Minimum Cost Multiobjective Programming Model for Target Efficiency in Sample Selection

In this study, we developed a model which elaborates relationship among efficiency of an estimator and survey cost. This model is based on a multiobjective optimization programming structure. Survey cost and efficiency of related estimator(s) lie in different directions, i.e., if one increases, the...

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Vydané v:Scientific programming Ročník 2019; číslo 2019; s. 1 - 9
Hlavní autori: Shamsuddin, Sadaf, Shoukry, Alaa Mohamd, Hussain, Ijaz, Khan, Saima, Shad, Muhammad Yousaf, Hussain Gani, Showkat
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019
Hindawi
John Wiley & Sons, Inc
Predmet:
Algorithms
Cost engineering
Costs
Economic models
Efficiency
Estimates
Goal programming
Linear programming
Mathematical programming
Mean square errors
Minimum cost
Multiple objective analysis
Optimization
Studies
Variance analysis
ISSN:1058-9244, 1875-919X
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Shrnutí:In this study, we developed a model which elaborates relationship among efficiency of an estimator and survey cost. This model is based on a multiobjective optimization programming structure. Survey cost and efficiency of related estimator(s) lie in different directions, i.e., if one increases, the other decreases. The model presented in this study computes cost for a desired level of efficiency on various characteristics (goals). The calibrated model minimizes the cost for the compromise optimal sample selection from different strata when characteristic j is subject to achieve at least 1−αj level of efficiency of its estimator. In the first step, the proposed model minimizes the variance for a fixed cost, and it then finds the rise in cost for an αj percent rise in efficiency of any characteristic j. The resultant model is a multiobjective compromise allocation goal programming model.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1058-9244
1875-919X
DOI:10.1155/2019/7193726