<table><caption>**Table 13** - Ranking according to the sensitivity analysis test for L2 students</caption><thead><tr><th rowspan="2">Student</th><th colspan="12">L2 Students</th><th colspan="4">OP</th></tr><tr><th>PC</th><th>PC</th><th>PC</th><th>PC</th><th>DR</th><th>DR</th><th>DR</th><th>DR</th><th>DC</th><th>DC</th><th>DC</th><th>DC</th><th>PW</th><th>PW</th><th>PW</th><th>PW</th><th>OP</th><th>OP</th><th>OP</th><th>OP</th></tr><tr><th></th><th>W1</th><th>W2</th><th>W3</th><th>W4</th><th>W5</th><th>W6</th><th>W7</th><th>W8</th><th>W9</th><th>W10</th><th>W11</th><th>W12</th><th>W13</th><th>W14</th><th>W15</th><th>W16</th><th>W17</th><th>W18</th><th>W19</th><th>W20</th></tr></thead><tbody><tr><td>L201</td><td>10</td><td>8</td><td>11</td><td>7</td><td>7</td><td>7</td><td>7</td><td>7</td><td>10</td><td>8</td><td>10</td><td>7</td><td>8</td><td>8</td><td>9</td><td>10</td><td>14</td><td>14</td><td>11</td><td>10</td></tr><tr><td>L202</td><td>14</td><td>17</td><td>16</td><td>17</td><td>16</td><td>17</td><td>15</td><td>16</td><td>7</td><td>11</td><td>8</td><td>11</td><td>17</td><td>15</td><td>11</td><td>15</td><td>10</td><td>6</td><td>14</td><td>14</td></tr><tr><td>L203</td><td>19</td><td>19</td><td>21</td><td>19</td><td>21</td><td>19</td><td>19</td><td>21</td><td>19</td><td>17</td><td>17</td><td>17</td><td>19</td><td>19</td><td>19</td><td>21</td><td>21</td><td>21</td><td>21</td><td>21</td></tr><tr><td>L204</td><td>18</td><td>18</td><td>17</td><td>18</td><td>18</td><td>18</td><td>18</td><td>17</td><td>16</td><td>16</td><td>14</td><td>19</td><td>18</td><td>18</td><td>16</td><td>17</td><td>12</td><td>12</td><td>16</td><td>16</td></tr><tr><td>L205</td><td>20</td><td>20</td><td>19</td><td>20</td><td>19</td><td>20</td><td>20</td><td>19</td><td>20</td><td>20</td><td>20</td><td>22</td><td>20</td><td>20</td><td>20</td><td>19</td><td>16</td><td>17</td><td>17</td><td>17</td></tr><tr><td>L206</td><td>20</td><td>20</td><td>19</td><td>20</td><td>19</td><td>20</td><td>20</td><td>19</td><td>20</td><td>20</td><td>20</td><td>22</td><td>20</td><td>20</td><td>20</td><td>19</td><td>16</td><td>17</td><td>17</td><td>17</td></tr><tr><td>L207</td><td>3</td><td>4</td><td>3</td><td>4</td><td>5</td><td>5</td><td>5</td><td>5</td><td>2</td><td>2</td><td>2</td><td>3</td><td>4</td><td>4</td><td>3</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>L208</td><td>1</td><td>1</td><td>1</td><td>2</td><td>1</td><td>1</td><td>1</td><td>1</td><td>6</td><td>5</td><td>4</td><td>5</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td><td>3</td><td>2</td><td>2</td></tr><tr><td>L209</td><td>4</td><td>3</td><td>5</td><td>3</td><td>2</td><td>2</td><td>2</td><td>2</td><td>4</td><td>3</td><td>5</td><td>2</td><td>3</td><td>3</td><td>5</td><td>5</td><td>9</td><td>7</td><td>4</td><td>4</td></tr><tr><td>L210</td><td>6</td><td>10</td><td>6</td><td>10</td><td>17</td><td>16</td><td>17</td><td>18</td><td>14</td><td>19</td><td>19</td><td>14</td><td>16</td><td>17</td><td>18</td><td>16</td><td>19</td><td>20</td><td>20</td><td>20</td></tr><tr><td>L211</td><td>10</td><td>8</td><td>11</td><td>7</td><td>7</td><td