Resolving Indeterminacy Approach to Solve Multi-Criteria Zero-Sum Matrix Games with Intuitionistic Fuzzy Goals
Abstract
:1. Introduction
1.1. Background
1.2. Literature Review
1.3. The Contribution and Structure of This article
2. Preliminaries
- (1)
- (2)
- (3)
- (4)
- if and only ifand
- (1)
- (2)
- (3)
3. Zero-Sum Multi-Criteria Matrix Games
4. Application of IFS to Optimization Problem
4.1. Decision Making Approaches with I-Fuzzy Environment
4.2. Interpretation of I-Fuzzy Inequalities
5. Mathematical Model of Multi-Criteria Zero-Sum Matrix Games with I-Fuzzy Goals
6. Resolving Indeterminacy Approach
6.1. Optimistic Approach
6.1.1. Optimization Model for Player I
6.1.2. Optimization Model for Player II
6.2. Pessimistic Approach
6.2.1. Optimization Model for Player I
6.2.2. Optimization Model for Player II
7. Numerical Examples and Computational Result Comparison
7.1. Example 1
7.1.1. Optimistic Approach
7.1.2. Pessimistic Approach
7.2. Example 2
7.2.1. Optimistic Approach
7.2.2. Pessimistic Approach
7.3. Results and Discussion
- The proposed approach is based on IFS, and it is evident that IFS suitably reflects the hesitation and uncertainty of human thinking; so, it provides more flexibility to the decision makers while expressing their decision.
- By comparing our results, as in Table 5, Table 6, Table 7 and Table 8, with Nishizaki et al. [10] and Aggarwal et al. [41], as in Table 9, Table 10 and Table 11, it can be easily seen that our proposed model produces much better optimal strategies with higher securities as, for player I, in our model, as opposed to and in case of Nishizaki’s and Aggarwal’s, respectively. Additionally, for player II, in our model, but and in Nishizaki and Aggarwal models, respectively.
8. Conclusions and Future Work
- Outlining the arithmetic operations and indeterminacy resolving functions of Atanassov’s I-fuzzy number.
- Proposing an effective algorithm based on the indeterminacy resolving function, I-fuzzy inequality relations, and Inuiguchi et al [42] algorithm.
- Constructing crisp models from the proposed I-fuzzy models.
- Solving the reduced crisp multi-objective linear programming models using the GAMS software [43].
- Conducting two numerical simulations to evaluate the applicability and effectiveness of the proposed approach.
- The numerical results confirm that the IFS outperform fuzzy set when studying uncertainty in game theory.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
- Step 1:
- Suppose the break points are .Compute , and and give them as , and , respectively, for . Then, , and , for .
- Step 2:
- Let and then obtain the value of .
- Step 3:
- Calculate and
- Step 4:
- For , and m = 1,2, find
Appendix B
- Step 1:
- Suppose the break points areCompute , and and give them as , and , respectively, for . Then, , and , for
- Step 2:
- Let and then obtain the value of
- Step 3:
- Calculate and
- Step 4:
- For , and m = 1,2, find
Appendix C
- Step 1:
- We have
- Step 2:
- Set and compute
- Step 3:
- Normalizing we obtain
- Step 4:
- Then for , and m = 1,2
Appendix D
- Step 1:
- We have
- Step 2:
- Set and compute
- Step 3:
- Normalizing we obtain
- Step 4:
- Then for , and m = 1,2
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# | ||||
---|---|---|---|---|
1 | 0.7917 | 0.2083 | 0.6743 | 1 |
2 | 0.8147 | 0.1803 | 0.6626 | 1.0249 |
3 | 0.925 | 0.1443 | 0.6408 | 1.041 |
4 | 0.983 | 0.1163 | 0.6259 | 1.0695 |
# | ||||
---|---|---|---|---|
1 | 0.36 | 0.64 | 1 | 0.79 |
2 | 0.374 | 0.626 | 0.9883 | 0.811 |
3 | 0.388 | 0.612 | 0.976 | 0.832 |
4 | 0.402 | 0.598 | 0.965 | 0.853 |
# | ||||
---|---|---|---|---|
1 | 0.8316 | 0.3291 | 0.5529 | 1 |
2 | 0.941 | 0.3011 | 0.541 | 1.0359 |
3 | 0.962 | 0.2621 | 0.5157 | 1.0526 |
4 | 0.991 | 0.2331 | 0.4874 | 1.0955 |
# | ||||
---|---|---|---|---|
1 | 0.472 | 0.556 | 0.9447 | 0.9354 |
