 9.2, 10) | (0, 0.6, 2.2) | (0, 1, 3) | (7.4, 9.2, 10) | (0, 0.2, 1.4) |
Robot 4 | (6.6, 8.6, 9.8) | (0, 0.4, 1.8) | (7.8, 9.4, 10) | (1.4, 3.4, 5.4) | (0, 1, 3) | (2.2, 4.2, 6.2) |
Robot 5 | (5.4, 7.4, 9.2) | (0, 0.8, 2.6) | (8.6, 9.8, 10) | (7.8, 9.4, 10) | (2.6, 4.6, 6.6) | (0.8, 2.6, 4.6) |
Robot 6 | (2.2, 4.2, 6.2) | (0, 0, 1) | (2.6, 4.6, 6.6) | (5, 7, 9) | (0, 0.2, 1.4) | (9, 10, 10) |
Robot 7 | (8.6, 9.8, 10) | (0.6, 2.2, 4.2) | (0.2, 1.4, 3.4) | (9, 10, 10) | (1.4, 3.4, 5.4) | (7.8, 9.4, 10) |
Robot 8 | (0.6, 2.2, 4.2) | (5.4, 7.4, 9.2) | (1, 3, 5) | (3.4, 5.4, 7.4) | (0, 0.4, 1.8) | (3.8, 5.8, 7.8) |
Alternative | Robot 1 | Robot 2 | Robot 3 | Robot 4 | Robot 5 | Robot 6 | Robot 7 | Robot 8 |
---|---|---|---|---|---|---|---|---|
0.2656 | 0.4608 | 0.3525 | 0.2307 | 0.1653 | 0.3168 | 0.2131 | 0.2673 | |
0.2803 | 0.0819 | 0.1953 | 0.3183 | 0.3835 | 0.2319 | 0.3301 | 0.2843 |
Alternative | Robot 1 | Robot 2 | Robot 3 | Robot 4 | Robot 5 | Robot 6 | Robot 7 | Robot 8 |
---|---|---|---|---|---|---|---|---|
0.5134 | 0.1508 | 0.3565 | 0.5798 | 0.6987 | 0.4227 | 0.6077 | 0.5154 | |
Rank | 5 | 8 | 7 | 3 | 1 | 6 | 2 | 4 |
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Tran, N.-T.; Trinh, V.-L.; Chung, C.-K. An Integrated Approach of Fuzzy AHP-TOPSIS for Multi-Criteria Decision-Making in Industrial Robot Selection. Processes 2024, 12, 1723. https://doi.org/10.3390/pr12081723
Tran N-T, Trinh V-L, Chung C-K. An Integrated Approach of Fuzzy AHP-TOPSIS for Multi-Criteria Decision-Making in Industrial Robot Selection. Processes. 2024; 12(8):1723. https://doi.org/10.3390/pr12081723
Chicago/Turabian StyleTran, Ngoc-Tien, Van-Long Trinh, and Chen-Kuei Chung. 2024. "An Integrated Approach of Fuzzy AHP-TOPSIS for Multi-Criteria Decision-Making in Industrial Robot Selection" Processes 12, no. 8: 1723. https://doi.org/10.3390/pr12081723