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Uses of Sampling Techniques & Inventory Control with Capacity Constraints

By Malik, Sachin

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Book Id: WPLBN0100303028
Format Type: PDF (eBook)
File Size: 1.34 MB.
Reproduction Date: 4/1/2016

Title: Uses of Sampling Techniques & Inventory Control with Capacity Constraints  
Author: Malik, Sachin
Volume:
Language: English
Subject: Non Fiction, Social Sciences
Collections: Sociology, Authors Community
Historic
Publication Date:
2016
Publisher: Pons Editions
Member Page: InfiniteScience

Citation

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Malik, S., & Smarandache, F. (2016). Uses of Sampling Techniques & Inventory Control with Capacity Constraints. Retrieved from http://worldpubliclibrary.net/


Description
The main aim of the present book is to suggest some improved estimators using auxiliary and attribute information in case of simple random sampling and stratified random sampling and some inventory models related to capacity constraints. This volume is a collection of six papers, written by five co-authors (listed in the order of the papers): Dr. Rajesh Singh, Dr. Sachin Malik, Dr. Florentin Smarandache, Dr. Neeraj Kumar, Mr. Sanjey Kumar & Pallavi Agarwal. In the first chapter authors suggest an estimator using two auxiliary variables in stratified random sampling for estimating population mean. In second chapter they proposed a family of estimators for estimating population means using known value of some population parameters. In Chapter third an almost unbiased estimator using known value of some population parameter(s) with known population proportion of an auxiliary variable has been used. In Chapter four authors investigates a fuzzy economic order quantity model for two storage facility. The demand, holding cost, ordering cost, storage capacity of the own - warehouse are taken as a trapezoidal fuzzy numbers and in Chapter five a two-warehouse inventory model deals with deteriorating items, with stock dependent demand rate and model affected by inflation under the pattern of time value of money over a finite planning horizon. Shortages are allowed and partially backordered depending on the waiting time for the next replenishment. The purpose of this model is to minimize the total inventory cost by using Genetic algorithm.

 

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