scholarly journals Linear programming and pediatric dietetics

1990 ◽  
Vol 64 (2) ◽  
pp. 307-317 ◽  
Author(s):  
Ciro Colavita ◽  
Renato D'orsi
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Operations Research ◽  
Analytical Methods ◽  
Simple Problem ◽  
Feasible Region ◽  
Recommended Dietary Allowances ◽  
Geometrical Representation

The composition of 500 foods has been stored in a computer in order to analyse a child's diet. The methodology of operations research is applied to a very simple problem: a diet with only two foods. The geometrical representation of the ‘feasible region’ and of the ‘objective function’ is illustrated. One of the analytical methods employable with many variables (foods) is considered. This method was used in trying to find diets allowing for the preferential use of selected foods while respecting recommended dietary allowances, the tastes of the child and other constraints. The theoretical difficulty of transferring this methodology to pediatric dietetics was examined. We solved a simple case utilizing this procedure.

scholarly journals Didáctica de la programación lineal con el método gráfico

2007 ◽  
pp. 17
Author(s):  
Fernando León Parada
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Programming Model ◽  
Geometric Analysis ◽  
Linear Programming Model ◽  
Feasible Region ◽  
Optimal Point ◽  
Decision Variables ◽  
Programación Lineal

Every Linear Programming model has a Objective Function determined by the coefficients of the decision variables. The Objective Function can be represented as a line evaluated on an optimal point which slope depends from the variation of the coefficients. The segments adjacent to the optimal point in the Feasible Region can also determine the variation of this slope. Finally a geometric analysis allows to establishing the constraining of the boundary for the Feasible Region keeping as a domain for the Objective Function.

scholarly journals On validation of solutions to linear programming problems on cluster computing systems

2021 ◽  
pp. 252-261
Author(s):  
Л.Б. Соколинский ◽  
И.М. Соколинская
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Large Scale ◽  
Cluster Computing ◽  
Parallel Implementation ◽  
Main Idea ◽  
Small Radius ◽  
Feasible Region ◽  
Computing Systems ◽  
Scalable Algorithm

В статье представлен параллельный алгоритм валидации решений задач линейного программирования. Идея метода состоит в том, чтобы генерировать регулярный набор точек на гиперсфере малого радиуса, центрированной в точке тестируемого решения. Целевая функция вычисляется для каждой точки валидационного множества, принадлежащей допустимой области. Если все полученные значения меньше или равны значению целевой функции в точке, проверяемой как решение, то эта точка считается корректным решением. Параллельная реализация алгоритма VaLiPro выполнена на языке C++ с использованием параллельного BSF-каркаса, инкапсулирующего в проблемно-независимой части своего кода все аспекты, связанные с распараллеливанием программы на базе библиотеки MPI. Приводятся результаты масштабных вычислительных экспериментов на кластерной вычислительной системе, подтверждающие эффективность предложенного подхода. The paper presents and evaluates a scalable algorithm for validating solutions to linear programming (LP) problems on cluster computing systems. The main idea of the method is to generate a regular set of points (validation set) on a small-radius hypersphere centered at the solution point submitted to validation. The objective function is computed at each point of the validation that belongs to the feasible region. If all the values are less than or equal to the value of the objective function at the point that is to be validated, then this point is the correct solution. The parallel implementation of the VaLiPro algorithm is written in C++ through the parallel BSF-skeleton, which encapsulates all aspects related to the MPI-based parallelization of the program. We provide the results of large-scale computational experiments on a cluster computing system to study the scalability of the VaLiPro algorithm.