>7</td><td>7</td><td>7</td><td>10</td><td>8</td><td>10</td><td>7</td><td>8</td><td>8</td><td>9</td><td>10</td><td>14</td><td>14</td><td>11</td><td>10</td></tr><tr><td>L212</td><td>15</td><td>13</td><td>10</td><td>12</td><td>10</td><td>10</td><td>10</td><td>10</td><td>15</td><td>13</td><td>13</td><td>15</td><td>11</td><td>11</td><td>12</td><td>9</td><td>5</td><td>8</td><td>6</td><td>7</td></tr><tr><td>L213</td><td>12</td><td>16</td><td>14</td><td>13</td><td>15</td><td>13</td><td>14</td><td>14</td><td>3</td><td>7</td><td>3</td><td>9</td><td>15</td><td>12</td><td>7</td><td>13</td><td>8</td><td>4</td><td>13</td><td>12</td></tr><tr><td>L214</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>20</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td></tr><tr><td>L215</td><td>7</td><td>5</td><td>8</td><td>5</td><td>4</td><td>4</td><td>4</td><td>4</td><td>5</td><td>4</td><td>7</td><td>4</td><td>5</td><td>5</td><td>6</td><td>6</td><td>11</td><td>10</td><td>5</td><td>5</td></tr><tr><td>L216</td><td>17</td><td>15</td><td>18</td><td>16</td><td>14</td><td>12</td><td>13</td><td>13</td><td>18</td><td>15</td><td>16</td><td>13</td><td>14</td><td>14</td><td>17</td><td>18</td><td>20</td><td>19</td><td>19</td><td>19</td></tr><tr><td>L217</td><td>16</td><td>14</td><td>13</td><td>14</td><td>11</td><td>11</td><td>12</td><td>11</td><td>17</td><td>14</td><td>15</td><td>16</td><td>12</td><td>13</td><td>14</td><td>12</td><td>7</td><td>9</td><td>7</td><td>8</td></tr><tr><td>L218</td><td>5</td><td>7</td><td>2</td><td>9</td><td>12</td><td>15</td><td>16</td><td>15</td><td>13</td><td>18</td><td>18</td><td>18</td><td>13</td><td>16</td><td>15</td><td>7</td><td>4</td><td>11</td><td>9</td><td>13</td></tr><tr><td>L219</td><td>8</td><td>12</td><td>7</td><td>15</td><td>13</td><td>14</td><td>11</td><td>12</td><td>9</td><td>12</td><td>6</td><td>12</td><td>6</td><td>6</td><td>2</td><td>2</td><td>3</td><td>2</td><td>10</td><td>6</td></tr><tr><td>L220</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td></tr><tr><td>L221</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td></tr><tr><td>L222</td><td>23</td><td>23</td><td>23</td><td>23</td><td>23</td><td>23</td><td>23</td><td>23</td><td>23</td><td>23</td><td>23</td><td>21</td><td>23</td><td>23</td><td>23</td><td>23</td><td>23</td><td>23</td><td>23</td><td>23</td></tr><tr><td>L223</td><td>2</td><td>2</td><td>4</td><td>1</td><td>3</td><td>3</td><td>3</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td><td>2</td><td>4</td><td>4</td><td>6</td><td>5</td><td>3</td><td>3</td></tr><tr><td>L224</td><td>13</td><td>11</td><td>15</td><td>11</td><td>9</td><td>9</td><td>9</td><td>9</td><td>12</td><td>10</td><td>12</td><td>10</td><td>10</td><td>10</td><td>13</td><td>14</td><td>18</td><td>16</td><td>15</td><td>15</td></tr><tr><td>L225</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td><td>24</td></tr><tr><td>L226</td><td>9</td><td>6</td><td>9</td><td>6</td><td>6</td><td>6</td><td>6</td><td>6</td><td>8</td><td>6</td><td>9</td><td>6</td><td>7</td><td>7</td><td>8</td><td>8</td><td>13</td><td>13</td><td>8</td><td>9</td></tr></tbody></table>

