Regression Tolerance Intervals

In this article, we discuss the utility of tolerance intervals for various regression models. We begin with a discussion of tolerance intervals for linear and nonlinear regression models. We then introduce a novel method for constructing nonparametric regression tolerance intervals by extending the well-established procedure for univariate data. Simulation results and application to real datasets are presented to help visualize regression tolerance intervals and to demonstrate that the methods we discuss have coverage probabilities very close to the specified nominal confidence level.     

Reducing Surgical Ward Congestion Through Improved Surgical Scheduling and Uncapacitated Simulation

High surgical bed occupancy levels often result in heightened staff stress, frequent surgical cancellations, and long surgical wait times. This congestion is in part attributable to surgical scheduling practices, which often focus on the efficient use of operating rooms but ignore resulting downstream bed utilization. This paper describes a transparent and portable approach to improve scheduling practices, which combines a Monte Carlo simulation model and a mixed integer programming (MIP) model. For a specified surgical schedule, the simulation samples from historical case records and predicts bed requirements assuming no resource constraints. The MIP model complements the simulation model by scheduling both surgeon blocks and patient types to reduce peak bed occupancies. Scheduling guidelines were developed from the optimized schedules to provide surgical planners with a simple and implementable alternative to the MIP model. This approach has been tested and delivered to planners in a health authority in British Columbia, Canada. The models have been used to propose new surgical schedules and to evaluate the impact of proposed system changes on ward congestion.     

Private Polls and Presidential Policymaking: Reagan as a Facilitator of Change

Did the Reagan administration disregard majority will when craftingits policy initiatives? Did it cater to a narrow partisan constituencyinstead? The answers to these questions will help with an assessmentof Jacobs and Shapiro’s (2000b) hypothesis that presidentssince the late 1970s have used private White House survey researchas a tool to manipulate or assuage centrist public opinion whilemeeting the policy demands of their partisan core supporters,resulting in a decline in presidential responsiveness to majoritywill. Using the actual surveys administered by Richard Wirthlin(Reagan’s pollster) between 1981 and 1983, this articlewill demonstrate the level of consistency between majority opinionon 129 policy issues and Reagan’s behavior through 1984,and it will explore the conditions under which the presidentwas more or less likely to respond to public preferences. Thedata reveal that the Reagan administration was constrained bythe popular will in predicable ways: if the policy issues wereabout domestic concerns, highly popular, and visible in themedia, then the administration acted in line with public preferencesmore than 70 percent of the time. Further, Reagan and his adviserswere selective in responding to party activists: they championedissues drawn from their conservative ideological agenda thatfit with the current tide in public opinion, while sidesteppingother issues dear to party activists that encountered strongmajority resistance. While I do not contest Jacobs and Shapiro’s(2000b) important observation that presidents often use surveyresearch to "craft talk" in an attempt to channel the publicdebate, the evidence here highlights how the president nonethelessremains constrained by the popular will, at least on domesticissues.     

Multiple Rhythmically Varying Prices,the Timing of Factor Demands and Induced Technological Change

ABSTRACT: This Paper the existing literature on optimal capital utilisation and shiftwork patterns by arguing that labour is not the only input to exhibit a price which varies over the time of day week or year Indeed in certain situations as in the w e of capital intensive industries which are heavy consumers of electricity labour costs may not even be the most important consideration The introduction of a second varying input price can completely alter the optimal operating times of production units Once this extension has been adopted it is a fairly easy final step to demonstrate that some firms face a strong inducement to develop technologies which allow them to use their capital and labour independently This type of technological change has become commonplace in recent years but largely unexplained by the existing literature These developments have important implications for the organisation of work patterns and the flexible use of the labour input     

Computing equilibrium in network utility-sharing and discrete election games

Journal of Combinatorial Optimization - This paper studies the computation of pure Nash equilibrium (PNE) in network utility-sharing and discretized Hotelling–Downs games, and the interplay...     

A Multiple Objective Embedded Network Model for Human Resource Planning and an Implementation of the Tchebycheff Method

A multiple objective embedded network model is proposed to model a variety of human resource planning problems including executive succession planning, compensation planning, training program design, diversity management and human systems design. The Tchebycheff Method, an interactive multiple objective programming solution procedure developed by Steuer and Choo [32], is implemented using NETSIDE, a computer routine for solving network problems with side constraints developed by Kennington and Whisman [17]. This paper demonstrates how the network structure common to many types of human resource planning problems can be exploited to improve solution efficiency, and how our approach extends the use of network models in human resource planning by including multiple objectives and extranetwork constraints. An illustrative example demonstrating the modeling and solution approach is presented, and the potential applications of these approaches in two specific areas of human resource planning are discussed.