ZERO NUCLEAR WEAPONS

The current nuclear nonproliferation order is no longer sustainable. The Treaty on the Non-Proliferation of Nuclear Weapons (NPT) has weakened considerably over the years, with nuclear have-nots displaying increased dissatisfaction with the status quo. Meanwhile, demands for civilian nuclear technology have led to increased proliferation risks in the form of dual-use technologies. Arms control as we currently understand it—piecemeal treaties and agreements—is no longer sufficient to address the growing threat of proliferation and the frailty of the NPT. This article calls for a bolder nonproliferation agenda pursuing multilateral nuclear disarmament. Disarmament is, in fact, technologically achievable; a lack of political will stands as the only remaining roadblock to a world free of nuclear weapons. A better understanding of the technological feasibility of disarmament, as well as recognition of the diminishing strategic value of nuclear weapons, will help to erode this political reluctance.     

The impact of exponential smoothing forecasts on the bullwhip effect

An important phenomenon often observed in supply chain management, known as the bullwhip effect, implies that demand variability increases as one moves up the supply chain, i.e., as one moves away from customer demand. In this paper we quantify this effect for simple, two‐stage, supply chains consisting of a single retailer and a single manufacturer. We demonstrate that the use of an exponential smoothing forecast by the retailer can cause the bullwhip effect and contrast these results with the increase in variability due to the use of a moving average forecast. We consider two types of demand processes, a correlated demand process and a demand process with a linear trend. We then discuss several important managerial insights that can be drawn from this research. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 269–286, 2000     

The cost impact of using simple forecasting techniques in a supply chain

In this paper we consider an inventory model in which the retailer does not know the exact distribution of demand and thus must use some observed demand data to forecast demand. We present an extension of the basic newsvendor model that allows us to quantify the value of the observed demand data and the impact of suboptimal forecasting on the expected costs at the retailer. We demonstrate the approach through an example in which the retailer employs a commonly used forecasting technique, exponential smoothing. The model is also used to quantify the value of information and information sharing for a decoupled supply chain in which both the retailer and the manufacturer must forecast demand. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 388–411, 2003     

Properties of a source location estimator in the plane

The statistical properties of an estimator of a source location were established by simulation for the case in which the source location is estimated—using transformation of lines to points—from the angles in which different observers see the source, and both the assumed locations of the observer points and the observed angles are subject to error. It was found that for normal error distributions the estimator is unbiased, and the resulting estimates are approximately normally distributed with a small standard deviation. An easy-to-use and reliable forecasting formula was suggested to forecast the parameters of the distributions of the estimates for different observer-source relationships. © 1993 John Wiley & Sons, Inc.     

Explicit modeling of detection within a stochastic duel

Many mathematical models have been formulated to describe combat between two weapon systems. However, until recently duel models did not explicitly represent target detection within a duel, leading to the necessity for the development of new model for each tactical situation. An earlier article by two of the authors described a duel between weapons with constant firing times and explicit modeling of detection. This article enhances the study of this form of duel between weapons by introducing a variable parameter for firing times. This enhancement removes the discontinuities evident during parametric analysis of the earlier model and hence provides a more coherent model of this combat situation. © 1993 John Wiley & Sons, Inc.     

Computational aspects in multistage bayesian acceptance sampling

Simple criteria are found for reducing the computational effort in multistage Bayesian acceptance sampling. Regions of optimality are given for both terminal actions accept and reject. Also, criteria are presented for detecting nonoptimality of sets of sample sizes. Finally, nearly optimal (z,c^?,c⁺)-sampling plans are constructed by restricting attention to a small subset of sample sizes.     

Selection of the optimal setup policy

A model is developed taking into consideration all the costs (namely cost of sampling, cost of not detecting a change in the process, cost of a false indication of change, and the cost of readjusting detected changes) incurred when a production process, using an unscheduled setup policy, utilizes fraction-defective control charts to control current production. The model is based on the concept of the expected time between detection of changes calling for setups. It is shown that the combination of unscheduled setups and control charts can be utilized in an optimal way if those combinations of sample size, sampling interval, and extent of control limits from process average are used that provide the minimum expected total cost per unit of time. The costs of a production process that uses unscheduled setups in conjunction with the appropriate optimal control charts are compared to the costs of a production process that uses scheduled setups at optimum intervals in conjunction with its appropriate control charts. This comparison indicates the criteria for selecting production processes with scheduled setups using optimal setup intervals over unscheduled setups. Suggestions are made to evaluate the optimal process setup strategy and the accompanying optimal decision parameters, for any specific cost data, by use of computer enumeration. A numerical example for assumed cost and process data is provided.     

Inventory models with a mixture of backorders and lost sales

This article presents several single-echelon, single-item, static demand inventory models for situations in which, during the stockout period, a fraction b of the demand is backordered and the remaining fraction 1 - b is lost forever. Both deterministic and stochastic demand are considered. although the case of stochastic demand is treated heuristically. In each situation, a mathematical model representing the average annual cost of operating the inventory system is developed. and an optimum operating policy derived. At the extremes b=1 and b=0 the models presented reduce to the usual backorders and lost sales cases, respectively.     

On nonpreemptive strategies in stochastic scheduling

It is shown that there is an optimal strategy for a class of stochastic scheduling problems which is nonpreemptive. The results which yield this conclusion are generalizations of previous ones due to Glazebrook and Gittins. These new results also lead to an evaluation of the performance of nonpreemptive strategies in a large class of problems of practical interest.     

Learning and costs in airframe production: A multiple-output production function approach

This article examines the influence of both production rate and learning on airframe program costs. A dynamic multiple-output production model is developed and is used to observe the cost impact of changes in production rate and learning. Several simulations are performed to demonstrate the sensitivity of the optimal time path of cost to changes in the model parameters. The model is applied by estimating parameters from the F102 airframe program.