Multi-objective Compromise Allocation in Multivariate Stratified Sampling Using Extended Lexicographic Goal Programming with Gamma Cost Function

2014 ◽  
Vol 14 (3) ◽  
pp. 255-265 ◽  
Author(s):  
Yousaf Shad Muhammad ◽  
Javid Shabbir ◽  
Ijaz Husain ◽  
Mitwali Abd-el.Moemen
Keyword(s):  
Cost Function ◽  
Goal Programming ◽  
Stratified Sampling ◽  
Multi Objective ◽  
Compromise Allocation ◽  
Multivariate Stratified Sampling ◽  
Lexicographic Goal Programming

scholarly journals A multiple response stratified sampling design with travel cost

2011 ◽  
Vol 29 (1) ◽  
pp. 31 ◽  
Author(s):  
Shazia Ghufran ◽  
Saman Khowaja ◽  
Najmussehar ◽  
M. J. Ahsan
Keyword(s):  
Cost Function ◽  
Travel Cost ◽  
Stratified Sampling ◽  
Sample Survey ◽  
Programming Approach ◽  
Multiple Response ◽  
Actual Cost ◽  
Compromise Allocation ◽  
The Cost ◽  
Linear Cost

In developing the theory of stratified sampling usually the cost function is taken as a linear function of sample sizes considering the measurement and the overhead costs only. In many practical situations the linear cost function does not approximate the actual cost incurred adequately. For example when the cost of traveling between the units selected in the sample within a stratum is significant, instead of linear cost function a cost function that is quadratic in will be a more close approximation to the actual cost. In this paper the problem of finding a compromise allocation for a multiple response stratified sample survey with a significant travel cost within strata is formulated as a multiobjective non linear programming problem. A solution procedure is proposed using the goal programming approach. A numerical example is also presented to illustrate the computational details.

scholarly journals A fuzzy goal programming approach in stochastic multivariate stratified sample surveys

2013 ◽  
Vol 31 (1) ◽  
pp. 80 ◽  
Author(s):  
Neha Gupta ◽  
Irfan Ali ◽  
Shafiullah ◽  
Abdul Bari
Keyword(s):  
Goal Programming ◽  
Computational Procedure ◽  
Fuzzy Goal Programming ◽  
Programming Approach ◽  
Membership Functions ◽  
Compromise Allocation ◽  
Multivariate Stratified Sampling ◽  
Non Linear ◽  
Fuzzy Goal ◽  
The Individual

This paper deals with fuzzy goal programming (FGP) approach to stochastic multivariate stratified sampling with non linear objective function and probabilistic non linear cost constraint which is formulated as a multiobjective non linear programming problem (MONLPP). In the model formulation of the problem, we first determine the individual best solution of the objective functions subject to the system constraints and construct the non linear membership functions of each objective. The non linear membership functions are then transformed into equivalent linear membership functions by first order Taylor series at the individual best solution point. Fuzzy goal programming approach is then used to achieve maximum degree of each of the membership goals by minimizing negative deviational variables and finally obtain the compromise allocation. A numerical example is presented to illustrate the computational procedure of the proposed approach.

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