A Comparison of Due Date Setting,Resource Assignment,and Job Preemption Heuristics for the Multiproject Scheduling Problem*

This research examines a heuristic, rule-based approach for setting due dates in a multiproject, multijob, or assembly shop. Due date estimation is a challenging problem because the operating environment is capacitated, involves the allocation of multiple resources, and allows for the preemption of resources from one project or job to another. The dynamic, continuous arrival of new jobs or orders frequently results in the preemption of resources through the application of managerially determined priority policies. These preemption policies have a significant impact on the ultimate completion time of a job or a project. A three-factor, full-factorial computer simulation experiment is used to assess the relative effectiveness of combinations of four due date setting heuristics, five resource assignment heuristics, and three resource preemption heuristics. Recommendations are made for the selection of due date and resource assignment heuristic combinations under the three preemption policies examined.     

A SIMULATION GAME FOR TEACHING SERVICE‐ORIENTED SUPPLY CHAIN MANAGEMENT: DOES INFORMATION SHARING HELP MANAGERS WITH SERVICE CAPACITY DECISIONS?*

For decades, the Beer Game has taught complex principles of supply chain management in a finished good inventory supply chain. However, services typically cannot hold inventory and can only manage backlogs through capacity adjustments. We propose a simulation game designed to teach service‐oriented supply chain management principles and to test whether managers use them effectively. For example, using a sample of typical student results, we determine that student managers can effectively use end‐user demand information to reduce backlog and capacity adjustment costs. The game can also demonstrate the impact of demand variability and reduced capacity adjustment time and lead times.     

Barcoding,linear and nonlinear analysis of full-day leg movements in infants with typical development and infants at risk of developmental disabilities: Cross-sectional study

Traditional methods do not capture the multidimensional domains and dynamic nature of infant behavioral patterns. We aim to compare full-day, in-home leg movement data between infants with typical development (TD) and infants at risk of developmental disabilities (AR) using barcoding and nonlinear analysis. Eleven infants with TD (2–10 months) and nine infants AR (adjusted age: 2–14 months) wore a sensor on each ankle for 7 days. We calculated the standard deviation for linear variability and sample entropy (SampEn) of leg acceleration and angular velocity for nonlinear variability. Movements were also categorized into 16 barcoding states, and we calculated the SampEn and proportions of the barcoding. All variables were compared between the two groups using independent-samples t-test or Mann-Whitney U test. The AR group had larger linear variability compared to the TD group. SampEn was lower in the AR group compared to TD group for both acceleration and angular velocity. Two barcoding states’ proportions were significantly different between the two groups. The results showed that nonlinear analysis and barcoding could be used to identify the difference of dynamic multidimensional movement patterns between infants AR and infants with TD. This information may help early diagnosis of developmental disabilities in the future.     

Understanding Patterns of Publishing Activity in Academic Research Occupations*

While it is widely understood that faculty in various disciplines tend to publish at different rates and in different forms, knowledge of these differences is too limited to facilitate systematically differentiated performance appraisal and reward systems. In this study, theory concerning knowledge production system characteristics as influences on individual performance is applied to academic research occupations using a classification scheme developed by Biglan [4]. Regression analysis is applied to a general (industry-wide) sample of United States faculty, with publishing patterns as performance measures. Each dimension of the classification scheme is found to have predictive validity. Output patterns are consistent with a conceptualization of research occupations in terms of (1) transformational/technological processes, (2) research mission, and (3) input/subject matter characteristics. The results offer a basis for generating disciplinary publishing norms and differentiated reward systems.     

ONE APPROACH TO REALLOCATING INSTRUCTIONAL RESOURCES: THE HOUSTON MODEL

In this paper a model for deciding which instructional departments should receive and surrender new or reallocatable resources is developed. It combines a two-dimensional assessment of resource needs based on instructional characteristics and the academic priority of the department to arrive at resource allocation priorities. Unusual features of the model are its reliance on judgments of departmental centrality to institutional mission as the discriminating variable in ascribing academic priorities and the careful avoidance of “halo effects” through independent assessment of resource needs and academic priorities. The model is sensitive enough to discriminate between competing claims for scarce resources while retaining a simplicity of logic that makes its mechanics understandable to those with little grounding in the decision sciences.     

FINANCIAL FRAGILITY,LIQUIDITY, AND ASSET PRICES

We define a financial system to be fragile if small shocks have disproportionately large effects. In a model of financial intermediation, we show that small shocks to the demand for liquidity cause either high asset‐price volatility or bank defaults or both. Furthermore, as the liquidity shocks become vanishingly small, the asset‐price volatility is bounded away from zero. In the limit economy, with no shocks, there are many equilibria. However, if banks face idiosyncratic liquidity shocks, then the only equilibria that are robust to the introduction of small aggregate risk involve stochastic consumption as well as volatile asset, prices. (JEL: D5, D8, G2)     

Bayesian modelling of effects of prenatal alcohol exposure on child cognition based on data from multiple cohorts

High levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, but the exact nature of the dose-response relationship is less well understood. To investigate this relationship, data were assembled from six longitudinal birth cohort studies examining the effects of PAE on cognitive outcomes from early school age through adolescence. Structural equation models (SEMs) are a natural approach to consider, because of the way they conceptualise multiple observed outcomes as relating to an underlying latent variable of interest, which can then be modelled as a function of exposure and other predictors of interest. However, conventional SEMs could not be fitted in this context because slightly different outcome measures were used in the six studies. In this paper we propose a multi-group Bayesian SEM that maps the unobserved cognition variable to a broad range of observed outcomes. The relation between these variables and PAE is then examined while controlling for potential confounders via propensity score adjustment. By examining different possible dose-response functions, the proposed framework is used to investigate whether there is a threshold PAE level that results in minimal cognitive deficit.     

DEMAND MANAGEMENT: THE EVALUATION OF PRICE AND DUE DATE NEGOTIATION STRATEGIES USING SIMULATION

The research considers the problem of demand management in a firm where the firm's historical delivery service level reputation influences the number of quotation requests from its potential customers. Customers have a maximum and the firm has a minimum net price to due date tradeoff curve for each job. The demand management function bargains with the customer over price and promised due date. Bargaining finishes either with an agreed price and delivery date or with the customer refusing the firm's bid and placing the order elsewhere. The firm's objective is to maximize its long-term net revenue. The firm's demand management negotiation strategy guides this bidding process. The research demonstrates the use of simulation to test different demand management bidding and negotiation strategies for different market and firm scenarios. The demonstration uses 16 scenarios to test the different demand management negotiation strategies with a model of a classical job shop in a classical market. The investigation examines finite scheduling-based due date estimation methods, as well as the more traditional parameter-based methods. This demonstration shows that it is possible to test different bidding policies, using a simulation model of a firm and its customers, and to obtain usable results.