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Maximizing student opportunities for in-person classes under pandemic capacity reductions
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2022 | Decision Support Systems | Citations: 0
Authors: Cardonha, Carlos; Bergman, David; Day, Robert

Abstract: In this article, we describe the decision support system that was developed for ... Expand

Abstract: In this article, we describe the decision support system that was developed for the assignment of courses to teaching modalities and rooms for the Fall semester of 2020 at the University of Connecticut (UConn). With the adoption of safety/mitigation standards imposed by the COVID-19 pandemic, the seating capacities of rooms were reduced by more than 70%, thus making virtually every existing room assignment for Fall 2020 infeasible. The demand for in-person instruction required the reassignment of a large number of courses to rooms, where not all requests for physical space could be accommodated. In order to maximize opportunities for in-person in struction, UConn introduced a teaching modality in which class meetings are attended on campus by only 50% of the enrolled students. As decision makers were given partial flexibility to assign teaching modalities to classes, the complexity of the assignment problem increased considerably, especially because the real-world instances involved hundreds of rooms and thousands of classes and required a quick solution turnaround in practice. In this article, we introduce this flexible assignment problem and describe the two mixed-integer programming for mulations that were used to solve the real-world instances of the problem; in particular, one of the formulations leverages structural properties presented in this work in order to represent the problem in a more compact way. We explain how we tailored our algorithms to solve the real-world problem, describe the dynamics of the interactive decision support system created in this initiative, and present insights derived from our study. Collapse

Semantic filters: Python integer linear programming

Topics: decision support system digital dashboard decision support covid 19 pandemic

Methods: computational algorithm optimization model experiment mixed-integer programming integer linear programming
On selecting a probabilistic classifier for appointment no-show prediction
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2021 | Decision Support Systems | Citations: 0
Authors: Harris, Shannon L.; Samorani, Michele

Abstract: Appointment no-shows are disruptive to healthcare clinics, and may increase pati ... Expand

Abstract: Appointment no-shows are disruptive to healthcare clinics, and may increase patient waiting time and clinic overtime, resulting in increased clinic costs. Appointment scheduling models typically mitigate the negative effects of no-shows through appointment overbooking. Recent work has proposed a predictive overbooking framework, where a probabilisitic classifier predicts the no-show probability of individual appointment requests, and a scheduling algorithm uses those predictions to optimally schedule appointments. Because predicting noshows is typically an imbalanced classification problem, the preferred classifier is often chosen based upon the area under the receiver operator characteristic curve (AUC), which is a commonly used metric for many other imbalanced classification problems. Contrary to intuition, in this paper we show that employing the AUC to select a classifier results in significantly lower schedule efficiency than using other metrics such as Log Loss or Brier Score. Our computational experiments, validated on large real-world appointment data, suggest that by using Log Loss or Brier Score instead of AUC, practitioners can improve the schedule quality by 3–7%. Collapse

Semantic filters: Python integer linear programming

Topics: Python logistics management

Methods: machine learning experiment parametric test ANOVA logistic regression
Bayesian Stackelberg games for cyber-security decision support
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2021 | Decision Support Systems | Citations: 0
Authors: Zhang, Yunxiao; Malacaria, Pasquale

Abstract: A decision support system for cyber-security is here presented. The system aims ... Expand

Abstract: A decision support system for cyber-security is here presented. The system aims to select an optimal portfolio of security controls to counteract multi-stage attacks. The system has several components: a preventive optimisation to select controls for an initial defensive portfolio, a learning mechanism to estimate possible ongoing attacks, and an online optimisation selecting an optimal portfolio to counteract ongoing attacks. The system relies on efficient solutions of bi-level optimisations, in particular, the online optimisation is shown to be a Bayesian Stackelberg game solution. The proposed solution is shown to be more efficient than both classical solutions like Harsanyi transformation and more recent efficient solvers. Moreover, the proposed solution provides significant security improvements on mitigating ongoing attacks compared to previous approaches. The novel techniques here introduced rely on recent advances in Mixed-Integer Conic Programming (MICP), strong duality and totally unimodular matrices. Collapse

Semantic filters: Python integer linear programming

Topics: decision support system IT security Python

Methods: optimization model case study integer linear programming computational algorithm linear optimization
Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon’s Public Cloud Infrastructure

2019 | HICSS | Citations: 0
Authors: Nwanganga, Frederick; Chawla, Nitesh V; Madey, Gregory

Abstract: Workload modeling in public cloud environments is challenging due to reasons suc ... Expand

Abstract: Workload modeling in public cloud environments is challenging due to reasons such as infrastructure abstraction, workload heterogeneity and a lack of deﬁned metrics for performance modeling. This paper presents an approach that applies statistical methods for distribution analysis, parameter estimation and Goodness-of-Fit (GoF) tests to develop theoretical (estimated) models of heterogeneous workloads on Amazon’s public cloud infrastructure using compute, memory and IO resource utilization data. Collapse

Semantic filters: Python integer linear programming

Topics: cloud computing Linux resource allocation Tencent QQ Python

Methods: simulation descriptive statistic integer linear programming literature study data modeling

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Found 4 articles

Maximizing student opportunities for in-person classes under pandemic capacity reductions

On selecting a probabilistic classifier for appointment no-show prediction

Bayesian Stackelberg games for cyber-security decision support

Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon’s Public Cloud Infrastructure