# 6. Discussion, conclusion, and future works

## 6.1. Discussion of the proposed method

In this section we present a brief discussion on the extracted results and finally provide a conclusion. Upon the results of the changes in the weights of the criteria, we found that for each prioritized criterion (PC, DR, DC, PW, OP) four different situations could be observed (1, 2, 3, 4).

**Situation 1 (blue)** presents the percentage of students who have different ranks on the priority of a criterion four times. **Situation 2 (orange)** denotes the percentage of students who have half equal ranking when prioritizing a criterion four times. **Situation 3 (grey)** shows the percentage of students who have 3 equal ranks out of four. Lastly, **Situation 4 (yellow)** indicates the
percentage of students who have stable ranks (4/4) on a criterion that has been prioritized four times.

Following this result, a rank stability analysis by prioritized criterion was carried out. The figure below shows the percentage of rank stability for students in L1.

<figure><img src="image_4.png" /><figcaption>Figure 3 – Rank stability analysis for CoCoFISo method (L1 students)</figcaption></figure>

Compared to these situations we can draw that for the students in L1, the percentage of the differences of the ranks is low (0% to 18%) for each prioritization of the criteria. On the other hand, the percentage of equality of ranks is significant for each prioritized criterion. We can cite among other things that situation 2 has the highest percentage (30% to 67%), while situation 3 presents an average percentage (11% to 37%) and situation 4 leaves a percentage almost similar to situation 3 (7% to 37%). Therefore, this can guarantee that by changing the priority criteria for L1 students, the ranks are generally stable. Similarly, the four situations (1, 2, 3, 4) are also observed for L2 students which are presented in the figure below.

<figure><img src="image_5.png" /><figcaption>Figure 4 - Rank stability analysis for CoCoFISo method (L2 students)</figcaption></figure>

Thus, the percentage of students who have different ranks on the four prioritization of a criterion ranges from 7% to 31% for all criteria. Students who have half equal ranks are in the range of 27% to 58%. Students whose ranks are three-quarters (3/4) equal have a percentage of 8% to 27%. Finally, students who have equal ranks on all four modified weights range from 19% to 58%.
percentage of students who have stable ranks (4/4) on a criterion that has been prioritized four times.

Following this result, a rank stability analysis by prioritized criterion was carried out. The figure below shows the percentage of rank stability for students in L1.

<figure><img src="image_6.png" /><figcaption>Figure 3 – Rank stability analysis for CoCoFISo method (L1 students)</figcaption></figure>

Compared to these situations we can draw that for the students in L1, the percentage of the differences of the ranks is low (0% to 18%) for each prioritization of the criteria. On the other hand, the percentage of equality of ranks is significant for each prioritized criterion. We can cite among other things that situation 2 has the highest percentage (30% to 67%), while situation 3 presents an average percentage (11% to 37%) and situation 4 leaves a percentage almost similar to situation 3 (7% to 37%). Therefore, this can guarantee that by changing the priority criteria for L1 students, the ranks are generally stable. Similarly, the four situations (1, 2, 3, 4) are also observed for L2 students which are presented in the figure below.

<figure><img src="image_7.png" /><figcaption>Figure 4 - Rank stability analysis for CoCoFISo method (L2 students)</figcaption></figure>

Thus, the percentage of students who have different ranks on the four prioritization of a criterion ranges from 7% to 31% for all criteria. Students who have half equal ranks are in the range of 27% to 58%. Students whose ranks are three-quarters (3/4) equal have a percentage of 8% to 27%. Finally, students who have equal ranks on all four modified weights range from 19% to 58%.
This analysis addresses us that by changing the criteria weights for L2 students, the ranks remained stable.

## 6.2. Conclusion

In this study we have proposed a new version of the CoCoSo method according to some errors observed in the algorithm in special cases which we called CoCoFISo. Along with the extended version, the two real cases of students' residence allocation with two groups of students (L1 and L2) were assumed to show us the limit of the CoCoSo method. Compared to the results of the PROMETHEE, WSM and TOPSIS methods, the ranking obtained by the CoCoFISo method was validated according to the Spearman and Kendall correlation coefficients. Moreover, we have designed several tests by different criteria weights, and we have checked that the result of the CoCoFISo method was globally stable. The main advantage of CoCoFISo is the correction of the first version of the algorithm. This new version is now usable without any exceptions and can solve any multi-criteria decision-making problem. In this article, the limit of the CoCoFISo method is that it was applied just for a case which is the allocation of student accommodation.

