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Showing 2 results for Solid Transportation
Dr. Seyed Hadi Nasseri, Ms. Parastoo Niksefat Dogori, Ms. Gohar Shakouri,
Volume 13, Issue 2 (12-2022)
Abstract
The most convenient models of Solid Transportation (ST) problems have been justly considered a kind of uncertainty in their parameters such as fuzzy, grey, stochastic, etc. and usually, they suggest solving the main problems by solving some crisp equivalent model/models based on their proposed approach such as using ranking functions, embedding problems etc. Furthermore, there exist some shortcomings in formulating the main model for the realistic situations, since it omitted the flexibility conditions in their studies. Hence, to overcome these shortages, we formulate these conditions for the mentioned these problems with fuzzy flexible constraints, where there are no exact predictions for the values of the resources. In particluar, numerical investigation shows that each increasing for the values of the supply and demand is not effective for improving the objective function. The value of the objective function is sensitive when supply and demand change, so we conduct a new study to diversify the value of the objective function, due to changes in resource and demand levels simultaneously.
Babak Khabiri, Majid Iranmanesh,
Volume 14, Issue 1 (6-2023)
Abstract
A transportation problem involving three constraints: source, destination, and conveyance, where all parameters of the problem are fuzzy is called Fully Fuzzy Solid Transportation Problem (FFSTP). In this paper, a new method is proposed to find an optimal solution of an unbalanced FFSTP which the fuzzy numbers are considered to be k-scale trapezoidal fuzzy numbers. The k-scale trapezoidal fuzzy numbers are a generalization of symmetric trapezoidal fuzzy numbers which are considered recently in the literature. In this method, using a new ranking method, we transform the unbalanced FFSTP into a crisp linear programming formulation and find a fuzzy optimal solution for it. The considered model is not necessary balanced and introduced method will solve that without convert it to a balanced model. The advantages of the proposed method are also discussed.
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