Entropy and Semi-Entropies of LR Fuzzy Numbers’ Linear Function with Applications to Fuzzy Programming
As a crucial concept of characterizing uncertainty, entropy has been widely used in fuzzy programming problems, while involving complicated calculations. To simplify the operations so as to broaden its applicable areas, this paper investigates the entropy within the framework of credibility theory a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/7/697 |
| _version_ | 1817990688906674176 |
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| author | Jian Zhou Chuan Huang Mingxuan Zhao Hui Li |
| author_facet | Jian Zhou Chuan Huang Mingxuan Zhao Hui Li |
| author_sort | Jian Zhou |
| collection | DOAJ |
| description | As a crucial concept of characterizing uncertainty, entropy has been widely used in fuzzy programming problems, while involving complicated calculations. To simplify the operations so as to broaden its applicable areas, this paper investigates the entropy within the framework of credibility theory and derives the formulas for calculating the entropy of regular LR fuzzy numbers by virtue of the inverse credibility distribution. By verifying the favorable property of this operator, a calculation formula of a linear function’s entropy is also proposed. Furthermore, considering the strength of semi-entropy in measuring one-side uncertainty, the lower and upper semi-entropies, as well as the corresponding formulas are suggested to handle return-oriented and cost-oriented problems, respectively. Finally, utilizing entropy and semi-entropies as risk measures, two types of entropy optimization models and their equivalent formulations derived from the proposed formulas are given according to different decision criteria, providing an effective modeling method for fuzzy programming from the perspective of entropy. The numerical examples demonstrate the high efficiency and good performance of the proposed methods in decision making. |
| first_indexed | 2024-04-14T01:02:24Z |
| format | Article |
| id | doaj.art-6b8794c840d8483e80ca4161b729e4e7 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-14T01:02:24Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-6b8794c840d8483e80ca4161b729e4e72022-12-22T02:21:21ZengMDPI AGEntropy1099-43002019-07-0121769710.3390/e21070697e21070697Entropy and Semi-Entropies of LR Fuzzy Numbers’ Linear Function with Applications to Fuzzy ProgrammingJian Zhou0Chuan Huang1Mingxuan Zhao2Hui Li3School of Management, Shanghai University, Shanghai 200444, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaSchool of Economics and Management, Tongji University, Shanghai 200082, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaAs a crucial concept of characterizing uncertainty, entropy has been widely used in fuzzy programming problems, while involving complicated calculations. To simplify the operations so as to broaden its applicable areas, this paper investigates the entropy within the framework of credibility theory and derives the formulas for calculating the entropy of regular LR fuzzy numbers by virtue of the inverse credibility distribution. By verifying the favorable property of this operator, a calculation formula of a linear function’s entropy is also proposed. Furthermore, considering the strength of semi-entropy in measuring one-side uncertainty, the lower and upper semi-entropies, as well as the corresponding formulas are suggested to handle return-oriented and cost-oriented problems, respectively. Finally, utilizing entropy and semi-entropies as risk measures, two types of entropy optimization models and their equivalent formulations derived from the proposed formulas are given according to different decision criteria, providing an effective modeling method for fuzzy programming from the perspective of entropy. The numerical examples demonstrate the high efficiency and good performance of the proposed methods in decision making.https://www.mdpi.com/1099-4300/21/7/697fuzzy programmingentropysemi-entropylinear functionLR fuzzy number |
| spellingShingle | Jian Zhou Chuan Huang Mingxuan Zhao Hui Li Entropy and Semi-Entropies of LR Fuzzy Numbers’ Linear Function with Applications to Fuzzy Programming Entropy fuzzy programming entropy semi-entropy linear function LR fuzzy number |
| title | Entropy and Semi-Entropies of LR Fuzzy Numbers’ Linear Function with Applications to Fuzzy Programming |
| title_full | Entropy and Semi-Entropies of LR Fuzzy Numbers’ Linear Function with Applications to Fuzzy Programming |
| title_fullStr | Entropy and Semi-Entropies of LR Fuzzy Numbers’ Linear Function with Applications to Fuzzy Programming |
| title_full_unstemmed | Entropy and Semi-Entropies of LR Fuzzy Numbers’ Linear Function with Applications to Fuzzy Programming |
| title_short | Entropy and Semi-Entropies of LR Fuzzy Numbers’ Linear Function with Applications to Fuzzy Programming |
| title_sort | entropy and semi entropies of lr fuzzy numbers linear function with applications to fuzzy programming |
| topic | fuzzy programming entropy semi-entropy linear function LR fuzzy number |
| url | https://www.mdpi.com/1099-4300/21/7/697 |
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