THE HERMITE DISTRIBUTION AS A MODEL OF DEMAND DURING LEAD TIME FOR SLOW-MOVING ITEMS

Heretofore, the Poisson and the Laplace distributions have been used to model demand during lead time for slow-moving items. In this paper, we present a Poisson-like distribution called the Hermite. The advantage of the Hermite is that it is as simple to use as the Poisson and the Laplace are. Moreover, the Hermite is the exact distribution of demand during lead time when unit demand is Poisson, P(Λ), and lead time is normally distributed, N(μ, σ²), so long as (μ/σ²)≥Λ. Thus, the Hermite can enhance the accuracy of analysis as well as add to the tools available to the analyst.     

An Evolutionary Algorithm for Sequencing Production on a Paced Assembly Line

A new sequencing method for mixed‐model assembly lines is developed and tested. This method, called the Evolutionary Production Sequencer (EPS) is designed to maximize production on an assembly line. The performance of EPS is evaluated using three measures: minimum cycle time necessary to achieve 100% completion without rework, percent of items completed without rework for a given cycle time, and sequence “smoothness.” The first two of these measures are based on a simulated production system. Characteristics of the system, such as assembly line station length, assembly time and cycle time, are varied to better gauge the performance of EPS. More fundamental variation is studied by modeling two production systems. In one set of tests, the system consists of an assembly line in isolation (i.e., a single‐level system). In another set of tests, the production system consists of the assembly line and the fabrication system supplying components to the line (i.e., a two‐level system). Sequence smoothness is measured by the mean absolute deviation (MAD) between actual component usage and the ideal usage at each point in the production sequence. The performance of EPS is compared to those of well‐known assembly line sequencing techniques developed by Miltenburg (1989), Okamura and Yamashina (1979), and Yano and Rachamadugu (1991). EPS performed very well under all test conditions when the criterion of success was either minimum cycle time necessary to achieve 100% production without rework or percent of items completed without rework for a given cycle time. When MAD was the criterion of success, EPS was found inferior to the Miltenburg heuristic but better than the other two production‐oriented techniques.     

Dogmatism and Systems Development Personnel Performance

This research explores the relationship between the personal variable, dogmatism, and the performance evaluations of systems analysts within a Fortune 500 company. The Rokeach Dogmatism Scale was used to measure the analysts’ level of dogmatism. The analysts’ performance measurement was the product of the company's annual evaluation process. The results of the research indicate that personnel with the highest dogmatism scores generally receive the extreme performance ratings. The results of this study provide further support to consider dogmatism as an important personal variable influencing systems analyst job performance.     

THE ROLE OF NATIONAL DEBTS IN THE DETERMINATION OF THE YEN‐DOLLAR EXCHANGE RATE

An intertemporal optimization model is developed to examine the determinants of the long‐run nominal yen‐dollar exchange rate in the presence of national debts. The model is tested empirically using data from Japan and the United States. The proposed theoretical specification is well supported by the data and shows that relative national debts as well as monetary and financial factors may play a significant role in the determination of the long‐run nominal exchange rate between the yen and the dollar. (JEL F31, G11, G15)     

Posterior sampling in two classes of multivariate fractionally integrated models: corrigendum to Ravishanker,N. and B. K. Ray (1997) Australian Journal of Statistics 39 (3), 295–311

We discuss posterior sampling for two distinct multivariate generalisations of the univariate autoregressive integrated moving average (ARIMA) model with fractional integration. The existing approach to Bayesian estimation, introduced by Ravishanker & Ray, claims to provide a posterior‐sampling algorithm for fractionally integrated vector autoregressive moving averages (FIVARMAs). We show that this algorithm produces posterior draws for vector autoregressive fractionally integrated moving averages (VARFIMAs), a model of independent interest that has not previously received attention in the Bayesian literature.     

Gross primary productivity of a large metropolitan region in midsummer using high spatial resolution satellite imagery

Although gross primary productivity (GPP) is estimated with remote sensing over large regions of Earth, urban areas are usually excluded due to the lack of light use efficiency (LUE) parameters for urban vegetation and the spatial heterogeneity of urban land cover. Here, we estimated midsummer GPP, both within and among vegetation and land-use types, across the Minneapolis-Saint Paul, Minnesota metropolitan region. We derived LUE parameters for urban vegetation types using estimates of GPP from tree sap flow and eddy covariance CO₂ flux observations, and from fraction of absorbed photosynthetically active radiation based on 2 m resolution WorldView-2 satellite imagery. Mean GPP per unit land area (including vegetation, impervious surfaces, and soil) was 2.64 g C m^?2 d^?1, and was 4.45 g C m^?2 d^?1 per unit vegetated area. Mapped GPP estimates were within 11.4% of estimates from independent tall tower eddy covariance measurements. Turf grass GPP had a larger coefficient of variation (0.18) than other vegetation classes (~0.10). Vegetation composition was largely consistent across the study area. Excluding golf courses, mean land-use GPP for the total study area varied more by percent vegetation cover (R²?=?0.98, p?<?0.001) than by variability within vegetation classes (R²?=?0.21, p?=?0.19). Urban GPP in general was less than half that of natural forests and grasslands in the same climate zone.     

Risk and protective factors for recent alcohol use among African-American youth

A total of 7488 7th-12th grade African-American students completed a survey assessing factors associated with recent alcohol use. Results indicated that 13.6% used alcohol in the past month. A series of odds ratios revealed that making good grades, participating in school activities, attending church, and having parents/teachers talk about the dangers of alcohol and set/enforce rules regarding alcohol were associated with decreased recent use. Participating in risky behaviors such as getting into trouble, skipping school, and having friends who use alcohol and other drugs was directly related to recent use. Prevention specialists should encourage parents/teachers to engage youth in family, school, and community activities to deter alcohol use. Results may assist youth health professionals in developing prevention programs aimed at African-American youth.