فهرست مطالب m. b. aryanezhad

In this paper, the portfolio selection problem is considered, where fuzziness and randomness appear simultaneously in optimization process. Since return and dividend play an important role in such problems, a new model is developed in a mixed environment by incorporating fuzzy random variable as multi-objective nonlinear model. Then a novel interactive approach is proposed to determine the preferred solution. Finally a numerical example is presented to illustrate the proposed model.

Occurrence of chaotic behaviors because of fluctuations creation and their intensified transfer along the chain is one of the main causes of inefficiency of Material Requirements Planning (MRP) based supply chains. One of the reasons of the inefficiency is the lumpiness phenomenon. Through this phenomenon, some parts have an irregular production schedule, with nothing produced in some periods and large batches released in other periods. In this paper it is tried via the mathematical models to analyze this phenomenon. Using the mathematical deduction it is shown that the inventory fluctuations in upper levels of Bill of Material (BOM) in creation of lumpiness phenomenon are more effective than the inferior levels.

In today’s dynamic market, organizations must be adaptive to market fluctuations. In addition, studies show that material-handling cost makes up between 20 and 50 percent of the total operating cost. Therefore, this paper considers the problem of arranging and rearranging, when there are changes in product mix and demand, manufacturing facilities such that the sum of material handling and rearrangement costs is minimized. This problem is called the dynamic plant layout problem (DPLP). In this paper, the authors develop a multi-start simulated annealing for DPLP. To compare the performance of meta-heuristics, data sets taken from literature are used in the comparison.

In this paper, the researchers present a multi-objective linear fractional inventory model using fuzzy pro-gramming. The proposed method in this paper is applied to a problem with two objective functions. The re-sulted fuzzy model is transformed into an ordinary linear programming. The implementation of the develop-ing model presented in this paper is demonstrated through the use of some numerical examples.

Data Envelopment Analysis (DEA) is a technique used to compare efficiency in various sectors such as hospitals, chain stores, and dealerships. It represents a set of linear programming techniques and uses deter-ministic data (inputs and outputs), in stable conditions. The DEA technique cannot be used when there is data with indeterministic nature, or when there is an environment with dynamic conditions. To address this prob-lem, DEA models can be developed based on linear programming in fuzzy environments (Fuzzy DEA).Most Fuzzy DEA models introduced in the literature are parametric models based on alpha cuts. However, the model introduced in this study is non-parametric and uses fuzzy L-R numbers. From the theory point of view, the objective of this study is to develop a simple and effective Fuzzy DEA model. From the practicality point of view, this model can be applied to assess many issues associated with qualitative factors.