2 | 0.458 | 0.542 | 0.9355 | 0.9515 |
3 | 0.472 | 0.528 | 0.9263 | 0.9676 |
4 | 0.486 | 0.514 | 0.9171 | 0.9838 |
# | ||||||
---|---|---|---|---|---|---|
1 | 0.5724637 | 0.2463768 | 0.1811594 | 0.4331723 | 0.4161490 | 0.7741020 |
2 | 0.875 | 0.125 | 0.00058216 | 0.5138888 | 0.3125 | 0.7608695 |
3 | 0.8098214 | 0.125 | 0.0651785 | 0.4994047 | 0.3404336 | 0.7580357 |
4 | 0.7446428 | 0.125 | 0.1303571 | 0.4849206 | 0.3683673 | 0.7552018 |
5 | 0.6794642 | 0.125 | 0.1955357 | 0.4704365 | 0.396301 | 0.752368 |
6 | 0.6142857 | 0.125 | 0.2607142 | 0.4559523 | 0.4242346 | 0.749534 |
# | ||||||
---|---|---|---|---|---|---|
1 | 0.625 | 0.0000683 | 0.375 | 0.7410714 | 0.4910714 | 0.44375 |
2 | 0.6287389 | 0.0186948 | 0.3525662 | 0.7354629 | 0.50122 | 0.44375 |
3 | 0.6366255 | 0.0581276 | 0.3052468 | 0.7236331 | 0.5226264 | 0.44375 |
4 | 0.6445121 | 0.0975604 | 0.2579274 | 0.7118032 | 0.5440328 | 0.44375 |
5 | 0.6523986 | 0.1369933 | 0.210608 | 0.6999734 | 0.5654392 | 0.44375 |
6 | 0.6602852 | 0.1764261 | 0.1632886 | 0.6881435 | 0.5868456 | 0.44375 |
# | ||||||
---|---|---|---|---|---|---|
1 | 0.875 | 0.125 | 0.0000416 | 0.7265625 | 0.065625 | 0.6634615 |
2 | 0.8132227 | 0.125 | 0.0617772 | 0.7188403 | 0.130491 | 0.6579966 |
3 | 0.7514455 | 0.125 | 0.1235544 | 0.7111182 | 0.1953571 | 0.6525317 |
4 | 0.6896682 | 0.125 | 0.1853316 | 0.703396 | 0.2602232 | 0.6470668 |
5 | 0.6278911 | 0.125 | 0.2471088 | 0.6956738 | 0.3250892 | 0.6416019 |
6 | 0.5661139 | 0.125 | 0.308886 | 0.6879517 | 0.3899553 | 0.636137 |
# | ||||||
---|---|---|---|---|---|---|
1 | 0.625 | 0.00000629 | 0.375 | 0.6380208 | 0.503125 | 0.2060439 |
2 | 0.6301808 | 0.0259042 | 0.3439149 | 0.6221544 | 0.5375776 | 0.2060439 |
3 | 0.634845 | 0.0492253 | 0.3159295 | 0.6078703 | 0.5685947 | 0.2060439 |
4 | 0.6395092 | 0.0725464 | 0.2879442 | 0.5935861 | 0.5996118 | 0.2060439 |
5 | 0.6441735 | 0.0958675 | 0.2599589 | 0.5793019 | 0.6306288 | 0.2060439 |
6 | 0.6488377 | 0.1191886 | 0.2319735 | 0.5650177 | 0.6616459 | 0.2060439 |
# | ||||||
---|---|---|---|---|---|---|
1 | 0.875 | 0.125 | 0.0 | 0.4531 | 0.0375 | 0.5769 |
2 | 0.8098 | 0.125 | 0.0651 | 0.4368 | 0.0766 | 0.5719 |
3 | 0.7446 | 0.125 | 0.1303 | 0.4205 | 0.1157 | 0.5668 |
4 | 0.6794 | 0.125 | 0.1955 | 0.4042 | 0.1548 | 0.5618 |
5 | 0.6142 | 0.125 | 0.2607 | 0.3879 | 0.1939 | 0.5568 |
6 | 0.5491 | 0.125 | 0.3258 | 0.3716 | 0.2330 | 0.5518 |
# | ||||||
---|---|---|---|---|---|---|
1 | 0.625 | 0.0 | 0.375 | 0.5468 | 0.2875 | 0.1442 |
2 | 0.6299 | 0.0249 | 0.3451 | 0.5337 | 0.3064 | 0.1442 |
3 | 0.6349 | 0.0498 | 0.3152 | 0.5207 | 0.3253 | 0.1442 |
4 | 0.6399 | 0.0747 | 0.2853 | 0.5076 | 0.3442 | 0.1442 |
5 | 0.6449 | 0.0996 | 0.2554 | 0.4945 | 0.3632 | 0.1442 |
6 | 0.6499 | 0.1245 | 0.2255 | 0.4814 | 0.3821 | 0.1442 |
Player I | Player II | ||||||
---|---|---|---|---|---|---|---|
0.3860 | 0.1250 | 0.48897 | 0.33088 | 0.25595 | 0.3469 | 0.3972 | 0.5804 |
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Brikaa, M.G.; Zheng, Z.; Ammar, E.-S. Resolving Indeterminacy Approach to Solve Multi-Criteria Zero-Sum Matrix Games with Intuitionistic Fuzzy Goals. Mathematics 2020, 8, 305. https://doi.org/10.3390/math8030305
Brikaa MG, Zheng Z, Ammar E-S. Resolving Indeterminacy Approach to Solve Multi-Criteria Zero-Sum Matrix Games with Intuitionistic Fuzzy Goals. Mathematics. 2020; 8(3):305. https://doi.org/10.3390/math8030305
Chicago/Turabian StyleBrikaa, M. G., Zhoushun Zheng, and El-Saeed Ammar. 2020. "Resolving Indeterminacy Approach to Solve Multi-Criteria Zero-Sum Matrix Games with Intuitionistic Fuzzy Goals" Mathematics 8, no. 3: 305. https://doi.org/10.3390/math8030305
APA StyleBrikaa, M. G., Zheng, Z., & Ammar, E.-S. (2020). Resolving Indeterminacy Approach to Solve Multi-Criteria Zero-Sum Matrix Games with Intuitionistic Fuzzy Goals. Mathematics, 8(3), 305. https://doi.org/10.3390/math8030305