## 6.3. Future research work

However, for future research, its application in various fields of knowledge will be interesting to know its strength. It is not always possible to obtain all data for such a method. Sometimes data is unavailable, sometimes it is uncertain. Researchers may in future develop the fuzzy CoCoFISo when uncertainty exists to implement new versions of fuzzy CoCoSo. This would allow us to consider uncertainty about data and scenario.

## References

Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. *Renewable and Sustainable Energy Reviews, 69*, 596-609.

Torkayesh, A. E., Rajaeifar, M. A., Rostom, M., Malmir, B., Yazdani, M., Suh, S., & Heidrich, O. (2022). Integrating life cycle assessment and multi criteria decision making for sustainable waste management: key issues and recommendations for future studies. *Renewable and Sustainable Energy Reviews, 168*, 112819.

Fallahpour, A., Yazdani, M., Mohammed, A., & Wong, K. Y. (2021). Green sourcing in the era of industry 4.0: Towards green and digitalized competitive advantages. *Industrial Management & Data Systems, 121*(9), 1997-2025.

Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019a). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. *Management Decision, 57*(9), 2501-2519.

Kumar, V., Kalita, K., Chatterjee, P., Zavadskas, E. K., & Chakraborty, S. (2022). A SWARA-CoCoSo-based approach for spray painting robot selection. *Informatica, 33*(1), 35-54.
This analysis addresses us that by changing the criteria weights for L2 students, the ranks remained stable.

## 6.2. Conclusion

In this study we have proposed a new version of the CoCoSo method according to some errors observed in the algorithm in special cases which we called CoCoFISo. Along with the extended version, the two real cases of students' residence allocation with two groups of students (L1 and L2) were assumed to show us the limit of the CoCoSo method. Compared to the results of the PROMETHEE, WSM and TOPSIS methods, the ranking obtained by the CoCoFISo method was validated according to the Spearman and Kendall correlation coefficients. Moreover, we have designed several tests by different criteria weights, and we have checked that the result of the CoCoFISo method was globally stable. The main advantage of CoCoFISo is the correction of the first version of the algorithm. This new version is now usable without any exceptions and can solve any multi-criteria decision-making problem. In this article, the limit of the CoCoFISo method is that it was applied just for a case which is the allocation of student accommodation.

## 6.3. Future research work

However, for future research, its application in various fields of knowledge will be interesting to know its strength. It is not always possible to obtain all data for such a method. Sometimes data is unavailable, sometimes it is uncertain. Researchers may in future develop the fuzzy CoCoFISo when uncertainty exists to implement new versions of fuzzy CoCoSo. This would allow us to consider uncertainty about data and scenario.

## References

Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. *Renewable and Sustainable Energy Reviews, 69*, 596-609.

Torkayesh, A. E., Rajaeifar, M. A., Rostom, M., Malmir, B., Yazdani, M., Suh, S., & Heidrich, O. (2022). Integrating life cycle assessment and multi criteria decision making for sustainable waste management: key issues and recommendations for future studies. *Renewable and Sustainable Energy Reviews, 168*, 112819.

Fallahpour, A., Yazdani, M., Mohammed, A., & Wong, K. Y. (2021). Green sourcing in the era of industry 4.0: Towards green and digitalized competitive advantages. *Industrial Management & Data Systems, 121*(9), 1997-2025.

Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019a). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. *Management Decision, 57*(9), 2501-2519.

Kumar, V., Kalita, K., Chatterjee, P., Zavadskas, E. K., & Chakraborty, S. (2022). A SWARA-CoCoSo-based approach for spray painting robot selection. *Informatica, 33*(1), 35-54.
Govindan, K., Kadziński, M., & Sivakumar, R. (2017). Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. *Omega, 71*, 129-145.

Khan, S., Kaushik, M. K., Kumar, R., & Khan, W. (2023). Investigating the barriers of blockchain technology integrated food supply chain: A BWM approach. *Benchmarking: An International Journal, 30*(3), 713-735.

