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作者： Thomas Ridremont Ecole Polytechnique de Montreal

学位级别：博士

Nowadays, the design of networks has become a decisive problematic which appears in many fields such as transport or energy. In particular, it has become necessary and important to optimize the way in which networks used to produce, collect or transport energy are designed. We focus in this thesis on electricity produced through wind farms. The production of energy by wind turbines appears more than ever like a good alterna- tive to the electrical production of thermal or nuclear power plants, giving that both of those production can have harmful consequences on the environment. It has then become necessary to optimize the design and construction of such networks. We focus in this thesis on the design of the cabling network which allows to collect and route the energy from the wind turbines to a sub-station, linking the wind farm to the electrical network. In this problem, we know the location of each wind turbine of the farm and the one of the sub-station. We also know the location of possible inter-connection nodes which allow to connect different cables between them. Each wind turbine produces a known quantity of energy and with each cable are associated a cost and a capacity (the maximum amount of energy that can be routed through this cable). The optimization problem that we consider is to select a set of cables of minimum cost such that the energy produced from the wind turbines can be routed to the sub-station in the network induced by this set of cables, without exceeding the capacity of each cable. We focus on cabling networks resilient to breakdowns.

关键词： Operations Research Combinatorial optimization Robust networks design Graph theory mixed integer programming Wind farm cabling networks

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Exploiting Symmetry in Subgraph Isomorphism and Formulating Neural Network Constrained Optimization Problems

Exploiting Symmetry in Subgraph Isomorphism and Formulating ...

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作者： Yang, Dominic Tianli University of California Los Angeles

学位级别：Ph.D., Doctor of Philosophy

In this dissertation, I will cover two distinct topics. In the first part, I will discuss the subgraph isomorphism problem which has a broad range of applications from pattern recognition and bioinformatics to compilers circuit design. In combinatorial problems such as these, symmetry, if unaccounted for, can confound standard solvers leading to significant amounts of redundant work being done. I will introduce multiple approaches for dynamically exploiting this symmetry to accelerate solvers as well as to compactly characterize the set of solutions. I rigorously establish conditions under which from one subgraph isomorphism, potentially exponentially many more may be generated. I also demonstrate through empirical assessment that incorporating symmetry into subgraph search can provide significant reductions in search time and produce many more solutions. In the presence of these huge solution spaces, there still remains the work of determining the actual subgraph isomorphism of interest. In an approach inspired by active learning, I will introduce the problem of querying template vertices for more information to uniquely identify solutions. Multiple different strategies for querying vertices which also incorporate symmetry are established and assessed by rigorous analysis and comprehensive experiments. In the second part, I will discuss methods for formulating neural network constrained optimization problems. With the advent of machine learning, neural networks have been used as a surrogate for complicated physical models, and the incorporation of these networks into optimization problems is a challenge. I will present a direct embedding approach,a mixed integer program, and a nonlinear program with complementarity constraints as methods for formulating these problems to be used with standard optimization solvers. I establish key properties of each methods, and through experiments, I assess the advantages and disadvantages for each approach.

关键词： Graph theory mixed integer programming Neural networks Nonlinear programming Optimization Subgraph matching

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Breaking barriers to design dimensions in nearly orthogonal Latin hypercubes

Breaking barriers to design dimensions in nearly orthogonal ...

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作者： Hernandez, Alejandro S. Naval Postgraduate School

学位级别：博士

A dynamic and extremely complex landscape in security and world events presents problems that challenge all sectors of society to develop efficient means for exploring a wide range of solutions. Similarly, exponential increases in technological capability make it difficult for commercial and governmental leaders to assess those proposed solutions. Computer experimentation is an established method for examining complex models with large numbers of factors. Orthogonal and nearly orthogonal Latin hypercubes are proven techniques for designing simulation experiments. A key property of these efficient, space-filling designs is their ability to explore many factors within a relatively modest number of design points; however, there is a limited inventory of these designs currently available. Those that have been catalogued are usually computationally expensive to produce and have severe restrictions in the number of factors and/or runs that they allow. To remedy this, we present a set of flexible methodologies to create design matrices with little or no correlation-including saturated nearly orthogonal Latin hypercubes. This new family of designs can explore as many factors as there are design points. This research also addresses experiments that include a mixture of continuous and integer variables, some of which have different numbers of value levels.

