Data Envelopment Analysis (DEA) cannot provide adequate discrimination among efficient decision making units (DMUs). To discriminate these efficient DMUs is an interesting research subject. The purpose of this paper is to develop the mix integer linear model which was proposed by Foroughi (Foroughi A.A. A new mixed integer linear model for selecting the best decision making units in data envelo...

Coal based fired power plant is a very complex unit. Today’s electric energy is playing an important role in the industrial and all-round development of every country. It is desired form of energy, because it can be produced easily and transmitted economically. Further, it can be used efficiently and easily to domestic and industrial applications. Performance evaluation of such units is becomin...

Disturbances in large-scale industrial systems can easily propagate through the process units and thereby adversely affect the overall process performance. In particular, oscillations in control loops are very common in industrial processes and lead to poor control performance, an excessive energy consumption and deteriorate the product quality. Identifying the propagation path of oscillations ...

Present study seeks the generation of forecasts of number of units, employment, capital formation and production of small scale industrial sector of Punjab for ensuing decade till 2019-20.Forecasts have been generated for the lead time of thirteen years by using Auto Regressive Integrated Moving Average (ARIMA) model, based on identifying the pattern followed by past values of a each variable a...

Today, one of the most fundamental issues within the field of industrial and nonindustrial activities is evaluate the costs performance of the units which are associated with industrial and nonindustrial activities. Data envelopment analysis (DEA) is a nonparametric method for evaluating performance. Fuzzy sets theory is a powerful tool for mentioning ambiguous situations. Traditional DEA model...

This work concentrates on the comparison of two approaches for the design of sensor networks with parameter estimation purposes. After extending a Maximum Precision Model recently published to multiple parameter estimation and binary variables, its equivalence with the Minimum Cost Model is presented. Industrial heat exchanger units are used to illustrate the results.