INTERNATIONAL JOURNAL OF DATA ENVELOPMENT ANALYSIS
Year:2014 | Volume:2 | Issue:2 (6)|Papers Count:7

Data envelopment analysis (DEA) is a nonparametric technique that includes models to evaluate the relative efficiency of Decision Making Units (DMUs). It has the ability to separate efficient units and inefficient units. One of the applications of this mathematical technique is evaluating performance of supply chain. According to such as the inefficiency factors of one DMU, is existence congestion in its inputs and with regard to the economic concept of congestion that is widely visible in the most phenomenon, therefore, cannot ignore its presence in supply chain. In this paper, at first a network DEA model is noted to evaluate the performance of supply chain. Then according to the important role of intermediate products in supply chain, the existence of congestion in this product is investigated, finally, an Inverse DEA model is introduced in order to overcome the problems caused by congestion. So that the amount required input for providing the desired intermediate product can be estimated by that. In this case, no congestion will occur in intermediate products. Finally for improving the efficiency of this unit with new intermediate products an inverse DEA model is proposed.

Control chart is the most well-known chart to monitor the number of nonconformities per inspection unit where each sample consists of constant size. Generally, the design of a control chart requires determination of sample size, sampling interval, and control limits width. Optimally selecting these parameters depends on several process parameters, which have been considered from statistical and/or economic aspects in the literature. This study presents a multi-objective economic-statistical design (MOESD) of the C control chart. An algorithm using data envelopment analysis (DEA) is employed to solve this model. A numerical example is used to illustrate the algorithm procedure.

Data Envelopment Analysis (DEA) is a nonparametric approach for measuring the relative efficiency of a decision making units consists of multiple inputs and outputs. In all standard DEA models semi positive real valued measures are assumed, while in some real cases inputs and outputs may take complex valued. The question is related to measuring efficiency in such cases. As far as we are aware, there is not any special formulation replying the complex-valued questions. The formulation developed in this paper enables one to estimate efficiency when the data are complex valued.

Data envelopment analysis (DEA) is a non-parametric method for assessing relative efficiency of decision-making units (DMUs). Every single decision-maker with the use of inputs produces outputs. These decision-making units will be defined by the production possibility set. Resource allocation to DMUs is one of the concerns of managers since managers can employ the results of this process to allocate resources in organizations appropriately and sufficiently and as a result achieve the lowest cost and highest level of profit. In this paper we will suggest an algorithm to select such efficient decision making units.

With regard to the importance of scientific decision-making in power plants prioritization to produce electrical energy (power) from renewable energies (purified), in this paper, research with specialists opinions with respond to questionnaires provided upon three efficient operational criterion in position of three presented power plant in country which used wind, solar water energies for producing energy, and using group hierarchical analysis multi-criteria decision-making method (AHP), options prioritization performed Achieved results indicates this fact that among three presented criteria, environmental issues are the most important criteria and standards of maintenance costs and also initial investment cost per Kw/h produced electricity (power), in order of importance, are in second place and one the other hand, among three presented power plant in Iran which used renewable energies (new) for producing power, wind power plant placed in first priority and then solar and electrical power plants- water are in the second placed.

In data envelopment analyze (DEA) the scale efficiency in the input-oriented CCR model is less than or equal to the scale efficiency in DEA based on the fractional analysis (DEA-R). Also, the scale efficiency in case of multiple inputs and one output and vice versa the scale efficiencies are equal in DEA and DEA-R. In this paper, first, DEA-R model with weight restrictions when there is relative data is recommended. Second, the scale efficiencies in a constant return to scale are compared with each other in DEA and DEA-R models. At the end, a numerical example is provided for the proposed models with 27DMUs.