Showing papers by "William W. Cooper published in 1989"
TL;DR: A cone ratio data envelopment analysis (DEA) model that substantially generalizes the Charnes-Cooper-Rhodes (CCR) model and characterizes its efficiency classes is developed and studied as discussed by the authors.
Abstract: A new ‘cone ratio’ data envelopment analysis (DEA) model that substantially generalizes the Charnes-Cooper-Rhodes (CCR) model and the Charnes-Cooper-Thrall approach characterizing its efficiency classes is developed and studied. It allows for infinitely many decision-making units (DM Us) and arbitrary closed convex cones for the virtual multipliers as well as the cone of positivily of the vectors involved. Generalizations of linear programming and polar cone equalizations arc the analytical vehicles employed.
445 citations
TL;DR: In this paper, the use of DEA (data envelopment analysis) as a tool for possible use in evaluating and planning the economic performance of China's cities (28 in all) which play a critical role in the government's program of economic development.
Abstract: This paper studies the use of DEA (data envelopment analysis) as a tool for possible use in evaluating and planning the economic performance of China's cities (28 in all) which play a critical role in the government's program of economic development. DEA promises advantages which include the absence of any need for the assignment of weights on an a priori basis (to reflect the supposed relative importance of various outputs or inputs) when evaluating technical efficiency. It is also unnecessary to explicitly specify underlying functions that are intended to prescribe the analytical form of the relations between inputs and outputs. Finally, as is illustrated in the paper, DEA can be used to identify sources, and estimate amounts of inefficiencies in each city's performance as well as to identify returns-to-scale possibilities in ways that seem well-suited to the mixture of centralized and decentralized planning and performance that China is currently trying to use.
227 citations
TL;DR: In this paper, a constrained game formulation for DEA (Data Envelopment Analysis) which extends the original (unconstrained) game formulations of R. Banker is presented.
76 citations
01 Dec 1989
TL;DR: Data envelopment analysis (DEA) as discussed by the authors is a generalization of the usual scientific-engineering efficiency valuation of a single input, single output system as the ratio of the output input (in the same physical measure, e.g., energy) to multi-input, multi-output systems (or organizations or production units) without known physical laws or the same measure for all inputs and outputs.
Abstract: : Data Envelopment Analysis (DEA) began in generalization of the usual scientific-engineering efficiency valuation of a single input, single output system as the ratio of the output input (in the same physical measure, e.g., energy) to multi-input, multi-output systems (or organizations or production units) without known physical laws or the same measure for all inputs and outputs. This was accomplished by (i) reduction of the multi-inputs and outputs to single virtual inputs and outputs, (ii) replacing absolute efficiency by efficiency relative to all members of a sample of units (called DMU's) having the same inputs and outputs, (iii) evaluating a unit's relative efficiency as the maximum of the ratio of its virtual output to virtual input subject to virtual outputs being less than or equal to virtual inputs for each (all) of the DMU's. The origin, history, current status and problems of Data Envelopment Analysis (DEA) on empirical multi-input, multi-output data are surveyed in relation to efficiency valuation, production function determination and stochastic frontier estimation.
1 citations