International Journal of Industrial Mathematics
Volume:1 Issue: 1, Winter 2009

In this paper, we present a method for solving the rst kind Abel integral equation. In thismethod, the rst kind Abel integral equation is transformed to the second kind Volterraintegral equation with a continuous kernel and a smooth deriving term expressed by weakly singular integrals. By using Sidi's sinm - transformation and modi-ed 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 eciency of the mentioned method.

The main purpose of this paper is to determine the best performance time period of asystem, consisting some DMUs, among some sequential time periods. This aim is satisedby two proposed algorithms, the rst based on global Malmquist Productivity Index andthe second is based on PPS frontiers.

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

In this paper we nd 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 signicant advantage, is that it substantially decreasesthe excess width eect.

Super eciency data envelopment analysis(DEA) model can be used in ranking the performance of ecient decision making units(DMUs). In DEA, non-extreme ecient unitshave a super eciency score one and the existing super eciency DEA models do notprovide a complete ranking about these units. In this paper, we will propose a methodfor ranking the performance of the extreme and non-extreme ecient units.

In many real applications, the data of production processes cant be precisely measured.We develop some fuzzy versions of the classical DEA models (in particular, the CCRmodel) by using some ranking methods based on the comparison of cuts. Our approachescan 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 othersimilar companies. In this way, decision making processes are one of the most importantactivities for help them. The multiple criteria decision making methods create forhelp better decision making in multidimensional environment to monitor organizationalresources and, generally, for ranking them and their departments.One of the simplest and applicable methods in multiple criteria decision making methodsis SAW method (simple additive weighting method).The general problem in MADM methods is lacking of complementary information for nal decision making. In optimizationsmethods (for example linear programming) the sensitivity analysis are used for producecomplementary information and this reason helps for popularity of these methods. Although MADM methods dont belong optimizations methods, but in this paper try to usesensitivity analysis approach for produce complementary information by determination ofcriteria 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 classier. 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 classication approach.