Govindan, K., & Jepsen, M. B. (2016). ELECTRE: A comprehensive literature review on methodologies and applications. *European Journal of Operational Research, 250*(1), 1-29.

Ecer, F., & Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo'B) multi-criteria model. *Journal of cleaner production, 266*, 121981.

Yazdani, M., Wen, Z., Liao, H., Banaitis, A., & Turskis, Z. (2019b). A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management. *Journal of Civil Engineering and Management, 25*(8), 858-874.

Torkayesh, A. E., Pamucar, D., Ecer, F., & Chatterjee, P. (2021). An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe. *Socio-Economic Planning Sciences, 78*, 101052.

Khan, S., & Haleem, A. (2021). Investigation of circular economy practices in the context of emerging economies: a CoCoSo approach. *International Journal of Sustainable Engineering, 14*(3), 357-367.

Peng, X., & Huang, H. (2020). Fuzzy decision-making method based on CoCoSo with critic for financial risk evaluation. Technological and Economic Development of Economy, 26(4), 695-724.

Ulutaş, A., Karakuş, C. B., & Topal, A. (2020). Location selection for logistics center with fuzzy SWARA and CoCoSo methods. *Journal of Intelligent & Fuzzy Systems, 38*(4), 4693-4709.

Hwang C.-L., Yoon K. « Methods for Multiple Attribute Decision Making ». In : Hwang C-L, Yoon K (éd.). Mult. Attrib. Decis. Mak. Methods Appl. State---Art Surv. [En ligne]. Berlin, Heidelberg : Springer, 1981. p. 58-191: < https://doi.org/10.1007/978-3-642-48318-9_3 > ISBN : 978-3-642-48318-9.

Rasoanaivo R. G. Système d'aide à la décision multicritère pour l'allocation de logements étudiants [En ligne]. phdthesis: Université de Toulouse Capitole, Institut de Recherche en Informatique de Toulouse (IRIT), 2023. Disponible sur : < https://hal.science/tel-04119898 >

Rasoanaivo R. G., Zaraté P. « A Decision Support System for student's room allocation ». 8th International Conference on Decision Support System Technology – ICDSST 2022, University of Macedonia, Department of Business Administration Thessaloniki, Greece, 23-25/may/2022. 23 mai 2022. p. 30-36.

Rasoanaivo R. G., Zaraté P. « Students' accommodation allocation: A Multicriteria Decision Support System ». Electron. J. SADIO EJS. 1 août 2023. Vol. 22, n°3, p. 15-27.

Yazdani M., Zaraté P., Kazimieras Zavadskas E., Turskis Z. « A Combined Compromise Solution (CoCoSo) method for multi-criteria decision-making problems ». Manag. Decis. [En ligne]. octobre 2019. Vol. 57, n°9, p. 2501-2519 : < https://doi.org/10.1108/MD-05-2017-0458 >

Wei Chen, Yue Shen, Yanan Wang. «Evaluation of economic transformation and upgrading of resource-based cities in Shaanxi province based on an improved TOPSIS method». Sustainable Cities and Society 37 (2018) 232–240, https://doi.org/10.1016/j.scs.2017.11.019.

Hua-Wen Wu, En-qun Li, Yuan-yun Sun, Bao-tian Dong. «Research on the operation safety evaluation of urban rail stations based on the improved TOPSIS method and entropy weight method». Journal of Rail Transport Planning & Management 20 (2021) 100262, https://doi.org/10.1016/j.jrtpm.2021.100262.
Govindan, K., Kadziński, M., & Sivakumar, R. (2017). Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. *Omega, 71*, 129-145.

Khan, S., Kaushik, M. K., Kumar, R., & Khan, W. (2023). Investigating the barriers of blockchain technology integrated food supply chain: A BWM approach. *Benchmarking: An International Journal, 30*(3), 713-735.

Govindan, K., & Jepsen, M. B. (2016). ELECTRE: A comprehensive literature review on methodologies and applications. *European Journal of Operational Research, 250*(1), 1-29.

Ecer, F., & Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo'B) multi-criteria model. *Journal of cleaner production, 266*, 121981.

