Looking ahead in India

India and China contain more than 40% of the world's population, yet in India it is painfully clear that the political commitment necessary to tackle India's greatest problem is not there in full measure. India's present per capita income is less than $300, and nearly 65% of the people live below the poverty line. The average Indian woman produces 5 children; even if the Indian government's efforts to reduce family size to 2 children is successful by the year 2040, India will have a population of 2.5 billion. The possibility that India will succeed in reducing average family size to 2 children appears remote. 30 years ago, India became the 1st developing country to formally make family planning a matter of national policy. In the early years of the national family planning programs, practitioners had access mostly to sterilization and condoms. Over the years, theIndian government persuaded the US and other western donors to give $2 billion to population control programs. Still, the population continues to grow annually at the rate of 2.1%. Government statistics reflect the ups and downs of national population control policies; thenumber of new family planning users increased from 4.3 million in 1974-1975 to 12.5 million in 1976-1977, due largely to a dramatic increase in vasectomies. Tge number of new contraceptive users fell to 4.5 million after the "emergency" was lifted in 1977. The present Indian generation is far more receptive culturally as well as sociologically to the concept of population control than most other developing countries. What is needed now is renewed political committment by the Gandhi adminiostration. India cannot afford to replicate the Chinese way of tackling overpopulation without inflicting human abuses and without undermining its painstakingly cultivated democratic system.     

The interactive fixed charge linear programming problem

In many decision-making situations, each activity that can be undertaken may have associated with it both a fixed and a variable cost. Recently, we have encountered serveral practical problems in which the fixed cost of undertaking an activity depends upon which other activities are also undertaken. To our knowledge, no existing optimization model can accomodate such a fixed cost structure. To do so, we have therefore developed a new model called the interactive fixed charge linear programming problem (IFCLP). In this paper we present and motivate problem (IFCLP), study some of its characteristics, and present a finite branch and bound algorithm for solving it. We also discuss the main properties of this algorithm.     

An operational analysis of system calibration

Mathematical models are proposed for studying the impact of miscalibration upon operational effectiveness. Methodology for assessing the system effectiveness and an approach for optimizing the effectiveness of a calibration program are examined. The theory application is discussed and the results of some specific and convenient models are presented.     

Two heuristics for the economic lot scheduling problem: An experimental study

In this article we present and test two heuristics for the economic lot scheduling problem. The first heuristic was developed by one of us (P.C. Geng) during Ph.D. research, while the other is a convergent implementation of an algorithm due to Doll and Whybark. We study the performance of these heuristics on a large set of test problems constructed using a new form of problem generation that yields random problems within an experimental design.     

A two-echelon inventory model with purchases,dispositions, shipments,returns and transshipments

This paper presents a one-period two-echelon inventory model with one warehouse in the first echelon and n warehouses in the second echelon. At the beginning of the period the stock levels at all facilities are adjusted by purchasing or disposing of items at the first echelon, returning or shipping items between the echelons and transshipping items within the second echelon. During the period, demands (which may be negative) are placed on all warehouses in the second echelon and an attempt is made to satisfy shortages either by an expedited shipment from the first echelon to the second echelon or an expedited transshipment within the second echelon. The decision problem is to choose an initial stock level at the first echelon (by a purchase or a disposition) and an initial allocation so as to minimize the initial stock movement costs during the period plus inventory carrying costs and system shortage costs at the end of the period. It is shown that the objective function takes on one of four forms, depending on the relative magnitudes of the various shipping costs. All four forms of the objective function are derived and proven to be convex. Several applications of this general model are considered. We also consider multi-period extensions of the general model and an important special case is solved explicitly.     

Sequential tests for hypotheses concerning the product of poisson parameters with applications to problems in reliability and biometry

Sequential tests for the product of Poisson parameters based on the generalized incomplete modified Bessel (g.i.m.B.) distributions are given. Applications to reliability and biometry are indicated.     

Steady state waiting time in a multicenter job shop

This paper analyzes the waiting-time distribution of a specific job as it moves through a job-shop with multiple centers and exponential service times. The movement of the job through the shop is governed by a Markovian transition matrix and ends with the job's exit from the shop.     

Return policies for an inventory system with positive and negative demands

We consider a single item inventory system with positive and negative stock fluctuations. Items can be purchased from a central stock, n items can be returned for a cost R + rn, and a linear inventory carrying cost is charged. It is shown that for minimizing the asymptotic cost rate when returns are a significant fraction of stock usage, a two-critical-number policy (a,b) is optimal, where b is the trigger level for returns and b – a is the return quantity. The values for a and b are found, as well as the operating characteristics of the system. We also consider the optimal return decision to make at time zero and show that it is partially determined by a and b.