INTERNATIONAL JOURNAL OF INDUSTRIAL MATHEMATICS
Year:2009 | Volume:1 | Issue:1|Papers Count:8

In this paper, we present a method for solving the first kind Abel integral equation.In this method, the first kind Abel integral equation is transformed to the second kind Volterra integral equation with a continuous kernel and a smooth deriving term expressed by weakly singular integrals. By using Sidi's sinm - transformation and modified Navot-Simpson's integration rule, an algorithm for solving this kind of integral equation is proposed, then the convergence of algorithm is derived. Some numerical results show the efficiency of the mentioned method.

The main purpose of this paper is to determine the best performance time period of a system, consisting some DMUs, among some sequential time periods. This aim is satisfied by two proposed algorithms, the first based on global Malmquist Productivity Index and the second is based on PPS frontiers.

In this work, the Homotopy Perturbation Method (HPM) is implemented for finding approximate solution of the Fuzzy Initial Value Problem (FIVP) involving generalized differentiability.This method is based upon homotopy perturbation theory. The comparison of the exact solution with approximate solution obtained by HPM is in detail. The results reveal that the method is very effective and simple.

In this paper we find solution of linear interval equation in dual form based on the generalized procedure of interval extension which is called interval extended zero method.Moreover, proposed method has significant advantage, is that it substantially decreases the excess width effect.

Super efficiency data envelopment analysis (DEA) model can be used in ranking the performance of efficient decision making units (DMUs). In DEA, non-extreme efficient units have a super efficiency score one and the existing super efficiency DEA models do not provide a complete ranking about these units. In this paper, we will propose a method for ranking the performance of the extreme and non-extreme efficient units.

In many real applications, the data of production processes can't be precisely measured.We develop some fuzzy versions of the classical DEA models (in particular, the CCR model) by using some ranking methods based on the comparison of cuts. Our approaches can be seen as an extension of the DEA methodology. The provides users and practitioners with models which represent some real life processes more appropriately. DEA- based Malmquist productivity index measures the productivity change over time. In this paper we provide an extension to the DEA- based Malmquist productivity index for all DMUs with fuzzy data.

All of organizations around the world try to increase competitive ability regards to other similar companies. In this way, decision making processes are one of the most important activities for help them. The multiple criteria decision making methods create for help better decision making in multidimensional environment to monitor organizational resources and, generally, for ranking them and their departments.One of the simplest and applicable methods in multiple criteria decision making methods is SAW method (simple additive weighting method).The general problem in MADM methods is lacking of complementary information for final decision making. In optimizations methods (for example linear programming) the sensitivity analysis are used for produce complementary information and this reason helps for popularity of these methods. Although MADM methods don't belong optimizations methods, but in this paper try to use sensitivity analysis approach for produce complementary information by determination of criteria values domain in decision making matrix.

Satellite image segmentation, as a main step of remotely-sensed image processing, is often accomplished by clustering when ground truth is not available to provide samples to train a supervised classifier. To solve this problem, here we propose a new purposes approach for fuzzy segmentation error reduction fuzzy logic-based algorithms as well as structural information is utilized in our proposed multi-resolution Fuzzy C-Mean (FCM) clustering algorithm. The results show that the multi resolution based FCM can improve the result of the standard FCM for an unsupervised classification approach.