In original data envelopment analysis (DEA) models, the data for all inputs and outputs are known exactly. When some inputs and outputs are unknown decision variables, such as interval data, ordinal data, and ratio bounded data, the DEA model is called imprecise DEA (IDEA). In this paper, We develop an alternative approach based upon slacksbased measure of efficiency (SBM) for dealing with inte...

In this paper, we have developed a heuristic that uses Data Envelopment Analysis (DEA) to rank Decision Making Units (DMUs) with fuzzy input and output values in the order of their probable efficiency. The heuristic is designed to be used with the raw samples of the fuzzy data, and does not require knowledge of the fuzzy data’s probability distribution. It is meant to be a simple method of diff...

Data envelopment analysis (DEA) is a linear programming problem approach for evaluating the relative efficiency of peer decision making units (DMUs) that have multiple inputs and outputs. DMUs can have a two-stage structure where all the outputs from the first stage are the only inputs to the second stage, in addition to the inputs to the first stage and the outputs from the second stage. The o...

Many multimedia scheduling algorithms implement fair sharing of the CPU among processes. However, often a share of the CPU does not adequately satisfy the timing constraints of applications such as MPEG video. Several schedulers have been proposed to address this problem; each provides CPU shares but also features innovative uses of virtual time to better support multimedia applications. To giv...

Data envelopment analysis (DEA) is a non-parametric data-driven approach for evaluating the efficiency of set homogeneous decision-making units (DMUs) with multiple inputs and outputs. The number performance factors (inputs outputs) plays crucial role when applying DEA to real-world applications. In other words, if significantly greater than DMUs, it highly possible arrive at large portion effi...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently network DEA models been developed to examine the efficiency of DMUs with internal structures. The internal network structures range from a simple two-stage process to a complex system where multiple divisions are linked together with intermediate measures. In general, there ar...

Data envelopment analysis (DEA) is a mathematical approach to measuring the relative efficiency of peer decision making units (DMUs). It is particularly useful where no a priori information on the tradeoffs or relations among various performance measures is available. However, it is very desirable if “evaluation standards,” when they can be established, be incorporated into DEA performance eval...

assura tegoriz nput w t weigh he DEA ss the es the e effic velope The most popular weight restrictions are to be within certain ranges. ARs can be ca ARI specify bounds on ratios between i bounds on ratios that link input to outpu efficiency, but in the presence of ARII t become infeasible. In this paper we discu pose a new nonlinear model that overcom which enables the assessment of relativ sp...

BACKGROUND How the central nervous system (CNS) organizes the joint dynamics for multi-joint movement is a complex problem, because of the passive interaction among segmental movements. Previous studies have demonstrated that the CNS predictively compensates for interaction torque (INT) which is arising from the movement of the adjacent joints. However, most of these studies have mainly examine...