Sharif Digital Repository

To solve a fuzzy optimization problem, we need to compare fuzzy numbers. Here, we make use of our recently proposed modified Kerre’s method as an effective approach for comparison of LR fuzzy numbers. Using our new results on LR fuzzy numbers, we show that to compare two LR fuzzy numbers, we do not need to compute the fuzzy maximum of two numbers directly. We propose a new variable neighborhood search approach for solving fuzzy number quadratic programming problems by using the modified Kerre’s method. In our algorithm, a local search is performed using descent directions, found by solving five crisp mathematical programming problems. In several available methods, a fuzzy optimization... 

In this paper we consider a multiple products inventory location problem with disruption. Disruptions may occur in any supply chain due to several reasons such as earthquakes, floods, fires, labor strikes, terrorist attacks, equipment breakdowns and economic crises. We develop a model by assuming that facilities may fail partially in case of disruption and considering substitutable products as a strategy to mitigate the risk of disruption. The developed model is a non-linear integer programming model and NP-Hard. To solve the model, a hybrid algorithm including Tabu Search (TS) and Variable Neighborhood Search (VNS) is proposed. Numerical results show reasonable performance of the algorithm... 

We want to solve fuzzy linear programming problems with fuzzy coefficients in the objective function using a new variable neighborhood search algorithm. In our proposed algorithm, the local search is defined based on descent directions. We make use of our recently proposed modified Kerre's method for finding descent directions. The fuzzy optimization problem is solved directly, without changing it to a crisp program. We show the effectiveness of our proposed method in comparison with some available methods by using a non-parametric statistical sign test. The objective function values obtained by our proposed method turn to be more accurate the ones obtained by other methods. © 2018 IEEE  

The Burr III distribution properly approximates many familiar distributions such as Normal, Lognormal, Gamma, Weibull, and Exponential distributions. It plays an important role in reliability engineering, statistical quality control, and risk analysis models. The Burr III distribution has four parameters known as location, scale, and two shape parameters. The estimation process of these parameters is controversial. Although the maximum likelihood estimation (MLE) is understood as a straightforward method in parameters estimation, using MLE to estimate the Burr III parameters leads to maximize a complicated function with four unknown variables, where using a conventional optimization such as...