scholarly journals A MATHEMATICAL PROGRAMMING APPROACH IN OPTIMUM STRATIFICATION UNDER NEYMAN ALLOCATION FOR TWO STRATIFYING VARIABLES

2019 ◽  
pp. 173-185
Author(s):  
Faizan Danish ◽  
.E.H. Rizvi ◽  
Manish Kumar Sharma ◽  
Sudhakar Dwivedi ◽  
Bupesh Kumar ◽  
...  
Keyword(s):  
Relative Efficiency ◽  
Auxiliary Variable ◽  
Programming Approach ◽  
Auxiliary Variables ◽  
Study Variable ◽  
Numerical Illustration ◽  
Non Linear Programming ◽  
Dynamic Programming Approach ◽  
Mathematical Programming Approach ◽  
Neyman Allocation

The current study discusses the solution for obtaining stratification points under Neyman allocation having one study variable and two auxiliary variables. Using dynamic programming approach non-linear programming problem has been solved. The proposed technique has gained in precision rather than using only one auxiliary variable. Numerical illustration has been given in which each of the auxiliary variable is supposed to follow different distribution. Through the empirical study, the proposed method has been compared with the Ravindra and Sukhatme (1969) and Khan et al.(2005) methods with the conclusion of having its more relative efficiency.  

Scheduling and control of linear projects

1994 ◽  
Vol 21 (2) ◽  
pp. 219-230 ◽  
Author(s):  
Neil N. Eldin ◽  
Ahmed B. Senouci
Keyword(s):  
Dynamic Programming ◽  
Programming Approach ◽  
State Variables ◽  
Project Schedule ◽  
Numerical Illustration ◽  
Total Cost ◽  
Dynamic Programming Approach ◽  
Completion Date ◽  
And Control ◽  
Linear Projects

A two-state-variable, N-stage dynamic programming approach to scheduling and control of linear projects is presented. This approach accounts for practical considerations related to work continuity, interruptions, and lags between successive activities. In the dynamic programming formulation, stages represent project activities and state variables represent possible activity resources and interruptions at each location. The objective of the dynamic programming solution is to provide for the selection of resources, interruptions, and lags for production activities that lead to the minimum project total cost. In addition, the presented system produces a graphical presentation of the optimum project schedule and updates the original schedule based on update information input by the user. The updated schedule determines the new completion date, and forecasts the project new total cost based on the current project performance. A small linear project is provided as a numerical illustration of the system. Key words: dynamic programming, linear projects, scheduling systems, optimization of cost and scheduling durations.

scholarly journals GENERALISED EXPONENTIAL RATIO-TYPE ESTIMATOR FOR FINITE POPULATION VARIANCE UNDER RANDOM NON-RESPONSE

2021 ◽  
Vol 9 (01) ◽  
pp. 589-596
Author(s):  
Alisha Mittal ◽  
◽  
Manoj Kumar ◽  
Keyword(s):  
Unbiased Estimator ◽  
Finite Population ◽  
Research Paper ◽  
Auxiliary Variable ◽  
Population Variance ◽  
Study Variable ◽  
Numerical Illustration ◽  
The Family ◽  
Better Than

In this research paper an effort has been made for the estimation of population variance of the study variable by using information on certain known parameters of the auxiliary variable under non-response for scheme I and II given by Singh and Joarder (1998). Generalized exponential ratio-type estimator has been proposed and their properties have been studied under non response techniques and conditions were found when the family of proposed estimators identified by using different choices for (P, Q) performed better than the usual unbiased estimator. It was also observed that for different values of α ∈ (0.0, 1.0), the estimators and were found to be best under numerical illustration.

scholarly journals Efficient Method of Estimating the Finite Population Mean Based on Two Auxiliary Variables in the Presence of Non-Response Under Stratified Sampling

2021 ◽  
Author(s):  
Housila P. Singh ◽  
Pragati Nigam
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Variables ◽  
Optimum Conditions ◽  
Study Variable ◽  
Numerical Illustration ◽  
Population Mean ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Class Of Estimators ◽  
Better Than

This article addresses the problem of estimating the population mean using information on two auxiliary variables in presence of non-response on study variable only under stratified random sampling. A class of estimators has been defined. We have derived the bias and mean squared error up to first order of approximation. Optimum conditions are obtained in which the suggested class of estimators has minimum mean squared error. In addition to Chaudhury et al. (2009) estimator, many estimators can be identified as a member of the suggested class of estimators. It has been shown that the suggested class of estimators is better than the Chaudhury et al. (2009) estimator and other estimators. Results of the present study are supported through numerical illustration.

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