Grey-fuzzy solution for multi-objective linear programming with interval coefficients

2018 ◽  
Vol 8 (3) ◽  
pp. 312-327 ◽  
Author(s):  
Amin Mahmoudi ◽  
Mohammad Reza Feylizadeh ◽  
Davood Darvishi ◽  
Sifeng Liu
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Fuzzy Programming ◽  
Feasible Region ◽  
Grey Theory ◽  
Acceptable Solution ◽  
Content Type ◽  
Multi Objective ◽  
Interval Coefficients ◽  
Interactive Fuzzy Programming

Purpose The purpose of this paper is to propose a method for solving multi-objective linear programming (MOLP) with interval coefficients using positioned programming and interactive fuzzy programming approaches. Design/methodology/approach In the proposed algorithm, first, lower and upper bounds of each objective function in its feasible region will be determined. Afterwards using fuzzy approach, considering a membership function for each objective function and finally using grey linear programming, the solution for this problem will be obtained. Findings According to the presented example, in this paper, the proposed method is both simple in use and suitable for solving different problems. In the numerical example mentioned in this paper, the proposed method provides an acceptable solution for such problems. Practical implications As in most real-world situations, the coefficients of decision models are not known and exact. In this paper, the authors consider the model of MOLP with interval data, since one of the solutions to cover uncertainty is using interval theory. Originality/value Based on using grey theory and interactive fuzzy programming approaches, an appropriate method has been presented for solving MOLP problems with interval coefficients. The proposed method, against the complex methods, has less effort and offers acceptable solutions.

scholarly journals Application of Change of Basis in the Simplex Method

2017 ◽  
Vol 11 (1) ◽  
pp. 41
Author(s):  
Mehmet Hakan Özdemir
Keyword(s):  
Linear Programming ◽  
Iterative Method ◽  
Objective Function ◽  
Simplex Method ◽  
Programming Model ◽  
Feasible Region ◽  
Optimal Value ◽  
Linear Programming Problems ◽  
Repeated Use ◽  
Simplex Tableau

The simplex method is a very useful method to solve linear programming problems. It gives us a systematic way of examining the vertices of the feasible region to determine the optimal value of the objective function. It is executed by performing elementary row operations on a matrix that we call the simplex tableau. It is an iterative method that by repeated use gives us the solution to any n variable linear programming model. In this paper, we apply the change of basis to construct following simplex tableaus without applying elementary row operations on the initial simplex tableau.

Developing Schedule With Linear Programming (Case Study: STTF II Project Komplek Sukamukti Banjaran)

2020 ◽  
Vol 4 (02) ◽  
pp. 34-45
Author(s):  
Naufal Dzikri Afifi ◽  
Ika Arum Puspita ◽  
Mohammad Deni Akbar
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Project Scheduling ◽  
Time Limit ◽  
Minimum Time ◽  
Service Cost ◽  
Trade Off ◽  
Overtime Work ◽  
The Cost

Shift to The Front II Komplek Sukamukti Banjaran Project is one of the projects implemented by one of the companies engaged in telecommunications. In its implementation, each project including Shift to The Front II Komplek Sukamukti Banjaran has a time limit specified in the contract. Project scheduling is an important role in predicting both the cost and time in a project. Every project should be able to complete the project before or just in the time specified in the contract. Delay in a project can be anticipated by accelerating the duration of completion by using the crashing method with the application of linear programming. Linear programming will help iteration in the calculation of crashing because if linear programming not used, iteration will be repeated. The objective function in this scheduling is to minimize the cost. This study aims to find a trade-off between the costs and the minimum time expected to complete this project. The acceleration of the duration of this study was carried out using the addition of 4 hours of overtime work, 3 hours of overtime work, 2 hours of overtime work, and 1 hour of overtime work. The normal time for this project is 35 days with a service fee of Rp. 52,335,690. From the results of the crashing analysis, the alternative chosen is to add 1 hour of overtime to 34 days with a total service cost of Rp. 52,375,492. This acceleration will affect the entire project because there are 33 different locations worked on Shift to The Front II and if all these locations can be accelerated then the duration of completion of the entire project will be effective

scholarly journals Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110144
Author(s):  
Qianqian Zhang ◽  
Daqing Wang ◽  
Lifu Gao
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Inverse Kinematics ◽  
Differential Evolution Algorithm ◽  
Weight Coefficient ◽  
Adaptive Mutation ◽  
Feasible Region ◽  
Serial Manipulators ◽  
De Algorithm ◽  
Self Adaptive