Yazdani, M., Wen, Z., Liao, H., Banaitis, A., & Turskis, Z. (2019b). A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management. *Journal of Civil Engineering and Management, 25*(8), 858-874.

Torkayesh, A. E., Pamucar, D., Ecer, F., & Chatterjee, P. (2021). An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe. *Socio-Economic Planning Sciences, 78*, 101052.

Khan, S., & Haleem, A. (2021). Investigation of circular economy practices in the context of emerging economies: a CoCoSo approach. *International Journal of Sustainable Engineering, 14*(3), 357-367.

Peng, X., & Huang, H. (2020). Fuzzy decision-making method based on CoCoSo with critic for financial risk evaluation. *Technological and Economic Development of Economy, 26*(4), 695-724.

Ulutaş, A., Karakuş, C. B., & Topal, A. (2020). Location selection for logistics center with fuzzy SWARA and CoCoSo methods. *Journal of Intelligent & Fuzzy Systems, 38*(4), 4693-4709.

Hwang C.-L., Yoon K. « Methods for Multiple Attribute Decision Making ». In : Hwang C-L, Yoon K (éd.). Mult. Attrib. Decis. Mak. Methods Appl. State---Art Surv. [En ligne]. Berlin, Heidelberg : Springer, 1981. p. 58-191: < https://doi.org/10.1007/978-3-642-48318-9_3 > ISBN : 978-3-642-48318-9.

Rasoanaivo R. G. Système d'aide à la décision multicritère pour l'allocation de logements étudiants [En ligne]. phdthesis: Université de Toulouse Capitole, Institut de Recherche en Informatique de Toulouse (IRIT), 2023. Disponible sur : < https://hal.science/tel-04119898 >

Rasoanaivo R. G., Zaraté P. « A Decision Support System for student's room allocation ». 8th International Conference on Decision Support System Technology – ICDSST 2022, University of Macedonia, Department of Business Administration Thessaloniki, Greece, 23-25/may/2022. 23 mai 2022. p. 30-36.

Rasoanaivo R. G., Zaraté P. « Students' accommodation allocation: A Multicriteria Decision Support System ». Electron. J. SADIO EJS. 1 août 2023. Vol. 22, n°3, p. 15-27.

Yazdani M., Zaraté P., Kazimieras Zavadskas E., Turskis Z. « A Combined Compromise Solution (CoCoSo) method for multi-criteria decision-making problems ». Manag. Decis. [En ligne]. octobre 2019. Vol. 57, n°9, p. 2501-2519 : < https://doi.org/10.1108/MD-05-2017-0458 >

Wei Chen, Yue Shen, Yanan Wang. «Evaluation of economic transformation and upgrading of resource-based cities in Shaanxi province based on an improved TOPSIS method». Sustainable Cities and Society 37 (2018) 232–240, https://doi.org/10.1016/j.scs.2017.11.019.

Hua-Wen Wu, En-qun Li, Yuan-yun Sun, Bao-tian Dong. «Research on the operation safety evaluation of urban rail stations based on the improved TOPSIS method and entropy weight method». *Journal of Rail Transport Planning & Management* 20 (2021) 100262, https://doi.org/10.1016/j.jrtpm.2021.100262.
Jie Wu, Jiasen Sun, Yong Zha, and Liang Liang. «Ranking approach of cross-efficiency based on improved TOPSIS technique». Journal of Systems Engineering and Electronics Vol. 22, No. 4, August 2011, pp.604–608, DOI: 10.3969/j.issn.1004-4132.2011.04.008.

Haijing Zhang, Mengke Lu, Xianbo Ke, Shaoqi Yu, Jianzhe Zhao, Ye Wu, Lin Cheng, Xiaoteng Li. «Evaluation model of black-start schemes based on optimal combination weights and improved VIKOR method». Electrical Power and Energy Systems 129 (2021) 106762, https://doi.org/10.1016/j.ijepes.2021.106762.

Chuan Zhang, Yu-xin Tian, Ling-wei Fan, Ying-hui Li. «Customized ranking for products through online reviews: a method incorporating prospect theory with an improved VIKOR». Applied Intelligence (2020) 50:1725–1744, https://doi.org/10.1007/s10489-019-01577-3.

