Journal of Operational Research in its Applications
Year:2010 | Volume:7 | Issue:2 (25)|Papers Count:8

The present paper, at first, it calculates effect of internal waves on sound speed and time stability of sound channel specifically at effect zone of waves. Then, it discusses the modal solutions of governing equations on the internal waves with finite differences method and based on numerical ideas extant in normal modes method. Next, it applies the changed sound speed profile and the density in parabolic equations method. Also, it considers conditions of effect of the internal waves on sound speed variations and on the time stability of sound channels. In the end, with comparison of the effect of these waves on the sound propagation, it is analyzed the change of the procedure of acoustic waves simultaneous in diagrams with propagation state without effect of the internal wave which the gotten results confirm the method efficiency.

The essential goal of air traffic control is to allow an efficient flow of aircraft while maintaining safe aircraft operation. However, safety is the primary objective and can never be compromised in any way. Air traffic delays occur when demand for airports or airspace exceeds available capacity. Effects of these delays can be lessened by increasing capacity or by modifying demand. Of course, increasing capacity is an intersecting solution, but it is a long-range option which is done by construction of new airports, expansion of existing airports and new landing procedures. Therefore, a shortterm decision making needs a tactical optimization model to reduce delays at available facilities. In this paper, we present a modeling framework that is a generalization of dynamic network model with uncertain capacities.

A new clustering approach has been recently proposed by Guh et al. in [13] that use data envelopment analysis to cluster a data set. This approach uses the piecewise production functions derived from the DEA method to cluster the data with input and output items. But their alternative algorithm has a practical difficulty because of alternative optimal solutions of DEA models. So, in this paper, the algorithm’s problem will be illustrated and a new algorithm will be suggested to conduct a DEA- based clustering for a group of data, correctly.

The Data Envelopment Analysis models are free to choose the best weights. Because of some problems on freedom of weights on input and output weights, we put some constraints. One kind of weight constraints that proposed to now is symmetric weight constraints. The symmetric weight constraints were explained by Dimitrov and Sutton in 2009 [3] . They decided to remove zero weights from input and output weights DMU under evaluation by new weight constraints that added to input and output oriented models of CCR. Symmetric Weight Assignment Technique which was suggested by Dimitrov and Sutton doesn' t affect on feasibility region of model and thus the model is always feasible. Also let the DMUs to choose symmetric the weights.

In this paper we consider the Variable Returns to Scale (VRS) Data Envelopment Analysis (DEA) model. In a DEA model each Decision Making Unit (DMU) is classified either as efficient or inefficient [1] . Changes are inputs or outputs of any DMU can alter its classification, i.e. an efficient DMU can become inefficient and vice versa. The goal of this paper is to assess changes in inputs and outputs of extreme efficient DMU [11] that will not alter its efficiency status, thus obtaining the region of efficiency for that DMU. The representation of this region is done using an iterative procedure. In the first step an excluded DEA model, whereby a DMU under evaluation is excluded from the reference set, is used. In the iterative part of the procedure, by using optimal simplex tableau we apply parametric programming, obtains optimal solution. Thus to finding dual solutions and solving facet equation and with iterating can move from one facet to the adjacent one and this operations continue until representing all facets for region of efficiency. At the end of the procedure we obtain the complete region of efficiency for a DMU under consideration.

The market price of a good and the quantity produced are determined by the equality between supply and demand. Demand is the amount of a good that consumers are willing and able to buy at a given price. Supply is the amount of good producers is willing and able to sell at a given price. Suppose that demand and supply are linear functions of the price and moreover suppose that that only some imprecise data on the relation between the quantities supplied and requested at a given price are available and that the relation can be naturally described as a fuzzy linear system of equations. Hence for obtaining a solution, it is necessary to solve a fuzzy version of linear system of equations. Here, an application of solving fuzzy linear system of equations in economic is discussed. We first briefly state some fundamental concepts of fuzzy sets theory and economic and then give an example to illustrate the main idea of this study.

In this paper solution of a nxn singular system of linear equations is invetigated. It is shown that any singular linear system is equivalent to an underdetermined linear system. Some numerical example are given to illustrate that the minimum norm solution of satisfies to the other one.

The Hotelling's T2 control chart, is the most widely used multivariate procedure for two or more related quality characteristics, but it’s power lacks the desired performance in detecting small to moderate shifts. Recently, the variable sampling intervals and Control Limits (VSICL) control scheme has been proved to have a very good performance on detecting small to moderate shifts when it is compared to the fixed ratio sampling (FRS) T2 control chart. Moreover, it is shown that the VSICL scheme is more economical than the classical one. This article studies the economic consequences of a new control scheme named variable sample sizes, sampling intervals and control limits (VSSICL) in that the sample size n, sampling interval h and control limit k vary between minimum and maximum values. We apply the cost model proposed by Costa and Rahim (2001). This model considers the cost of false alarms, the cost of finding and repairing an assignable cause, the cost of producing out of control items and the cost of sampling and testing. Furthermore, we assume that the length of time that the process remains in control is exponentially distributed which allows us to apply the Markov chain approach for developing the cost model. We apply genetic algorithm to determine the optimal values of model parameters by minimizing the cost function. Finally, the both VSICL and VSSICL T2 control charts are compared with respect to the expected cost per unit time.