On the solution of a stochastic bottleneck assignment problem and its variations

In this article, we study the stochastic version of the so-called bottleneck assignment problem. Our primary objective is to maximize the probability that the bottleneck value satisfies a specified target. Under general stochastic assumptions, we show that the solution in this case is easily obtained by solving a linear assignment problem. We next examine the situation where the target is to be minimized, given that the probability of satisfying the target exceeds a specified threshold. Finally, we address extensions to the original problem where a second objective is also considered.     

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.     

On the exponentially weighted moving variance

MacGregor and Harris (J Quality Technol 25 (1993) 106–118) proposed the exponentially weighted mean squared deviation (EWMS) and the exponentially weighted moving variance (EWMV) charts as ways of monitoring process variability. These two charts are particularly useful for individual observations where no estimate of variability is available from replicates. However, the control charts derived by using the approximate distributions of the EWMS and EWMV statistics are difficult to interpret in terms of the average run length (ARL). Furthermore, both control charting schemes are biased procedures. In this article, we propose two new control charts by applying a normal approximation to the distributions of the logarithms of the weighted sum of chi squared random variables, which are respectively functions of the EWMS and EWMV statistics. These new control charts are easy to interpret in terms of the ARL. On the basis of the simulation studies, we demonstrate that the proposed charts are superior to the EWMS and EWMV charts and they both are nearly unbiased for the commonly used smoothing constants. We also compare the performance of the proposed charts with that of the change point (CP) CUSUM chart of Acosta‐Mejia (1995). The design of the proposed control charts is discussed. An example is also given to illustrate the applicability of the proposed control charts. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

Fencing the bear? Explaining US foreign policy towards Russian interventions

Despite the burgeoning literature on Russia’s renewed power politics, little attention has been paid to the fact that US reactions towards Russia’s military interventions were all but coherent. The USA has chosen weak measures in Georgia in 2008 (shaming) compared to its assertive response in Ukraine in 2014 (sanctions, hard deterrence). This article assesses the explanatory power of neorealist, liberal and constructivist theories for the variation in US reactions towards Russian interventions in Georgia and Ukraine. Our argument is that the constructivist perspective explains the cases best as it highlights the power and communality of normative assessments. The Ukraine crisis was perceived by the USA as a violation of core international norms, especially the non-use of force and the principle of territorial integrity. Relevant international norm carriers shared this assessment of the conflict. In contrast, the perception of the Georgian war centred on the issue of democracy promotion. While democracy is an important aim of US foreign policy, it does not summon the same normative importance as general principles of international law. Furthermore, the perception of the Georgian war remained contested among Western allies, which decreased the communality of the normative assessment.     

Waiting time distributions for transient bulk queues with general vehicle dispatching strategies

A numerical approach is presented for determining the waiting time distribution in a transient bulk-arrival, bulk-service queue. Vehicle departures from the queue are governed by a general dispatch strategy that includes holding with a variable release function and vehicle cancellations. The waiting time distribution of a customer (in a group) arriving at a given point in time is calculated by simulating the process in discrete time and determining at each step the probability the customer has left the system. The dispatch strategies require knowing the total length of the queue as well as the position a customer holds in the queue. An exact approach is compared to an accurate approximation which is 50 to 100 times faster. Comparisons are made with other approaches in the context of steady-state systems.     

The relationship between material failures and flight hours

If material failures follow a Poisson distribution, then the expected number of failures is exactly proportional to flight hours. However, this article demonstrates that proportionality will not be revealed by simple correlation or regression analysis between monthly flight hours and the number of monthly failures. To test for proportionality, one must instead test the underlying hypothesis that the data follow a Poisson distribution. This article presents three simple tests that may be used for this purpose. The Poisson distribution requires that the mean and variance of the number of failures be equal. This article suggests several alternative models that may be used for samples in which the variance exceeds the mean. First, the mean of the Poisson distribution may itself be randomly distributed across the observational units according to a gamma distribution. If so, the number of failures will have a negative binomial distribution. Second, the mean of the Poisson distribution may depend systematically upon a set of observable explanatory variables. In this case, the Poisson regression model is appropriate. Finally, the mean of the Poisson distribution may contain both a systematic component that depends upon observable variables and a random component. This situation yields a generalized Poisson regression model.     

Algorithms and heuristics for variable-yield lot sizing

We consider the multiperiod lot-sizing problem in which the production yield (the proportion of usable goods) is variable according to a known probability distribution. We review two economic order quantity (EOQ) models for the stationary demand continuous-time problem and derive an EOQ model when the production yield follows a binomial distribution and backlogging of demand is permitted. A dynamic programming algorithm for an arbitrary sequence of demand requirements is presented. Heuristics based on both the EOQ model and appropriate modification of the underlying perfect-yield lot-sizing policies are discussed, and extensive computational evaluation of these heuristics is presented. Two of these heuristics are then modified to include the notion of supply safety stock. The modified heuristics consistently produce near-optimal lot-sizing policies for problems with stationary and time-varying demands.     

Waiting-time distributions for bulk arrival,bulk service queues with vehicle-holding and cancellation strategies

Procedures are developed for numerically calculating the waiting-time distribution for bulk arrival, bulk service queues operated under a vehicle-cancellation or a vehicle-holding strategy, as well as for queues where vehicles depart regardless of the length of the queue. Experiments indicate that the mean and variance of the calculated distribution agree very closely with analytical expressions obtained from transforms. The results can be used to study the service reliability of different dispatching strategies. Alternatively, the results may be used to evaluate simpler approximations for the higher moments.     

The cost of production breaks

Disruptions in the production process can have a serious impact on production costs. Most of the previous literature which addresses the cost impact of production breaks attributes the observed increase in costs solely to “loss of learning.” We develop a mathematical model which implies that breaks in the unit learning curve can occur because of a production break, even under the assumption of no forgetting. In such cases, increases in unit cost can be caused by decreasing returns as the amount of time available to meet the delivery schedule decreases due to the production break.