Browsing by Author "Nino, Luisa"

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A simulation of variability-oriented sequencing rules on block surgical scheduling
(2016-12) Nino, Luisa; Harris, Sean; Claudio, David
Surgery scheduling has received considerable attention in recent years. Block schedules, in which surgeon groups utilize the OR for whatever surgeries they have scheduled for the day, present additional challenges to schedulers. While mean operation times are often used as the primary factor in scheduling strategies, the variability of these operations is not. Recent research suggests that sequencing surgeries based on their variation may decrease the number of late surgery starts. This article builds upon this emerging methodology of variability-oriented sequencing rules for block schedules. Discrete event simulation was used to examine the effectiveness of different sequencing algorithms in reducing the number of behind schedule surgeries and their magnitude. The number and magnitude of tardy surgeries and the patient waiting time were significantly improved by an average of 40% with the proposed scheduling strategies. Additional simulations explored several variations of the variability-based scheduling methodology.
A Statistical Comparison between Different Multicriteria Scaling and Weighting Combinations
(2020-05) Harris, Sean; Nino, Luisa; Claudio, David
Multicriteria decision making presents several challenges for researchers. These include selecting a technique that will produce accurate results but not require too much time or resources. Researchers have been comparing different techniques for several years, though a comprehensive study of using many different techniques for the same set of problems is relatively new. Additionally, knowing if the difference in scores between alternatives is significant or not presents another challenge. The use of confidence intervals has recently been employed by researchers to examine whether results are actually statistically different from one another. This study uses 21 different scaling-weighting combinations and confidence intervals on six different decision-making problems to measure their ability to produce unambiguous results. Linear normalization as a scaling technique tends to be the best at identifying one or two clear winners while avoiding complete ambiguity. Despite the variety of combinations used, several common themes emerge across the decision problems.