The difference between the managerial and mathematical interpretation of sensitivity analysis results in linear programming

2000 ◽  
Vol 65 (3) ◽  
pp. 257-274 ◽  
Author(s):  
Tamás Koltai ◽  
Tamás Terlaky
Keyword(s):  
Sensitivity Analysis ◽  
Linear Programming ◽  
The Difference ◽  
Mathematical Interpretation

POSITIVE SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING

2004 ◽  
Vol 21 (01) ◽  
pp. 53-68 ◽  
Author(s):  
CHAN-KYOO PARK ◽  
WOO-JE KIM ◽  
SANGWOOK LEE ◽  
SOONDAL PARK
Keyword(s):  
Sensitivity Analysis ◽  
Linear Programming ◽  
Optimal Solution ◽  
Optimal Partition ◽  
Optimal Basis ◽  
Analysis Methods ◽  
Sensitivity Analysis Method ◽  
The Difference ◽  
The Relationship ◽  
Special Case

Positive sensitivity analysis (PSA) is a sensitivity analysis method for linear programming that finds the range of perturbations within which positive value components of a given optimal solution remain positive. Its main advantage is that it is applicable to both an optimal basic and nonbasic optimal solution. The first purpose of this paper is to present some properties of PSA that are useful for establishing the relationship between PSA and sensitivity analysis using optimal bases, and between PSA and sensitivity analysis using the optimal partition. We examine how the range of PSA varies according to the optimal solution used for PSA, and discuss the relationship between the ranges of PSA using different optimal solutions. The second purpose is to clarify the relationship between PSA and sensitivity analysis using an optimal basis, and the relationship between PSA and sensitivity analysis using the optimal partition. We show that sensitivity analysis using the optimal partition is a special case of PSA, and its properties can be derived from the properties of PSA. The comparison among the three sensitivity analysis methods will lead to a better understanding of the difference among sensitivity analysis methods.

scholarly journals Measuring cost and affordability of current vs. healthy diets in Argentina: an application of linear programming and the INFORMAS protocol

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Gabriel Giacobone ◽  
Maria Victoria Tiscornia ◽  
Leila Guarnieri ◽  
Luciana Castronuovo ◽  
Sally Mackay ◽  
...  
Keyword(s):  
Linear Programming ◽  
Energy Intake ◽  
Current Model ◽  
Food Product ◽  
Poor Households ◽  
Average Household Income ◽  
Healthy Diets ◽  
Diet Cost ◽  
The Difference ◽  
Optimal Approach

Abstract Background Food cost and affordability is one of the main barriers to improve the nutritional quality of diets of the population. However, in Argentina, where over 60% of adults and 40% of children and adolescents are overweight or obese, little is known about the difference in cost and affordability of healthier diets compared to ordinary, less healthy ones. Methods We implemented the “optimal approach” proposed by the International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support (INFORMAS). We modelled the current diet and two types of healthy diets, one equal in energy with the current diet and one 6.3% lower in energy by linear programming. Cost estimations were performed by collecting food product prices and running a Monte Carlo simulation (10,000 iterations) to obtain a range of costs for each model diet. Affordability was measured as the percentage contribution of diet cost vs. average household income in average, poor and extremely poor households and by income deciles. Results On average, households must spend 32% more money on food to ensure equal energy intake from a healthy diet than from a current model diet. When the energy intake target was reduced by 6.3%, the difference in cost was 22%. There are no reasonably likely situations in which any of these healthy diets could cost less or the same than the current unhealthier one. Over 50% of households would be unable to afford the modelled healthy diets, while 40% could not afford the current diet. Conclusions Differential cost and affordability of healthy vs. unhealthy diets are germane to the design of effective public policies to reduce obesity and NCDs in Argentina. It is necessary to implement urgent measures to transform the obesogenic environment, making healthier products more affordable, available and desirable, and discouraging consumption of nutrient-poor, energy-rich foods.

scholarly journals Sensitivity analysis of Economic Value Added as a dominant indicator of concept of economic profit

2021 ◽  
Vol 129 ◽  
pp. 03014
Author(s):  
Dusan Karpac ◽  
Viera Bartosova
Keyword(s):  
Sensitivity Analysis ◽  
Economic Value ◽  
Value Added ◽  
Economic Value Added ◽  
Financial Status ◽  
Economic Profit ◽  
Methods Of Synthesis ◽  
The Difference ◽  
Individual Variables ◽  
The Impact

Research background: The modern goal of enterprises, value creation, is achieved through the concept of economic profit. Profit, as part of profit or loss, is one of the most important flows, pointing to how efficiently corporate capital is used in an entity (Coatney & Poliak, 2020). The article deals with the difference between accounting and economic profit, the selected form of economic profit - the EVA indicator. The economic value added (EVA) indicator is one of the best-known modern indicators of a company's performance (Siekelova et al., 2019). It shows whether the given entity increases its value or only earns for its economic survival. The benefit of this indicator is the valuation of equity and taking into account the risk. It is difficult to express the economic profit itself, therefore the article also addresses the issue of its calculation (Shah et al., 2016). The company needs to know its financial status and the direction it is heading, so we decided to calculate a selected form of economic profit. Purpose of the article: The company needs to know its financial status and the direction it is heading, so we decided to calculate a selected form of economic profit. When expressing the value of the economic value added indicator, it is also important to know the items and components of the calculation that have the strongest meaning and effect on the possible amount of the indicator. Given this, we decided to use a sensitivity analysis, which points to the effect of individual variables that participate in the construction of the EVA calculation. Methods: In this work, the methods of induction, deduction, and comparison were used to obtain a true picture of the subject issue. Methods of synthesis and analysis of the researched issues were also used. Findings & Value added: In the paper there is pointed out the intensity of the impact of individual variables that entered into the calculation of the economic value added indicator as a dominant indicator of concept of economic profit.

The tolerance approach to sensitivity analysis in network linear programming

Networks ◽  
1988 ◽  
Vol 18 (3) ◽  
pp. 159-171 ◽  
Author(s):  
N. Ravi ◽  
Richard E. Wendell
Keyword(s):  
Sensitivity Analysis ◽  
Linear Programming ◽  
Tolerance Approach

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.

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