3 edition of Linear programming for financial planning under uncertainty found in the catalog.
Published
1969
by M.I.T. in Cambridge
.
Written in
Edition Notes
| Statement | [by] Stewart C. Myers. |
| Series | M.I.T. Alfred P. Sloan School of Management. Working papers -- no. 387-69, Working paper (Sloan School of Management) -- 387-69. |
| The Physical Object | |
|---|---|
| Pagination | 34 leaves |
| Number of Pages | 34 |
| ID Numbers | |
| Open Library | OL18078095M |
| OCLC/WorldCa | 14403147 |
In this study, we introduce a robust linear programming approach for water and environmental decision-making under uncertainty. This approach is of significant practical utility to decision makers for obtaining reliable and robust management decisions that are “immune” to the uncertainty attributable to data perturbations. The immunization guarantees that the chosen robust management plan Cited by: Planning under uncertainty Professors George Dantzig and Gerd Infanger have a special interest in developing methods and software for stochastic linear programming. This SOL research program concerns techniques for solving mathematical models of decision problems whose parameters (coefficients, right-hand sides) are not known with certainty but.
Stochastic programming is the study of procedures for decision making under uncertainty over time. The uncertainty can be in the model's parameters or in the model itself. Parameters may be uncertain because of lack of reliable data, measurement errors, future and unobservable events, etc. (version J ) This list of books on Stochastic Programming was compiled by J. Dupacová (Charles University, Prague), and first appeared in the state-of-the-art volume Annals of OR 85 (), edited by R. J-B. Wets and W. T. Ziemba.. Books and collections of papers on Stochastic Programming, primary classification 90C15 A. The known ones ~ in English, including translations.
Scheduling Project Crashing Time using Linear Programming Technique Omar M. Elmabrouk. modeling structures that have been of great value in analyzing extended planning horizon project time-cost crashes Project scheduling under uncertainty by using survey and research potentials was carried out. In that survey they. More editions of Linear programming for financial planning under uncertainty: Linear programming for financial planning under uncertainty: ISBN ( .
Practical inplications of the model are discussed in the third section. THE LINEAR FORMAT FOR FINANCIAL PLANNING Introduction We will consider the firms financial planning problem in the following terms.
(Typographical errors above are due to OCR software and don't occur in the book.) About the PublisherAuthor: Stewart C. Myers. texts All Books All Texts latest This Just In Smithsonian Libraries FEDLINK Linear programming for financial planning under uncertainty Item Preview remove-circle Linear programming for financial planning under uncertainty by Myers, Stewart C.
Publication date Pages: rwords,afinancingorinvestmentoptionwith uncertainreturnsdoesnothaveanuncertainvalue;the"market's"pref- erencesarewell-defined.2 Third.
choolofmanagement linearprogrammingforfinancialplanning underuncertainty fi^aa massachusetts instituteoftechnology. Open Library is an open, editable library catalog, building towards a web page for every book ever published.
A linear programming model for short term financial planning under uncertainty by G. Pogue,M.I.T.] edition, in EnglishPages: Full text of "Linear programming for financial planning under uncertainty" See other formats V T, iw,'fr>K^^ /i, V--', LINEAR PROGRAMMING FOR FINANCIAL PLANNING UNDER UNCERTAINTY (Revised) Stewart C.
>fyers May Not to be reproduced in whole or in part without the author's permission. This paper presents a stochastic linear programming formulation of a firm's short term financial planning problem. This framework allows a more realistic representation of the uncertainties fundamental to this problem than previous by: Enter the password to open this PDF file: Cancel OK.
File name:. This chapter originally appeared in Management Science, April–JulyVol. 1, Nos. 3 and 4, pp. –, published by The Institute of Management Sciences. This article was also reprinted in Cited by: Linear Programming Under Uncertainty. This chapter originally appeared in Management Science, April–JulyVol.
1, Nos. 3 and 4, pp. –, published by The Institute of Management : George B. Dantzig. Sensitivity Analysis and Uncertainty in Linear Programming Thispaperwasrefereed.
Linear programming (LP) is one of the great successes to emerge from operations research and management science. It is well developed and widely used.
LP problems in practice are in planning under uncertainty, it is critical to prop-File Size: KB. Thus, a bank's financial goal of maximizing returns to shareholders through maximizing profits can be translated into the operational goal of achieving some target end-of-period balance sheet position producing the greatest profits.
This paper describes a successful application of linear programming for assisting the management of. Planning Under Uncertainty: Solving Large-Scale Stochastic Linear Programs presents a new approach devised by George Dantzig, P.
Glynn, and Gerd Infanger. This approach makes it possible to solve large-scale stochastic linear problems with numerous stochastic by: Stochastic mixed-integer linear programming Dealing with end effects in multistage planning Financial Planning Model.
Operations Research 46 () C. Dillenberger, L.F. Escudero, A. Wollensak, W. Zhang. On practical resource allocation for production Optimization under uncertainty: modeling and solution methods.
Thus, a bank's financial goal of maximizing returns to shareholders through maximizing profits can be translated into the operational goal of achieving some target end-of-period balance sheet position producing the greatest profits. This paper describes a successful application of linear programming for assisting the management Cited by: 7.
SHORT TERM FINANCIAL PLANNING UNDER UNCERTAINTY* J. KALLBERG,t R. WHITEt AND W. ZIEMBA. This paper presents a stochastic linear programming formulation of a firm's short term financial planning problem. This framework allows a more realistic representation of the uncertainties fundamental to this problem than previous models.
Filling the need for an introductory book on linear programming that discusses the important ways to mitigate parameter uncertainty, Introduction to Linear Optimization and Extensions with MATLAB® provides a concrete and intuitive yet rigorous introduction to modern linear optimization.
In this paper, a mixed integer linear programming (MILP) formulation is proposed that integrates financial risk measures into the design and planning of closed-loop supply chains, considering. Downloadable. This paper presents a stochastic linear programming formulation of a firm's short term financial planning problem.
This framework allows a more realistic representation of the uncertainties fundamental to this problem than previous models. In addition, using Wets's algorithm for linear simple recourse problems, this formulation has approximately the same computational complexity. This paper proposes a new methodology to include financial risk management in the framework of two-stage stochastic programming for energy planning under uncertainties in demand and fuel price.
A deterministic mixed integer linear programming formulation is extended to a two-stage stochastic programming model in order to take into account Cited by:. Risk Management for a Global Supply Chain Planning under Uncertainty: Models and Algorithms.
Fengqi You 1, John M. Wassick2, Ignacio E. Grossmann1* 1 Dept. of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 2 The Dow Chemical Company, Midland, MI. Planning Under Uncertainty by Gerd Infanger,The method hs been successfully tested on facility expansions and financial planning problems.
In one case a problem whose deterministic equivalant would appear as a linear programming problem with approxiamately 10/27 constraints and variables was solved on a laptop : Gerd Infanger.The crop planning problem is often formulated as a linear programming problem.
But, in many actual cases, the profit coefficients for agricultural products are not certain values because of the influence of the future weather, so a linear programming model with constant coefficients does not describe the environment of decision making by:
