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A Different Approach for Solving the Shortest Path Problem Under Mixed Fuzzy Environment

A Different Approach for Solving the Shortest Path Problem Under Mixed Fuzzy Environment

Ranjan Kumar, Sripati Jha, Ramayan Singh
Copyright: © 2020 |Volume: 9 |Issue: 2 |Pages: 30
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781522598428|DOI: 10.4018/IJFSA.2020040106
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MLA

Kumar, Ranjan, et al. "A Different Approach for Solving the Shortest Path Problem Under Mixed Fuzzy Environment." IJFSA vol.9, no.2 2020: pp.132-161. http://doi.org/10.4018/IJFSA.2020040106

APA

Kumar, R., Jha, S., & Singh, R. (2020). A Different Approach for Solving the Shortest Path Problem Under Mixed Fuzzy Environment. International Journal of Fuzzy System Applications (IJFSA), 9(2), 132-161. http://doi.org/10.4018/IJFSA.2020040106

Chicago

Kumar, Ranjan, Sripati Jha, and Ramayan Singh. "A Different Approach for Solving the Shortest Path Problem Under Mixed Fuzzy Environment," International Journal of Fuzzy System Applications (IJFSA) 9, no.2: 132-161. http://doi.org/10.4018/IJFSA.2020040106

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Abstract

The authors present a new algorithm for solving the shortest path problem (SPP) in a mixed fuzzy environment. With this algorithm, the authors can solve the problems with different sets of fuzzy numbers e.g., normal, trapezoidal, triangular, and LR-flat fuzzy membership functions. Moreover, the authors can solve the fuzzy shortest path problem (FSPP) with two different membership functions such as normal and a fuzzy membership function under real-life situations. The transformation of the fuzzy linear programming (FLP) model into a crisp linear programming model by using a score function is also investigated. Furthermore, the shortcomings of some existing methods are discussed and compared with the algorithm. The objective of the proposed method is to find the fuzzy shortest path (FSP) for the given network; however, this is also capable of predicting the fuzzy shortest path length (FSPL) and crisp shortest path length (CSPL). Finally, some numerical experiments are given to show the effectiveness and robustness of the new model. Numerical results show that this method is superior to the existing methods.

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