Microsoft Excel sensitivity analysis for linear and stochastic program feed formulation

: So far the study of stochastic programs with recourse has been limited to the case (called by G. Dantzig programming under uncertainty) when only the right-hand sides or resources of the problem are random. In this paper the authors extend the theory to the general case when essentially all the parameters involved are random. This generalization immediately raises the problem of attributing a precise meaning to the stochastic constraints. They examine a probability formulation (satisfying the constraints almost surely) and a possibility formulation (satisfying the constraints for all values of the random parameters in the support of their joint distribution) and show them equivalent under a rather weak but curious W-condition. Finally, they prove that without restriction the equivalent deterministic form of a stochastic program with recourse is a convex program for which we obtain some additional properties when some of the parameters of the original problem are constant. The applications of the theoretical results of this paper to certain classes of stochastic programs which have arisen from practical problems will be presented in a separate paper: 'Stochastic Programs with Recourse: Special Forms.' (Author)

Engineering Optimization Theory and Practice

The study of making the best decision under risk management in a variety of areas of our lives is known as Stochastic Programming. We will go through two-stage Stochastic Linear Programming approaches for a variety of real-world choice issues, as well as how to solve them. We will achieve this by constructing stochastic linear programming models based on real-world situations like the well-known Farmer's situation and News Vendors problems. The influence of pricing, Stochastic Integer Linear Programming problem, second stage Stochastic Integer Linear Programming problem, first stage Stochastic Binary Linear Programming problem, risk aversion problem, and continuous function for random variables based on two-stage SLP with the aid of Farmer's problem will all be examined. We will address the Newsvendor’s problem with Deterministic Equivalent Stochastic Linear Programming, an extension of Deterministic Stochastic Linear Programming for risk aversion with a high number of decision variables and restrictions, utilizing the two-stage Stochastic Linear Programming approach once more. Hand calculation is a challenging way to acquire the solution to the problems. As a result, we will use the programming language AMPL to design computer solutions for tackling both farmer and newsvendor difficulties. We will also utilize MATLAB to create graphs for the farmer's problem's continuous function. Dhaka Univ. J. Sci. 72(1): 30-45, 2024 (January)

An Adjustable Nutrient Margin of Safety Comparison Using Linear and Stochastic Programming in an Excel Spreadsheet

Linear programming has received much attention as a tool for managerial decision making in business and government. It provides the decision maker with a precise and simple framework for defining his problem and a quick and simple means of obtaining an optimal solution to that problem. In addition, linear programming has the advantages of being applicable to a wide range of managerial problems, being able to consider simultaneously each of the multiple goals and relationships which may be represented by a complex problem, and providing information about the relative significance of each constraint with respect to the objective (shadow prices). However, because of the simplicity inherent in the linear programming model, there are many managerial problems to which it cannot readily be applied. Such problems may be too complex for a modeling approach, or may contain relationships which cannot be represented by linear functions, or may be characterized by probabilistic relationships or perhaps relationships which change over time. Most managers could probably cite which the simple linear programming model would be unable to consider if applied to problems in their area of authority. To cope with complexities of this sort, several variations and extensions of linear programming have been developed. Quadratic programming and integer programming have been developed to attack problems of nonlinearity of functional relationships. Stochastic programming deals with problems containing probabilistic relationships. Dynamic programming can be adapted to problems in which time or the sequence of events is an important consideration. However, it can be stated that, in general, what these techniques contribute in terms of more accurate representation of actual problems is often sacrificed in the form of great difficulty in reaching an optimal solution. This paper concentrates upon a particular class of extensions of the traditional linear programming model-those problems containing integer variables which are restricted to a value of either zero or one. These variables are referred to as variables, and the formulation and solution of problems containing such variables is referred to as programming. Dichotomous-integer variables generally indicate discrete changes in objective or constraint functions, or the presence or absence of some condition or decision. Several well-known linear and integer programming problems are presented in this paper and then extended by means of adding dichotomousinteger variables representing additional considerations or complicating factors which are typical of many real world problems. The purpose of the paper is to demonstrate that integer programming, particularly dichotomous-integer programming, provides a powerful means of * Assistant professor of accounting at the College of Business Administration, University of Texas at Austin.

