Uses of Sampling Techniques & Inventory Control with Capacity Constraints

TL;DR

This collection of papers advances sampling estimators and inventory models with capacity constraints, incorporating auxiliary information, fuzzy numbers, and genetic algorithms for improved accuracy and cost minimization.

Contribution

It introduces new estimators using auxiliary variables and parameters, and develops fuzzy and genetic algorithm-based inventory models for capacity-constrained systems.

Findings

Proposed improved estimators for population mean using auxiliary information.

Developed fuzzy EOQ model for two storage facilities with trapezoidal fuzzy numbers.

Optimized inventory costs using genetic algorithms under capacity and deterioration constraints.

Abstract

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 five papers, written by six 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 the 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 trapezoidal fuzzy numbers. 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 the genetic algorithm. This book will be helpful for the researchers and students who are working in the field of sampling techniques and inventory control.