INTERNATIONAL JOURNAL OF INDUSTRIAL MATHEMATICS
Year:2014 | Volume:6 | Issue:1|Papers Count:10

Data Envelopment Analysis (DEA) is a branch of management, concerned with evaluating the performances of homogeneous Decision Making Units (DMUs). The performances of DMUs are affected by the amount of sources that DMUs used. Usually increases in inputs cause increases in outputs.However, there are situations where increase in one or more inputs generate a reduction in one or more outputs. In such situations there is congestion in inputs or production process. In this study, we review the approaches that are available in the DEA literature for evaluating congestion. Also we introduce a model to compute output losses due to congestion. Then, we present the results of the mentioned models on an empirical example and interpret the results.

In this paper, a new method is proposed to find the fuzzy optimal solution of fully fuzzy linear programming (abbreviated to FFLP) problems. Also, we employ linear programming (LP) with equality constraints to find a non-negative fuzzy number vector x˜ whic h satisfies A˜x˜=b˜, where A˜ is a fuzzy number matrix. Then we investigate the existence of a positive solution of fully fuzzy linear system (FFLS).

This paper presents a comparison between variational iteration method (VIM) and modified variational iteration method (MVIM) for approximate solution a system of Volterra integral equation of the first kind. We convert a system of Volterra integral equations to a system of Volterra integro differential equations that use VIM and MVIM to approximate solution of this system and hence obtain an approximation for system of Volterra integral equations. Some examples are given to show the pertinent features of this methods.

Consider the random walk among N places with N (N-1)/2 transports. We attach an exponential random variable Xij to each transport between places Pi and Pj and take these random variables mutually independent. If transports are possible or impossible independently with probability p and 1-p, respectively, then we give a lower bound for the distribution function of the smallest path at point log N as Np is large.

In this paper, we present an efficient method for determining the solution of the stochastic second kind Volterra integral equations (SVIE) by using the Taylor expansion method. This method transforms the SVIE to a linear stochastic ordinary differential equation which needs specified boundary conditions. For determining boundary conditions, we use the integration technique. This technique gives an approximate simple and closed form solution for the SVIE. Expectation of the approximating process is computed. Some numerical examples are used to illustrate the accuracy of the method.

Multi objective quadratic fractional programming (MOQFP) problem involves optimization of several objective functions in the form of a ratio of numerator and denominator functions which involve both contains linear and quadratic forms with the assumption that the set of feasible solutions is a convex polyhedral with a finite number of extreme points and the denominator part of each of the objective functions is non-zero in the constraint set. In this paper, we extend the procedure as suggested by Lachhwani (Proc. Nat. Acad. Sci. India, 82 (4), 317-322) based on fuzzy goal programming approach for the solution of multi objective quadratic fractional programming (MOQFP) problem.The proposed technique is simple, efficient and requires less computational work. In the proposed FGP model formulation, corresponding objectives of equivalent multi objective programming problem are transformed into fuzzy goals (membership functions) by means of assigning an aspiration level to each of them and suitable membership function is de fined for each objectives. Then achievement of the highest membership value of each of fuzzy goals is formulated by minimizing the sum of negative deviational variables. The proposed methodology is illustrated with numerical example in order to support the proposed methodology.

In this paper, at first we mention to some results related to PI and vertex Co-PI indices and then we introduce the edge versions of Co-PI indices. Then, we obtain some properties about these new indices.

Since data envelopment analysis (DEA) introduced in 1970s, it has been widely applied to measure the efficiency of a wide variety of production and operation systems. Recently DEA has been extended to examine the efficiency of decision making units (DMUs) with two-stage network structures or processes, where the outputs from the first stage are intermediate measures that make up the inputs of the second stage. Many researchers developed several DEA based models for evaluating the efficiencies of such systems. This paper considers evaluation of the general two-stage network structures, while each stage may produce undesirable output, in addition to desirable ones. The developed model is applied to Green Hen poultry chain in Guilan province, Iran.

In this paper we give conditions for a tuple of commutative bounded linear operators which holds in the property of the Hypercyclicity Criterion. We characterize topological transitivity and semi-hereiditarily hypercyclicity of a dynamical system given by an n-tuple of operators acting on a separable infinite dimensional Banach space X.

Silicon thin layers are deposited on glass substrates with the thickness of 103 nm, 147 nm and 197 nm. The layers are produced with electron gun evaporation method under ultra-high vacuum condition. The optical Reflectance and the Transmittance of produced layers were measured by using spectrophotometer. The optical functions such as, real and imaginary part of refractive index, real and imaginary part of dielectric constant, real and imaginary part of conductivity, absorption coefficient and optical band gap energy are calculated basing on the Kramers-Kronig relations. The void fractions of the silicon films are calculated by using Aspnes theorem. The effect of layer thickness on optical properties of silicon thin films is investigated.