This paper gives an algorithm for L-shaped linear programs which arise naturally in optimal control problems with state constraints and stochastic linear programs (which can be represented in this form with an infinite number of linear constraints). The first section describes a cutting hyperplane algorithm which is shown to be equivalent to a partial decomposition algorithm of the dual program. The two last sections are devoted to applications of the cutting hyperplane algorithm to a linear optimal control problem and stochastic programming problems.

Chapter 27 - Stochastic programming

On stochastic programming I. Static linear programming under risk

I. The General Framework.- 1. Multiobjective programming under uncertainty : scope and goals of the book.- 2. Multiobjective programming : basic concepts and approaches.- 3. Stochastic programming : numerical solution techniques by semi-stochastic approximation methods.- 4. Fuzzy programming : a survey of recent developments.- II. The Stochastic Approach.- 1. Overview of different approaches for solving stochastic programming problems with multiple objective functions.- 2. "STRANGE" : an interactive method for multiobjective stochastic linear programming, and "STRANGE-MOMIX" : its extension to integer variables.- 3. Application of STRANGE to energy studies.- 4. Multiobjective stochastic linear programming with incomplete information : a general methodology.- 5. Computation of efficient solutions of stochastic optimization problems with applications to regression and scenario analysis.- III. The Fuzzy Approach.- 1. Interactive decision-making for multiobjective programming problems with fuzzy parameters.- 2. A possibilistic approach for multiobjective programming problems. Efficiency of solutions.- 3. "FLIP" : an interactive method for multiobjective linear programming with fuzzy coefficients.- 4. Application of "FLIP" method to farm structure optimization under uncertainty.- 5. "FULPAL" : an interactive method for solving (multiobjective) fuzzy linear programming problems.- 6. Multiple objective linear programming problems in the presence of fuzzy coefficients.- 7. Inequality constraints between fuzzy numbers and their use in mathematical programming.- 8. Using fuzzy logic with linguistic quantifiers in multiobjective decision making and optimization: A step towards more human-consistent models.- IV. Stochastic Versus Fuzzy Approaches and Related Issues.- 1. Stochastic versus possibilistic multiobjective programming.- 2. A comparison study of "STRANGE" and "FLIP".- 3. Multiobjective mathematical programming with inexact data.

It is shown that solutions of linear inequalities, linear programs and certain linear complementarity problems (e.g. those with P-matrices or Z-matrices but not semidefinite matrices) are Lipschitz continuous with respect to changes in the right-hand side data of the problem. Solutions of linear programs are not Lipschitz continuous with respect to the coefficients of the objective function. The Lipschitz constant given here is a generalization of the role played by the norm of the inverse of a nonsingular matrix in bounding the perturbation of the solution of a system of equations in terms of a right-hand side perturbation.

Many planning problems involve choosing a set of optimal decisions for a system in the face of uncertainty of elements that may play a central role in the way the system is analyzed and operated. During the past decade, there has been a renewed interest in the modelling, analysis, and solution of such problems due to a remarkable development of both new theoretical results and novel computational techniques in stochastic optimization. A prominent approach is to develop upper and lower bounding approximations to the problem along with procedures to sharpen bounds until an acceptable tolerance is satisfied. The contributions of this dissertation are concerned with the latter approach. The thesis first studies the stochastic linear programming problem with randomness in both the objective coefficients and the constraints. A convex concave saddle property of the value function is utilized to derive new bounding techniques which generalize previously known results. These approximations require discretizing bounded domains of the random variables in such a way that tight upper and lower bounds result. Such techniques will prove attractive with the recent advances in large scale linear programming. The above results are also extended to obtain new upper and lower bounds when the domains of random variables are unbounded. While these bounds are tight, the approximating models are large-scale deterministic linear programs. In particular, with a proposed order-cone decomposition for the domains, these linear programs are well-structured, thus enabling one to use efficient techniques for solution, such as parallel computation. The thesis next considers convex stochastic programs. Using aggregation concepts from the deterministic literature, new bounds are developed for the problem which are computable using standard convex programming algorithms. Finally, the discussion is focused on a stochastic convex program arising in a certain resource allocation problem. Exploiting the problem structure, bounds are developed via the Karush-Kuhn-Tucker conditions. Rather than discretizing domains, these approximations advocate replacing difficult multidimensional integrals by a series of simple univariate integrals. Such practice allows one to preserve differentiability properties so that smooth convex programming methods can be applied for solution.

