A micro level study of an Indian electric utility for efficiency enhancement
TL;DR: In this paper, a non-parametric approach to frontier analysis is applied to evaluate the relative performance of 29 electricity distribution divisions of an Indian hilly state, namely Uttarakhand.
About: This article is published in Energy.The article was published on 2010-10-01. It has received 50 citations till now. The article focuses on the topics: Data envelopment analysis & Inefficiency.
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TL;DR: In this article, the authors summarized previous research efforts on Data Envelopment Analysis (DEA) applied to energy and environment in the past four decades, including concepts and methodologies on DEA environmental assessment.
358 citations
TL;DR: In this paper, the authors proposed a new use of DEA (Data Envelopment Analysis) as a methodology for unified (operational and environmental) assessment, which separates outputs into desirable and undesirable categories.
118 citations
TL;DR: In this article, the authors used undesirable output-oriented data envelopment analysis (DEA) models to study the CO2 (Carbon dioxide) emissions performance of the transport sector throughout China's 30 administrative regions.
99 citations
TL;DR: In this article, the determinants of energy efficiency in 32 power electric generation management companies over the period 2005-2009 were analyzed using non-parametric Data Envelopment Analysis (DEA) to estimate the relative technical efficiency and productivity change.
77 citations
TL;DR: Diaz-Maurin et al. as discussed by the authors applied quantitative decision-making approaches to compare the same fossil fuel (coal) power plants with nuclear power plants, and the DEA (data envelopment analysis) and SAW (simple additive weighting) are the methods applied.
55 citations
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TL;DR: A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs and methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs.
25,433 citations
TL;DR: The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs as mentioned in this paper.
Abstract: In management contexts, mathematical programming is usually used to evaluate a collection of possible alternative courses of action en route to selecting one which is best. In this capacity, mathematical programming serves as a planning aid to management. Data Envelopment Analysis reverses this role and employs mathematical programming to obtain ex post facto evaluations of the relative efficiency of management accomplishments, however they may have been planned or executed. Mathematical programming is thereby extended for use as a tool for control and evaluation of past accomplishments as well as a tool to aid in planning future activities. The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs. A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data. Technical inefficiencies are identified with failures to achieve best possible output levels and/or usage of excessive amounts of inputs. Methods for identifying and correcting the magnitudes of these inefficiencies, as supplied in prior work, are illustrated. In the present paper, a new separate variable is introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decreasing returns to scale in multiple input and multiple output situations. The results are discussed and related not only to classical single output economics but also to more modern versions of economics which are identified with "contestable market theories."
14,941 citations
Book•
01 Jan 2007
TL;DR: This chapter discusses Deterministic Dynamic Programming, a model for nonlinear programming, and nonlinear Programming Algorithms, a system for solving linear programming problems.
Abstract: 1. Overview of Operations Research. I. DETERMINISTIC MODELS. 2. Introduction to Linear Programming. 3. The Simplex Method. 4. Duality and Sensitivity Analysis. 5. Transportation Model and Its Variants. 6. Network Models. 7. Advanced Linear Programming. 8. Goal Programming. 9. Integer Linear Programming. 10. Deterministic Dynamic Programming. 11. Deterministic Inventory Models. II. PROBABILISTIC MODELS. 12. Review of Basic Probability. 13. Forecasting Models. 14. Decision Analysis and Games. 15. Probabilistic Dynamic Programming. 16. Probabilistic Inventory Models. 17. Queueing Systems. 18. Simulation Modeling. 19. Markovian Decision Process. III. NONLINEAR MODELS. 20. Classical Optimization Theory. 21. Nonlinear Programming Algorithms. Appendix A: Review of Matrix Algebra. Appendix B: Introduction to Simnet II. Appendix C: Tora and Simnet II Installation and Execution. Appendix D: Statistical Tables. Appendix E: Answers to Odd-Numbered Problems. Index.
1,819 citations
TL;DR: A systematic application procedure of the DEA methodology in its various stages is suggested, focused on the selection of 'decision making units' (DMUs) to enter the analysis as well as the choice and screening of factors.
Abstract: Data Envelopment Analysis (DEA) has become an accepted approach for assessing efficiency in a wide range of cases. The present paper suggests a systematic application procedure of the DEA methodology in its various stages. Attention is focused on the selection of ‘decision making units’ (DMUs) to enter the analysis as well as the choice and screening of factors. The application of several DEA models (in different versions and formulations) is demonstrated, in the process of determining relative efficiencies within the compared DMUs.
1,280 citations