A New Approach to Solve Fully Fuzzy Multi-Objective Transportation Problem

Abstract

The transportation problem is the problem of transferring goods from several sources or producers to multiple destinations or consumers in a cost-effective way, which is one of the most important problems in the supply chain management problems. The application of this problem in addition to the distribution of goods in the location and production planning problems is also important. Many real-life transportation problems encounter multiple, conflicting, and incommensurable objective functions. In addition, in real applications, due to lack of information, it is not possible to accurately estimate the parameters of this problem. Therefore, the main goal of this paper is to find the Pareto optimal solutions of fully fuzzy multi-objective transportation problem under the conditions of uncertainty. In accordingly, a new approach based on nearest interval approximation is proposed to solve the problem. Numerical examples are provided to illustrate the proposed approach and results.

KEYWORDS:

• Fully fuzzy transportation problem
• multi-objective problem
• nearest interval approximation
• Pareto optimal solutions

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Malihe Niksirat

Malihe Niksirat received her PhD degree in 2016 on Applied Mathematics and Computer Sciences from Amirkabir University of Technology, and since 2018 she has been a faculty member at the Department of Computer Sciences in Birjand University of Technology, Birjand, Iran. She was a member of the Scientific Committee of the 9th International Conference on Fuzzy Information and Engineering in 2018. Now, she is a member of Iranian Operations Research Society. Her research interests are in the areas of Fuzzy Mathematical Models and Methods, Fuzzy Arithmetic, Fuzzy Optimization and Decision Making, Operations Research, Transportation problems, Meta-heuristic optimization, Gray Systems, Neural Networks, Logistic and Uncertainty Analysis.