Yield improvement and yield variability reduction in an EOQ model with planned shortages and random yield

This paper reports the results of an investigation of a set of continuous time, constant demand inventory models under the condition of yield uncertainty. Specifically, the impact of yield improvement programs on lot size, backorder level, and the resulting costs are examined. Models for improving yield rate and reducing yield variability are developed and examined through a series of numerical exercises. In addition, a model for the simultaneous improvement of yield rate and yield variability is presented for the case where there is a relationship between the mean and variance of the yield distribution. In all cases, investment programs improve the picture with respect to manufacturing yield for processes which are not necessarily under statistical control.     

The factorial structure of the Dutch translation of the Inventory of Interpersonal Problems: A test of the long and short versions.

In this article, the authors study the factorial structure of 2 versions (64 items and 32 items) of the Dutch translation of the Inventory of Interpersonal Problems (IIP; L. M. Horowitz, L. E. Alden, J. S. Wiggins, & A. L. Pincus, 2000) in both a clinical sample (n = 382) and a student sample (n = 287). First, the authors test whether both inventories are constituted by correlated uni-dimensional subscales, and they conclude that this is true only for the short version. Second, the authors study whether the correlations between the subscales follow a circumplex pattern. This proves to be true for both versions. Coefficients alpha and correlations between the IIP-64 and IIP-32 subscales are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Assessing Consequences of Substance Use: Psychometric Properties of the Inventory of Drug Use Consequences.

Negative consequences are an important component of the substance use change process, yet no standardized measure exists to assess consequences of use. This study evaluated the psychometric properties of the Inventory of Drug Use Consequences, a self-report measure assessing drinking and drug use consequences. Participants (N=252) were assessed at entry into outpatient substance abuse treatment. Items loaded strongly on 1 factor, and internal consistency of the total scale was high. Fifteen items with the highest correlations with the total scale were chosen to represent the construct. The shortened measure exhibited good internal consistency, convergent and discriminant validity, and detected change over time and may be the measure of substance use consequences that is lacking in the field. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub-shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.     

A bi-criteria model for the inventory aggregation problem under risk pooling

Inventory aggregation, also called Risk Pooling, is one of the most efficient ways to reduce the level of safety stocks thereby reducing inventory across the supply chain. Determining the best level of aggregation is a difficult problem and needs extensive study of all the possible scenarios that can affect this decision. Minimizing costs in a supply chain is no longer the sole priority of businesses. Maintaining a high level of responsiveness is also considered equally important. The conflicting nature of these two criteria makes the solution of the problem difficult. In this paper, we develop a bi-criteria nonlinear stochastic integer programming model to determine the best supply chain distribution network to meet customer demands, where minimizing costs while maintaining high levels of responsiveness is important. We develop a two-stage optimization algorithm to solve this problem.     

供应链库存管理的几项措施

供应链库存管理与某一部门的管理有极大的不同，它涉及到各部门，各企业之间存在着的错综复杂的工序关系和不确定性。     

确定型存贮模型的系统动态描述

本文尝试用系统动态学的研究方法于存贮理论，建立确定型存贮问题的系统动态优化模型，并对模型作了数学推导。     

Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification

Multi-criteria ABC inventory classification (MCIC), which aims to classify inventory items by considering more than one criterion, is one of the most widely employed techniques for inventory control. This paper suggests a cross-evaluation-based weighted linear optimization (CE-WLO) model for MCIC that incorporates a cross-efficiency evaluation method into a weighted linear optimization model for finer classification (or ranking) of inventory items. The present study demonstrated the inventory-management-cost effectiveness and advantages of the proposed model using a simulation technique to conduct a comparative experiment with the previous, related investigations. We established that the proposed model enables more accurate classification of inventory items and better inventory management cost effectiveness for MCIC, specifically by mitigating the adverse effect of flexibility in the choice of weights and yielding a unique ordering of inventory items.     

Advancements in the design and validation of an air pollution integrated assessment model for Spain

This paper describes the design and application of the Atmospheric Evaluation and Research Integrated model for Spain (AERIS). Currently, AERIS can provide concentration profiles of NO₂, O₃, SO₂, NH₃, PM, as a response to emission variations of relevant sectors in Spain. Results are calculated using transfer matrices based on an air quality modelling system (AQMS) composed by the WRF (meteorology), SMOKE (emissions) and CMAQ (atmospheric-chemical processes) models. The AERIS outputs were statistically tested against the conventional AQMS and observations, revealing a good agreement in both cases. At the moment, integrated assessment in AERIS focuses only on the link between emissions and concentrations. The quantification of deposition, impacts (health, ecosystems) and costs will be introduced in the future. In conclusion, the main asset of AERIS is its accuracy in predicting air quality outcomes for different scenarios through a simple yet robust modelling framework, avoiding complex programming and long computing times.     

联合补充库存的拉格朗日松弛与退火算法

针对确定性需求的联合生产补充(JRP)问题,有多种启发式算法,但这些算法或不能保证最优或计算复杂。本文利用现代优化方法模拟退火算法和拉格朗日松驰算法,设计出一种新的算法,通过算例表明该算法可得出较优的可行解,可以更有效地解决实际问题。