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

scholarly journals An alternative perspective on copositive and convex relaxations of nonconvex quadratic programs

2021 ◽  
Author(s):  
E. Alper Yıldırım
Keyword(s):  
Objective Function ◽  
Convex Relaxation ◽  
Large Family ◽  
Quadratic Program ◽  
Feasible Region ◽  
Recession Cone ◽  
Convex Relaxations ◽  
Quadratic Programs ◽  
Alternative Perspective ◽  
Optimal Value

AbstractWe study convex relaxations of nonconvex quadratic programs. We identify a family of so-called feasibility preserving convex relaxations, which includes the well-known copositive and doubly nonnegative relaxations, with the property that the convex relaxation is feasible if and only if the nonconvex quadratic program is feasible. We observe that each convex relaxation in this family implicitly induces a convex underestimator of the objective function on the feasible region of the quadratic program. This alternative perspective on convex relaxations enables us to establish several useful properties of the corresponding convex underestimators. In particular, if the recession cone of the feasible region of the quadratic program does not contain any directions of negative curvature, we show that the convex underestimator arising from the copositive relaxation is precisely the convex envelope of the objective function of the quadratic program, strengthening Burer’s well-known result on the exactness of the copositive relaxation in the case of nonconvex quadratic programs. We also present an algorithmic recipe for constructing instances of quadratic programs with a finite optimal value but an unbounded relaxation for a rather large family of convex relaxations including the doubly nonnegative relaxation.

Local Perturbation Analysis of Linear Programming with Functional Relation Among Parameters

2013 ◽  
pp. 1-24
Author(s):  
Payam Hanafizadeh ◽  
Abolfazl Ghaemi ◽  
Madjid Tavana
Keyword(s):  
Sensitivity Analysis ◽  
Linear Programming ◽  
Objective Function ◽  
Perturbation Analysis ◽  
Functional Relation ◽  
Local Perturbation ◽  
Nonlinear Functional ◽  
Functional Relations ◽  
Software Packages ◽  
The Right

In this paper, the authors study the sensitivity analysis for a class of linear programming (LP) problems with a functional relation among the objective function parameters or those of the right-hand side (RHS). The classical methods and standard sensitivity analysis software packages fail to function when a functional relation among the LP parameters prevail. In order to overcome this deficiency, the authors derive a series of sensitivity analysis formulae and devise corresponding algorithms for different groups of homogenous LP parameters. The validity of the derived formulae and devised algorithms is corroborated by open literature examples having linear as well as nonlinear functional relations between their vector b or vector c components.

A new solution method for a class of large dimension rank-two nonconvex programs

2020 ◽  
Author(s):  
Riccardo Cambini ◽  
Irene Venturi
Keyword(s):  
Objective Function ◽  
Optimal Level ◽  
Outer Approximation ◽  
Point Of View ◽  
Large Dimension ◽  
Low Rank ◽  
Feasible Region ◽  
Quantitative Management ◽  
Solution Methods ◽  
Optimal Level Solutions

Abstract Low-rank problems are nonlinear minimization problems in which the objective function, by means of a suitable linear transformation of the variables, depends on very few variables. These problems often arise in quantitative management science applications, for example, in location models, transportation problems, production planning, data envelopment analysis and multiobjective programs. They are usually approached by means of outer approximation, branch and bound, branch and select and optimal level solution methods. The paper studies, from both a theoretical and an algorithmic point of view, a class of large-dimension rank-two nonconvex problems having a polyhedral feasible region and $f(x)=\phi (c^Tx+c_0,d^Tx+d_0)$ as the objective function. The proposed solution algorithm unifies a new partitioning method, an outer approximation approach and a mixed method. The results of a computational test are provided to compare these three approaches with the optimal level solutions method. In particular, the new partitioning method performs very well in solving large problems.

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