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A Simple and Efficient Algorithm for Solving Type-1 Intuitionistic Fuzzy Solid Transportation Problems

A Simple and Efficient Algorithm for Solving Type-1 Intuitionistic Fuzzy Solid Transportation Problems

P. Senthil Kumar
Copyright: © 2018 |Volume: 9 |Issue: 3 |Pages: 33
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781522544623|DOI: 10.4018/IJORIS.2018070105
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MLA

Kumar, P. Senthil. "A Simple and Efficient Algorithm for Solving Type-1 Intuitionistic Fuzzy Solid Transportation Problems." IJORIS vol.9, no.3 2018: pp.90-122. http://doi.org/10.4018/IJORIS.2018070105

APA

Kumar, P. S. (2018). A Simple and Efficient Algorithm for Solving Type-1 Intuitionistic Fuzzy Solid Transportation Problems. International Journal of Operations Research and Information Systems (IJORIS), 9(3), 90-122. http://doi.org/10.4018/IJORIS.2018070105

Chicago

Kumar, P. Senthil. "A Simple and Efficient Algorithm for Solving Type-1 Intuitionistic Fuzzy Solid Transportation Problems," International Journal of Operations Research and Information Systems (IJORIS) 9, no.3: 90-122. http://doi.org/10.4018/IJORIS.2018070105

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Abstract

This article describes how in solving real-life solid transportation problems (STPs) we often face the state of uncertainty as well as hesitation due to various uncontrollable factors. To deal with uncertainty and hesitation, many authors have suggested the intuitionistic fuzzy (IF) representation for the data. In this article, the author tried to categorise the STP under uncertain environment. He formulates the intuitionistic fuzzy solid transportation problem (IFSTP) and utilizes the triangular intuitionistic fuzzy number (TIFN) to deal with uncertainty and hesitation. The STP has uncertainty and hesitation in supply, demand, capacity of different modes of transport celled conveyance and when it has crisp cost it is known as IFSTP of type-1. From this concept, the generalized mathematical model for type-1 IFSTP is explained. To find out the optimal solution to type-1 IFSTPs, a single stage method called intuitionistic fuzzy min-zero min-cost method is presented. A real-life numerical example is presented to clarify the idea of the proposed method. Moreover, results and discussions, advantages of the proposed method, and future works are presented. The main advantage of the proposed method is that the optimal solution of type-1 IFSTP is obtained without using the basic feasible solution and the method of testing optimality.

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