William W. Cooper

Bio: William W. Cooper is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Data envelopment analysis & Linear programming. The author has an hindex of 79, co-authored 254 publications receiving 76641 citations. Previous affiliations of William W. Cooper include Harvard University & Carnegie Mellon University.

A note extending Grosskopf, Hayes, Taylor and Weber, "Anticipating the consequences of school reform: A new use of DEA"

Abstract: The article cited in the current paper's title shows how to extend Data Envelopment Analysis (DEA) to evaluate future, as well as past and present, performance. This is accomplished by relaxing some of the constraints that reflect the presence of imposed regulations, and then re-computing the evaluations under these alternate conditions. We here extend this approach to account for “long-run” and “short-run” behaviors, and the related bodies of theory that play prominent roles in microeconomics.

Demon, mark ii: extremal equations solution and approximations.

Abstract: The DEMON model (Decision Mapping Via Optimum GO-NO Networks), a model for marketing new products, is formulated in terms of an extremal equation The latter can be reduced to solution of a separated system of simpler equations which, for discrete distributions, can be solved by linear programming methods The reduction also permits general characteristics of the solutions to be inferred Methods of approximation and bounding are developed and interpreted for the general case

Some problems and models for time-phased transport requirements. Chance constrained programs with normal deviates and linear decision rules

Abstract: This paper discusses the possibilities of analysis of risk and uncertainty in forward planning in time for implementation of transport requirements by means of “chance-constrained” programming models [1]. The possibilities of using chance-constrained programming models, in place of queuing models, and the solution of such a model for the leasing of tanker fleets. is considered in some detail.

Data disclosure and data sharing in scientific research.

Abstract: Data sharing is examined for its bearing on (i) quality assurance and (ii) extensions of results in scientific research as well as (iii) part of a tradition of openness in science. It is suggested that sharing can be accomplished in a simple manner that is also sufficiently flexible to fit varying individual situations by asking authors of data dependent articles and grant proposals to footnote (a) whether they are willing to make their data available to others and, if so, (b) how the data may be accessed. Appendices report results from a survey of current policies and practices in professional societies and in Federal government fund granting agencies. Emphasis is on the social and management sciences.

Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis

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."

Fuzzy Set Theory - and Its Applications

Abstract: Fuzzy Set Theory - And Its Applications, Third Edition is a textbook for courses in fuzzy set theory. It can also be used as an introduction to the subject. The character of a textbook is balanced with the dynamic nature of the research in the field by including many useful references to develop a deeper understanding among interested readers. The book updates the research agenda (which has witnessed profound and startling advances since its inception some 30 years ago) with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. All chapters have been updated. Exercises are included.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software

Abstract: List of Tables. List of Figures. Preface. 1. General Discussion. 2. The Basic CCR Model. 3. The CCR Model and Production Correspondence. 4. Alternative DEA Models. 5. Returns to Scale. 6. Models with Restricted Multipliers. 7. Discretionary, Non-Discretionary and Categorical Variables. 8. Allocation Models. 9. Data Variations. Appendices. Index.