M. Aghajani Mir, P. Taherei Ghazvinei, N.M.N. Sulaiman, N.E.A. Basri, S. Saheri, N.Z. Mahmood, A. Jahan, R.A. Begum, N. Aghamohammadi. «Application of TOPSIS and VIKOR improved versions in a multi criteria decision analysis to develop an optimized municipal solid waste management model». Journal of Environmental Management 166 (2016) 109e115, http://dx.doi.org/10.1016/j.jenvman.2015.09.028.

Irvanizam Irvanizam, Intan Syahrini, Nawar Nabila Zi, Natasya Azzahra, Muhd Iqbal, Marzuki Marzuki, and Muhammad Subianto. «An Improved EDAS Method Based on Bipolar Neutrosophic Set and Its Application in Group Decision-Making». Applied Computational Intelligence and Soft Computing, Volume 2021, Article ID 1474629, 16 pages, https://doi.org/10.1155/2021/1474629.

Dan Peng, JieWang, Donghai Liu, Zaiming Liu. «An Improved EDAS Method for the Multi Attribute Decision Making Based on the Dynamic Expectation Level of Decision Makers». Symmetry 2022, 14, 979, https://doi.org/10.3390/sym14050979.

Siqi ZHANG, Guiwu WEI, RuiWANG, JiangWU, Cun WEI, Yanfeng GUO, Yu WEI. «Improved CODAS Method Under Picture 2-Tuple Linguistic Environment and Its Application for a Green Supplier Selection». INFORMATICA, 2021, Vol. 32, No. 1, 195-216, DOI: https://doi.org/10.15388/20-INFOR414.

Lei Xu, Yi Liu, Fang Liu. «Improved MABAC method based on single-valued neutrosophic 2-tuple linguistic sets and Frank aggregation operators for MAGDM». Computational and Applied Mathematics (2021) 40:267, https://doi.org/10.1007/s40314-021-01656-7.

Han Lai, Huchang Liao, Zhi Wen, Edmundas Kazimieras Zavadskas, Abdullah Al-Barakati. «An Improved CoCoSo Method with a Maximum Variance Optimization Model for Cloud Service Provider Selection». Inzinerine Ekonomika-Engineering Economics, 2020, 31(4), 411-424, http://dx.doi.org/10.5755/j01.ee.31.4.24990.

Haolun Wang, Tahir Mahmood, Kifayat Ullah. «Improved CoCoSo Method Based on Frank Softmax Aggregation Operators for T-Spherical Fuzzy Multiple Attribute Group Decision-Making». International Journal of Fuzzy Systems, (2023) 25(3):1275-1310, https://doi.org/10.1007/s40815-022-01442-5.

Gunjan Yadav, Dinesh Seth, Tushar N. Desai. «Prioritising solutions for Lean Six Sigma adoption barriers through fuzzy AHP-modified TOPSIS framework». International Journal of Lean Six Sigma, Vol. 9 No. 3, pp. 270-300, https://doi.org/10.1108/IJLSS-06-2016-0023.

Xunjie Gou, Zeshui Xu, Huchang Liao, Francisco Herrera. Probabilistic double hierarchy linguistic term set and its use in designing an improved VIKOR method: The application in smart healthcare. Journal of the Operational Research Society Volume 72, 2021 - Issue 12, https://doi.org/10.1080/01605682.2020.1806741.

Peide Liu and Shufeng Cheng. An Improved MABAC Group Decision-Making Method Using Regret Theory and Likelihood in Probability Multi-Valued Neutrosophic Sets. International Journal of Information Technology & Decision Making, Vol. 19, No. 05, pp. 1353-1387 (2020), https://doi.org/10.1142/S0219622020500303.
Jie Wu, Jiasen Sun, Yong Zha, and Liang Liang. «Ranking approach of cross-efficiency based on improved TOPSIS technique». Journal of Systems Engineering and Electronics Vol. 22, No. 4, August 2011, pp.604–608, DOI: 10.3969/j.issn.1004-4132.2011.04.008.

