Accession Number : AD0714804

Title :   Chance-Constrained Programming and Related Approaches to Risk Control in Capital Budgeting.

Descriptive Note : Doctoral thesis,

Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA MANAGEMENT SCIENCES RESEARCH GROUP

Personal Author(s) : Byrne,Robert Francis

Report Date : JUN 1968

Pagination or Media Count : 279

Abstract : The report explores a group of approaches to risk control in the capital budgeting process. The specific meaning of risk in the capital investment decision is examined. Models are developed by incorporating risk control measures which are common business practice (particularly the 'payback' method) with some of the recent developments in mathematical programming. Specific models are developed to illustrate methods of dealing with two of the major risk elements in the capital budgeting risks in the sense of insufficient liquidity. In particular, the stochastic nature of the cash flows generated by a project is dealt with by the methodologies of Chance-Constrained Programming and Linear Programming Under Uncertainty (LPUU). A model is developed for the case in which the cash flows are assumed to be normally distributed. A model is also developed where the cash flows are described by arbitrary discrete distributions. The applicability of goemetric programming as a solution method for the discrete model is evaluated. An integer linear programming model is developed by a transformation of the geometric programming model, and its properties and interpretations are investigated. The dual to this model is found to offer significant insights into the problem, with particular reference to the effects of controlling risk elements on a portfolio basis in contrast with the common practice of controlling risks on an individual project basis. (Author)

Descriptors :   (*MANAGEMENT PLANNING AND CONTROL, DECISION MAKING), (*LINEAR PROGRAMMING, STOCHASTIC PROCESSES), PROBABILITY, MATHEMATICAL MODELS, PROBABILITY DENSITY FUNCTIONS, MATRICES(MATHEMATICS), COSTS, MONEY, RANDOM VARIABLES, DISTRIBUTION FUNCTIONS, THESES

Subject Categories : Administration and Management
      Operations Research

Distribution Statement : APPROVED FOR PUBLIC RELEASE