A STRATEGY FOR INTEGRATING QUANTITATIVE TECHNIQUES INTO THE BUSINESS CURRICULUM*

Quantitative methods courses play an increasingly significant role in the programs of Schools of Business. However, maximum utilization of the concepts can occur only when these quantitative techniques have been integrated into the various functional areas via a faculty knowledgeable with such techniques and their applications. Lacking sufficient stimuli, this integrative process is observed to be one of an evolutionary nature. This article presents a strategy for stimulation of this process.     

THE DEVELOPMENT OF AN INTEGRATIVE CURRICULUM IN AN UNDERGRADUATE BUSINESS PROGRAM*

This paper describes how the core portion of the undergraduate business program at Indiana University was revised to facilitate integration of basic functionally oriented decision making skills and thus help students gain a deeper understanding of the interdependence of business decisions. It relates, in essence, why and how a single computer-oriented case study which demanded a solution to a large-scale, unified finance-marketing-production problem was used at Indiana University, mentions how we plan to accomplish further integration of functional material, and provides some insight into student reaction to what was, in fact, a massive project from both student and administrative points of view.     

ANALYZING GEOGRAPHICALLY DISCRETE WAREHOUSING NETWORKS BY COMPUTER SIMULATION*

This paper describes a comprehensive simulation modeling approach to the problem of locating warehousing facilities in a fashion that minimizes the cost associated with operating a multi-product, multi-source, multi-destination distribution system. The digital simulation model presented in the study is derived within an “industrial dynamics” framework, utilizes input from an existing management information system, and employs a number of relatively simple heuristic procedures to analyze various alternative warehousing networks. Simulation results, in terms of the distribution costs associated with various warehouse locations, are presented for cases involving the effect of provision of a 100% service level, the effect of constraining product availability and/or inventory capacities at various warehouses, and the effect of deleting various warehouses from the existing warehouse network.     

A new heteroscedastic regression to analyze mass loss of wood in civil construction in Brazil

A heteroscedastic regression based on the odd log-logistic Marshall–Olkin normal (OLLMON) distribution is defined by extending previous models. Some structural properties of this distribution are presented. The estimation of the parameters is addressed by maximum likelihood. For different parameter settings, sample sizes and some scenarios, various simulations investigate the performance of the heteroscedastic OLLMON regression. We use residual analysis to detect influential observations and to check the model assumptions. The new regression explains the mass loss of different wood species in civil construction in Brazil.     

Cost/quality tradeoffs for control procedures in information systems

An analytical framework is presented to study the cost/benefit tradeoffs of alternative internal control scenarios designed to ensure quality of outputs from information systems. A mathematical model is used to evaluate the impact that various alternatives have on system performance and cost. The model can be used to compare the benefits gained from enhanced processing with those arising from more effective internal control procedures. Model variables under the control of system designers include placement and effectiveness of internal control procedures, cost and quality of processing activities, both manual and computerized, and cost and quality of correction procedures. The model incorporates a penalty cost incurred by failure to detect and correct errors. The analytical process is illustrated through application to a specific information system.     

Examining Localized Patterns of Air Quality Perception in Texas: A Spatial and Statistical Analysis

Environmental and human health issues associated with outdoor air pollution, such as ozone, sulfur dioxide, and other pollutants in metropolitan regions, are an area of growing concern for both policy officials and the general public. Increasing attention from the news media, new health data, and public debate over the effectiveness of clean air regulations have raised the importance of air quality in the public consciousness. While public perceptions of air quality have been studied thoroughly dating back to the 1960s, little empirical research has been conducted to explain the spatial aspects of these perceptions, particularly at the local level. Although recent studies suggest characteristics of local setting are important in shaping perceptions of air quality, the roles of proximity, neighborhood characteristics, and location have not been clarified. This study seeks to improve understanding of the major factors shaping public perceptions of air quality by examining the spatial pattern of local risk perception, the role of socioeconomic characteristics in forming these perceptions, and the relationship between perceived and scientifically measured air pollution. First, we map the spatial pattern of local air quality perceptions using Geographic Information Systems (GIS) across the Dallas and Houston metropolitan areas. Next, we explain these perceptions through local contextual factors using both bivariate correlations and multivariate regression analysis. Results indicate that perceptions of air quality in the study areas are not significantly correlated with air quality based on readings of air monitoring stations. Instead, perceptions appear to be influenced by setting (urban vs. rural), state identification, access to information, and socioeconomic characteristics such as age, race, and political identification. We discuss the implications of the findings and provide direction on how further research can provide a deeper understanding of the local contextual factors influencing public perceptions.     

Compact labelings for efficient first-order model-checking

We consider graph properties that can be checked from labels, i.e., bit sequences, of logarithmic length attached to vertices. We prove that there exists such a labeling for checking a first-order formula with free set variables in the graphs of every class that is nicely locally clique-width-decomposable. This notion generalizes that of a nicely locally tree-decomposable class. The graphs of such classes can be covered by graphs of bounded clique-width with limited overlaps. We also consider such labelings for bounded first-order formulas on graph classes of bounded expansion. Some of these results are extended to counting queries.     

On the number of separable partitions

Consider partitions of a given set A of n distinct points in general position in ℝ ^d into parts where each pair of parts can be separated by a hyperplane that contains a given set of points E. We consider the problem of counting and generating all such partitions (correcting a classic 1967 result of Harding about the number of such partitions into two parts). Applications of the result to partition problems are presented.     

On the distance paired domination of generalized Petersen graphs <Emphasis Type="Italic">P</Emphasis>(<Emphasis Type="Italic">n</Emphasis>,1) and <Emphasis Type="Italic">P</Emphasis>(<Emphasis Type="Italic">n</Emphasis>,2)

Let G=(V,E) be a graph without an isolated vertex. A set D⊆V(G) is a k -distance paired dominating set of G if D is a k-distance dominating set of G and the induced subgraph 〈D〉 has a perfect matching. The minimum cardinality of a k-distance paired dominating set for graph G is the k -distance paired domination number, denoted by γ _p ^k (G). In this paper, we determine the exact k-distance paired domination number of generalized Petersen graphs P(n,1) and P(n,2) for all k≥1.     

Geometric rounding: a dependent randomized rounding scheme

We develop a new dependent randomized rounding method for approximation of a number of optimization problems with integral assignment constraints. The core of the method is a simple, intuitive, and computationally efficient geometric rounding that simultaneously rounds multiple points in a multi-dimensional simplex to its vertices. Using this method we obtain in a systematic way known as well as new results for the hub location, metric labeling, winner determination and consistent labeling problems. A comprehensive comparison to the dependent randomized rounding method developed by Kleinberg and Tardos (J. ACM 49(5):616–639, 2002) and its variants is also conducted. Overall, our geometric rounding provides a simple and effective alternative for rounding various integer optimization problems.