Application of Malmquist Productivity Index in Integrated Units of Power Plant

The Malmquist Productivity Index (MPI) evaluates the productivity change of a Decision Making Unit (DMU) between two time periods. DEA considers performance analysis at a given point of time. Classic Malmquist Productivity Index shows regress and progress of a DMU in different periods with efficiency and technology variations without considering the present value of money. In this chapter Application of Malmquist productivity index in integrated units of power plant is discussed. Four units of one of the power plants are assessed & the data of its five successive years are supplied. Also application of Malmquist productivity index (precise data) in Safa Rolling and pipe plants for the time period of 2007 – 2012 is studied.

Productivity Assessment in Data Envelopment Analysis

Malmquist Productivity Index (MPI) is taken into consideration by different researchers in different theoretical and scientific fields after S. Malmquist presented it. This index has a profound meaning and is used in a number of applications for performance evaluation. In literature, there exist variety of subjects consider this index, each of which tries to develop it from different points of view. Here, the aim, in accordance to the importance of this index, is to try gathering most of the issues, related to this subject, from the oldest one to the newest one, in a framework of a review chapter.

Evaluation of Supplier Performance and Efficiency

In the present competitive business environment, it is essential for the management of any organisation to take wise decisions regarding supplier evaluation. It plays a vital role in establishing an effective supply chain for any organisation. Most of the experts agreed that there is no one best way to evaluate the suppliers and different organizations use different approaches for evaluating supplier efficiency. The overall objective of any approach is to reduce purchase risk and maximize overall value to the purchaser. In this paper Data Envelopment Analysis (DEA) technique is developed to evaluate the supplier efficiency for an organisation. DEA is a multifactor productivity technique to measure the relative efficiency of the decision making units. The super efficiency method of DEA provides a way, which indicates the extent to which the efficient suppliers exceed the efficient frontier formed by other efficient suppliers. A case study is undertaken to evaluate the supplier performance and efficiency using DEA approach.

Performance Evaluation of Suppliers with Undesirable Outputs Using DEA

Performance evaluation and selection and ranking of suppliers is very important due to the competitiveness of companies in the present age. The nature of this kind of decision is usually complex and lacks clear structure and many qualitative and quantitative performance criteria such as quality, price, flexibility, and delivery times must be considered to determine the most suitable supplier. Given that in the supplier evaluation may offer undesirable outputs and random limitations, providing a model for evaluating the performance of suppliers is of utmost importance. With regard to the issue of multi-criteria selection of suppliers, one of the most efficient models to choose suppliers is DEA.in this paper to measure the strong performance and development of undesirable output and random limitations concept the SBM model is used.

Ranking Models in Data Envelopment Analysis Technique

Data envelopment analysis as a mathematical technique formulated based on linear programming problems which enables decision makers to evaluate Decision-Making Units (DMUs) with multiple inputs and outputs. One of the important issue in DEA technique which is widely discussed by researchers is ranking efficient units. Since these units are not comparable among each other. Ranking DMUs is an important issue in theory and practice and many applications in this field are performed. Considering the ranking order senior managers try to better guiding the system. In literature there exist different ranking models each of which tries to make improvements in this subject. Many researchers try to make advances in theory of ranking units and overcome the difficulties exist in presented methods. Each of the existing ranking method has its own specialties and advantages. As each of the existing method can be viewed from different aspects, it is possible that somewhat these groups have overlapping with the others.

Evaluation of Faculties by DEA-ANP Hybrid Algorithm of Chapter

One of the fundamental issues facing universities, research centers and institutes of higher education is the absence of an integrated system for performance evaluation. Data Envelopment Analysis (DEA) is a mathematical and management technique for evaluation of Decision Making Units (DMUs) with multiple input and output. The original DEA does not perform full-ranking; instead, it merely provides classification into two groups: efficient and inefficient. Among the available multi-attribute decision-making methods only Analytic Network Process (ANP) can be used to evaluate performance systematically due to the dependencies and feedbacks caused by the mutual effects of the criteria. The DEA-ANP hybrid algorithm, is designed to eliminate the disadvantage of full-ranking in the DEA method, as well as the disadvantage of subjective evaluation in the ANP method. The goal of this chapter is measuring educational and research performance of seventeen faculties, for the academic year 2009-2010, by using the DEA-ANP hybrid algorithm.

Classifying the Usage of Multiple Objective Decision Making Techniques in Data Envelopment Analysis

There exist a wide range of research studies that apply the Multiple Criteria Decision Making (MCDM) techniques in Data Envelopment Analysis (DEA) methodology and vice versa. Also, MCDM is divided into two subsets, Multiobjective Decision Making (MODM) and Multiattribute Decision Making (MADM). Early studies of DEA methodology utilized the MODM concepts and consequently, most studies in the relationships between MCDM and DEA have involved the usage of MODM techniques in DEA. There remains a large volume of papers in this field; yet, none of them classifies this relationship. Hence, in this research the authors focused on classification of this field that is divided into six groups. Then, some papers in each group are selected for consideration.

A Survey on Models and Methods for Preference Voting and Aggregation

A key issue in the preferential voting framework is how voters express their preferences on a set of candidates. In the existing voting systems, each voter selects a subset of candidates and ranks them from most to least preferred. The obtained preference score of each candidate is the weighted sum of votes receives in different places. Thus, one of the most important issues for aggregating preferences rankings is the determination of the weights associated with the different ranking places. To avoid the subjectivity in determining the weights, various models based on Data Envelopment Analysis (DEA) have been appeared in the literature to determine the most favorable weights for each candidate. This work presents a survey on models and methods to assess the weights in voting systems. The existing voting systems are divided into two areas. In the first area it is assumed that the votes of all the voters to have equal importance and in the second area voters are classifies into different groups and assumed that each group is assigned a different voting power. In this contribution, some of the most common models and procedures for determining the most favorable weights for each candidate are analyzed.