| Title |
Extended Z-MABAC Method Based on Regret Theory and Directed Distance for Regional Circular Economy Development Program Selection With Z-Information |
| ID_Doc |
28371 |
| Authors |
Shen, KW; Wang, XK; Qiao, D; Wang, JQ |
| Title |
Extended Z-MABAC Method Based on Regret Theory and Directed Distance for Regional Circular Economy Development Program Selection With Z-Information |
| Year |
2020 |
| Published |
Ieee Transactions On Fuzzy Systems, 28.0, 8 |
| DOI |
10.1109/TFUZZ.2019.2923948 |
| Abstract |
Decision makers (DMs) have different cognitive levels in practical experience, information reserve, and thinking ability. Thus, decision information is often not completely reliable. As a tool that can effectively represent information reliability, Z-number has been studied by many scholars in recent years. Current research on Z-number assumes that differences in various parts of a Z-number can complement one another. However, in many cases, the preference of DMs for each part is difficult to determine, or DMs believe that the differences in various parts cannot be complementary. Therefore, to solve such decision problems, this paper attempts to extend the traditional MABAC method to the Z-information environment by introducing the directed distance and regret theory. The proposed method simultaneously considers the randomness and fuzziness of Z-number. An example about regional circular economy development program selection is provided to illustrate the feasibility of the proposed method. Results show that the proposed method can solve complex decision problems rationally and effectively, and it has broad application prospects. |
| Author Keywords |
Euclidean distance; Decision making; Probability distribution; Computational complexity; Reliability theory; Current measurement; Circular economy (CE); fuzzy cut-set theory; regret theory; standardized Euclidean distance; Z-number |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000557355500028 |
| WoS Category |
Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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