For many of us, modern-day linear programming (LP) started with the work of George Dantzig in 1947. However, it must be said that many other scientists have also made seminal contributions to the subject, and some would argue that the origins of LP predate Dantzig’s contribution. It is matter open to debate [36]. However, what is not open to debate is Dantzig’s key contribution to LP computation. In contrast to the economists of his time, Dantzig viewed LP not just as a qualitative tool in the analysis of economic phenomena, but as a method that could be used to compute actual answers to specific real-world problems. Consistent with that view, he proposed an algorithm for solving LPs, the simplex algorithm [12]. To this day the simplex algorithm remains a primary computational tool in linear and mixed-integer programming (MIP). In [11] it is reported that the first application of Dantzig’s simplex algorithm to the solution of a non-trivial LP was Laderman’s solution of a 21 constraint, 77 variable instance of the classical Stigler Diet Problem [41]. It is reported that the total computation time was 120 man-days! The first computer implementation of an at-least modestly general version of the simplex algorithm is reported to have been on the SEAC computer at the then National Bureau of Standards [25]. (There were apparently some slightly earlier implementations for dealing with models that were “triangular”, that is, where all the linear systems could be solved by simple addition and subtraction.) Orchard-Hays [35] reports that several small instances having as many as 10 constraints and 20 variables were solved with this implementation. The first systematic development of computer codes for the simplex algorithm began very shortly thereafter at the RAND Corporation in Santa Monica, California. Dantzig’s initial LP work occurred at the Air Force following

This essay investigates the concept of linear programming in general and linear stochastic programming in particular. Linear stochastic programming is described as the model where the parameters of the linear programming admit random variability. The first three chapters present through a set-geometric approach the foundations of linear programming. Chapter one describes the evolution of the concepts which resulted in the adoption of the model. Chapter two describes the constructs in n-dimensional euclidian space which constitute the mathematical basis of linear programs, and chapter three defines the linear programming model and develops the computational basis of the simplex algorithm. The second three chapters analyze the effect of the introduction of risk into the linear programming model. The different approaches of estimating and measuring risk are studied and the difficulties arising in formulating the stochastic problem and deriving the equivalent deterministic problems are treated from the theoretical and practical point of view. Multiple examples are given throughout the essay for clarification of the salient points.

Contributors

Feed formulation using linear programming consists of determining the mixture of feedstuffs required to meet pre-established animal nutritional requirements at the lowest cost. On the other hand, with the use of non-linear programming, it is possible to define nutritional requirements at the time of formulation, aiming at maximum profit. The objective of the present study was to compare feeds formulated using linear and non-linear programming in terms of live performance and internal and external egg quality of commercial laying hens. A total of 288 Hisex® White laying hens, 1.540 ± 0.128 g body weight, were evaluated from 33 to 45 weeks of age. Hens were distributed in a completely randomized block design, including six treatments with six replicates of eight birds each. Three treatments consisted of feeds formulated using linear programming and based on the nutritional requirements of Rostagno et al. (2011), of the genetic strain manual, or mathematical models to maximize performance. The other three treatments consisted of feeds formulated using non-linear programming considering typical, favorable, or unfavorable market scenarios. Data were submitted to analysis of variance, and in case of significance (p 0.05) Haugh unit, albumen height, or external egg quality parameters. Treatment effects (p<0.05) on yolk weight, albumen weight, yolk color, yolk percentage, albumen percentage, and performance parameters were described. In general, feeds formulated using linear programming and based on nutritional requirements obtained by mathematical models and the genetic strain manual promoted better performance results because the feeds were nutritionally denser. However, the treatments that maximized live performance did not result in higher profitability, which was obtained with the diet formulated for a favorable market scenario using non-linear programming.