Abraham Charnes

Bio: Abraham Charnes is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Linear programming & Data envelopment analysis. The author has an hindex of 57, co-authored 222 publications receiving 63459 citations. Previous affiliations of Abraham Charnes include Carnegie Institution for Science & Northwestern University.

A futurological justification for historical cost and multi-dimensional accounting

Abstract: Futurology, interpreted as a forecast or, even better, as projections intended to influence the future, is applied to justify retention of historical costs in the context of proposed multidimensional accounting extensions for business, government and national goals accounting. Budgetary disclosures supported by opportunity costs and tradeoffs are also examined via multiple-objective models of goal programming varieties. Independent audit and attest is suggested not only for the mathematical model but also for managerial explanations of subsequent discrepancies from plans in each pertinent dimension.

Past, Present and Future of Development, Computational Efficiency, and Practical Use of Large Scale Transportation and Transhipment Computer Codes

Abstract: : Three generations of computers have elapsed since the first satisfactory method for solving transportation and transhipment problems was devised. During this time many computational advances have taken place in developing computer codes to solve these problems. The primary purpose of the paper is to summarize these events and to do some crystal ball gazing to provide what we believe to be best estimates of future trends.

Core-stem solutions of n-person essential games

Abstract: A new wide class of internally stable solutions to essential n -person games in characteristic function form, ‘core-stem’ solutions, is herein derived based on the notion of ‘core of a coalition’ and a finite sequence of meta-games (a ‘stem of games’) with a ‘stem-level’ solution for each meta-level. Core-stem solutions exist for every game, can diner according to different stems selected or different stem-level solutions selected. An example with an implausible core and plausible core-stem solution (two different stems) is presented.

On the Explicit Solution of a Special Class of Linear Economic Models

Abstract: This paper derives explicit solutions for a class of linear (inequality) economic models heretofore treated by linear programming. These solutions are quite elementary and the decision criteria given are easily interpreted. The explicit solutions may involve the inversion of a single matrix, but even this is not necessary for the three applications in capital budgeting, production planning, and input-output analysis given in the paper.

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.