Classes of linear programs with integral optimal solutions

1985 ◽  
pp. 225-237 ◽  
Author(s):  
Y. P. Aneja ◽  
R. Chandrasekaran ◽  
K. P. K. Nair
Keyword(s):  
Linear Programs ◽  
Optimal Solutions

Sur Une Representation Explicite des Solutions Optimales D'un Programme Lineaire

1986 ◽  
Vol 29 (4) ◽  
pp. 419-425
Author(s):  
Martin Bilodeau
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Explicit Representation ◽  
Linear Programs ◽  
Optimal Solutions

AbstractThis paper gives a sharper explicit representation of the set of optimal solutions for a class of linear programs than those obtained by A. Ben-Israel, A. Charnes and S. Zlobec since 1968. The representation is used to determine bounds on the coefficients of the objective function that produce the same set of optimal solutions (sensitivity analysis).

scholarly journals Efficiency of MOMA-plus Method to Solve Some Fully Fuzzy L-R Triangular Multiobjective Linear Programs

2018 ◽  
Vol 10 (2) ◽  
pp. 77 ◽  
Author(s):  
Abdoulaye Compaoré ◽  
Kounhinir Somé ◽  
Joseph Poda ◽  
Blaise Somé
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Linear Programs ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Linear Optimization Problem ◽  
Novel Approach ◽  
Linear Optimization Problems ◽  
Single Objective

In this paper, we propose a novel approach for solving some fully fuzzy L-R triangular multiobjective linear optimization programs using MOMA-plus method (Kounhinir, 2017). This approach is composed of two relevant steps such as the converting of the fully fuzzy L-R triangular multiobjective linear optimization problem into a deterministic multiobjective linear optimization and the applying of the adapting MOMA-plus method. The initial version of MOMA-plus method is designed for multiobjective deterministic optimization (Kounhinir, 2017) and having already been tested on the single-objective fuzzy programs (Abdoulaye, 2017). Our new method allow to find all of the Pareto optimal solutions of a fully fuzzy L-R triangular multiobjective linear optimization problems obtained after conversion. For highlighting the efficiency of our approach a didactic numerical example is dealt with and obtained solutions are compared to Total Objective Segregation Method proposed by Jayalakslmi and Pandia (Jayalakslmi 2014).

Combinatorial Optimization of Clustering Decisions: An Application to Refine Psychiatric Diagnoses

2018 ◽  
Author(s):  
Jordan Stevens ◽  
Douglas Steinley ◽  
Cassandra L. Boness ◽  
Timothy J Trull ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Alcohol Use Disorder ◽  
Point Of View ◽  
Psychiatric Diagnoses ◽  
Optimal Solutions ◽  
Specific Function ◽  
Significant Information ◽  
Diagnosis Group ◽  
Clustering Problems ◽  
Unique Application

Using complete enumeration (e.g., generating all possible subsets of item combinations) to evaluate clustering problems has the benefit of locating globally optimal solutions automatically without the concern of sampling variability. The proposed method is meant to combine clustering variables in such a way as to create groups that are maximally different on a theoretically sound derivation variable(s). After the population of all unique sets is permuted, optimization on some predefined, user-specific function can occur. We apply this technique to optimizing the diagnosis of Alcohol Use Disorder. This is a unique application, from a clustering point of view, in that the decision rule for clustering observations into the diagnosis group relies on both the set of items being considered and a predefined threshold on the number of items required to be endorsed for the diagnosis to occur. In optimizing diagnostic rules, criteria set sizes can be reduced without a loss of significant information when compared to current and proposed, alternative, diagnostic schemes.

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