Education CREATIVE THINKING AND INNOVATIVE EDUCATION IN THE DECISION SCIENCES

Creative problem solving is seldom addressed directly in the decision sciences literature. The first half of this paper reviews current thinking about creativity and its educational importance. The remainder addresses creative problem-solving processes and, in particular, the process developed by Parnes, Noller and Biondi [24] and Osborn [23]. This process has been integrated into an OR/MS problem-solving course which is described. Conclusions and implications for decision sciences education and practice in general are discussed.     

A DISCRETE DYNAMIC OPTIMIZATION MODEL FOR MADE-TO-ORDER COST ANALYSIS

A model is presented that yields optimal production rates for a firm producing a contracted order. The model is unique in that it considers the influence of production rate and learning on total program cost. An application to the specific characteristics of two military production programs is presented. As demonstrated by the application, models of this type may be used as decision-making tools when negotiating the cost impact of contract modifications.     

The multinational transfer of competency-based foster parent assessment, selection, and training: a nine-country case study.

The UN Convention on the Rights of the Child mandates (Articles 19 and 20) that alternative care be available to protect children from maltreatment by parents and caregivers. Increasingly, countries around the world have recognized the benefits to child well-being of family-based care as an alternative to institutional care. The special if not extraordinary needs of children separated from parents because of abuse, neglect, and maltreatment and placed with foster families requires a commensurate effort to develop foster families who have special if not extraordinary skills. Methods that are effective and replicable across countries offer significant advantages, reducing investments in policies and practices that otherwise would have to be developed independently, country by country. This case study describes the factors that contributed to the transfer of knowledge in the assessment, selection, and training of foster parents and the impact to date.     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

Conditional moving linear regression: Modeling the recruitment process for ALLHAT

Effective recruitment is a prerequisite for successful execution of a clinical trial. ALLHAT, a large hypertension treatment trial (N = 42,418), provided an opportunity to evaluate adaptive modeling of recruitment processes using conditional moving linear regression. Our statistical modeling of recruitment, comparing Brownian and fractional Brownian motion, indicates that fractional Brownian motion combined with moving linear regression is better than classic Brownian motion in terms of higher conditional probability of achieving a global recruitment goal in 4-week ahead projections. Further research is needed to evaluate how recruitment modeling can assist clinical trialists in planning and executing clinical trials.     

Double trouble: work–family conflict and well-being for second job holders

The purpose of this study was to provide an initial investigation into the work–family conflict (WFC) experiences of individuals who hold a second job. Specifically, we proposed two opposing theoretical arguments regarding the relationship between WFC and four measures of well-being: life satisfaction, physical health, emotional exhaustion, and exercise for individuals who hold two jobs. In a sample of individuals working two jobs, we tested whether the effects of WFC arising from the first job and from the second job exerted an additive or interactive effect on employee well-being. Our results indicated that for emotional exhaustion, physical symptoms, and life satisfaction, the effects of WFC from two jobs are interactive; but that for exercise, the effects of WFC from two jobs are additive. In both cases, the additional WFC experienced as a result of working a second job contributed to the prediction of employee well-being over and above WFC from the first job. Results are discussed within the context of relevant theory, and suggestions are made for future research.     

Phase I monitoring and change point estimation of autocorrelated poisson regression profiles

The objective of this paper is to study the Phase I monitoring and change point estimation of autocorrelated Poisson profiles where the response values within each profile are autocorrelated. Two charts, the SLRT and the Hotelling's T², are proposed along with an algorithm for parameter estimation. The detecting power of the proposed charts is compared using simulations in terms of the signal probability criterion. The performance of the SLRT method in estimating the change point in the regression parameters is also evaluated. Moreover, a real data example is presented to illustrate the application of the methods.