Haijing Zhang, Mengke Lu, Xianbo Ke, Shaoqi Yu, Jianzhe Zhao, Ye Wu, Lin Cheng, Xiaoteng Li. «Evaluation model of black-start schemes based on optimal combination weights and improved VIKOR method». Electrical Power and Energy Systems 129 (2021) 106762, https://doi.org/10.1016/j.ijepes.2021.106762.

Chuan Zhang, Yu-xin Tian, Ling-wei Fan, Ying-hui Li. «Customized ranking for products through online reviews: a method incorporating prospect theory with an improved VIKOR». Applied Intelligence (2020) 50:1725–1744, https://doi.org/10.1007/s10489-019-01577-3.

M. Aghajani Mir, P. Taherei Ghazvinei, N.M.N. Sulaiman, N.E.A. Basri, S. Saheri, N.Z. Mahmood, A. Jahan, R.A. Begum, N. Aghamohammadi. «Application of TOPSIS and VIKOR improved versions in a multi criteria decision analysis to develop an optimized municipal solid waste management model». Journal of Environmental Management 166 (2016) 109e115, http://dx.doi.org/10.1016/j.jenvman.2015.09.028.

Irvanizam Irvanizam, Intan Syahrini, Nawar Nabila Zi, Natasya Azzahra, Muhd Iqbal, Marzuki Marzuki, and Muhammad Subianto. «An Improved EDAS Method Based on Bipolar Neutrosophic Set and Its Application in Group Decision-Making». Applied Computational Intelligence and Soft Computing, Volume 2021, Article ID 1474629, 16 pages, https://doi.org/10.1155/2021/1474629.

Dan Peng, JieWang, Donghai Liu, Zaiming Liu. «An Improved EDAS Method for the Multi Attribute Decision Making Based on the Dynamic Expectation Level of Decision Makers». Symmetry 2022, 14, 979, https://doi.org/10.3390/sym14050979.

Siqi ZHANG, Guiwu WEI, RuiWANG, JiangWU, Cun WEI, Yanfeng GUO, Yu WEI. «Improved CODAS Method Under Picture 2-Tuple Linguistic Environment and Its Application for a Green Supplier Selection». INFORMATICA, 2021, Vol. 32, No. 1, 195-216, DOI: https://doi.org/10.15388/20-INFOR414.

Lei Xu, Yi Liu, Fang Liu. «Improved MABAC method based on single-valued neutrosophic 2-tuple linguistic sets and Frank aggregation operators for MAGDM». Computational and Applied Mathematics (2021) 40:267, https://doi.org/10.1007/s40314-021-01656-7.

Han Lai, Huchang Liao, Zhi Wen, Edmundas Kazimieras Zavadskas, Abdullah Al-Barakati. «An Improved CoCoSo Method with a Maximum Variance Optimization Model for Cloud Service Provider Selection». Inzinerine Ekonomika-Engineering Economics, 2020, 31(4), 411-424, http://dx.doi.org/10.5755/j01.ee.31.4.24990.

Haolun Wang, Tahir Mahmood, Kifayat Ullah. «Improved CoCoSo Method Based on Frank Softmax Aggregation Operators for T-Spherical Fuzzy Multiple Attribute Group Decision-Making». International Journal of Fuzzy Systems, (2023) 25(3):1275-1310, https://doi.org/10.1007/s40815-022-01442-5.

Gunjan Yadav, Dinesh Seth, Tushar N. Desai. «Prioritising solutions for Lean Six Sigma adoption barriers through fuzzy AHP-modified TOPSIS framework». International Journal of Lean Six Sigma, Vol. 9 No. 3, pp. 270-300, https://doi.org/10.1108/IJLSS-06-2016-0023.

Xunjie Gou, Zeshui Xu, Huchang Liao, Francisco Herrera. Probabilistic double hierarchy linguistic term set and its use in designing an improved VIKOR method: The application in smart healthcare. Journal of the Operational Research Society Volume 72, 2021 - Issue 12, https://doi.org/10.1080/01605682.2020.1806741.

Peide Liu and Shufeng Cheng. An Improved MABAC Group Decision-Making Method Using Regret Theory and Likelihood in Probability Multi-Valued Neutrosophic Sets. International Journal of Information Technology & Decision Making, Vol. 19, No. 05, pp. 1353-1387 (2020), https://doi.org/10.1142/S0219622020500303.