关键词： Computer Experiments Design of Experiments mixed integer programming Latin Hypercube Optimization Nearly Orthogonal

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Stochastic Models for the Operating Room Scheduling Problem Under Uncertainty

Stochastic Models for the Operating Room Scheduling Problem ...

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作者： Najjarbashi, Amirhossein University of Houston

学位级别：Ph.D., Doctor of Philosophy

Surgical procedures are complex tasks requiring a variety of specialized and expensive resources. They are recognized amongst the most crucial activities in hospitals from social, medical and economic points of view. Accordingly, operating room (OR) management has gained significant attention within the past decade. Specifically, efficient planning and scheduling of operating rooms has become a priority for both practitioners and researchers. Planning problems deal with assigning surgical patients to certain days over a given planning horizon (e.g., a week) considering limited resources (e.g., PACU bed). Scheduling problems address the allocation of patients to the OR and the calculation of surgery start times on a daily basis. One of the major problems associated with the development of accurate OR planning and scheduling strategies is the uncertainty inherent to surgical procedures. Among many sources of uncertainty mentioned in the literature, high variability of surgery durations is identified as the biggest challenge towards developing practical OR schedules. Uncertain surgery durations can cause a large deviation from the expected completion time of all surgery cases scheduled for each day. Larger than average surgery durations cause an extended overtime for surgical teams and waiting time for patients. On the other hand, shorter than expected durations result in unnecessary resource idle time and lost revenue. The goal of the proposed research is to address surgery duration uncertainty and make a trade-off between optimizing average performance and reducing variability of the performance measures via developing risk-based models and solution methods. A risk averse solution method using Conditional Value-at-Risk (CVaR) is proposed to reduce variability on overtime and its associated costs in a daily OR scheduling problem. CVaR is a risk measure that is shown to be effective in simultaneously reducing the expected value and the variance of a performance measure

关键词： Operating room scheduling mixed integer programming Conditional value-at-Risk Chance constraints Decomposition algorithm Uncertainty

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Multi-Objective TT&C Mission Planning Technique for On-Orbit Service

Multi-Objective TT&C Mission Planning Technique for On-Orbit...

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International Conference on Information Science and Cloud Computing Companion (ISCC-C)

作者： Li, Jing Gu, Xiaosong Ye, Gangqiang Xian Satellite Control Ctr State Key Lab Astronaut Dynam Xian 710000 Peoples R China

ISBN: (纸本)9781479952458

The multi-objective TT&C (Tracking, Telemetry and Command) mission planning technique is studied based on the background of both ground-based and space-based TT&C mission to the OOS (On-Orbit Service) type of "one-station to multi-objective". Firstly, the characteristics and constraint condition of OOS TT&C mission are analyzed. Secondly, the space-based and ground-based TT&C mission planning models are established. Finally, using typical OOS mission as an example, the method of TT&C resource planning is simulated for ground-based multi-objective and ISL (Inter-Satellite Link). The optimized design of TT&C events, optimization scheduling of TT&C resources and automatic implementation strategies of TT&C operations are included to the TT&C mission in this paper. The research results show that the ground-based TT&C resource can be saved by using of space-based TT&C resource. The technical reference can be provided for the implementation of future spacecraft OOS by the research results.

关键词： on-orbit service multi-objective TT&C mixed integer programming mission planning resources scheduling

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TECHNICAL NOTE: A SIMPLE MODEL FOR OPTIMIZING THE SINGLE MACHINE EARLY/TARDY PROBLEM WITH SEQUENCE‐DEPENDENT SETUPS

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Production and Operations Management 1992年第2期1卷 225-228页

作者： COLEMAN, B. JAY Department of Management Marketing and Logistics University of North Florida Jacksonville Florida 32216–6699 United States

A simple mixed integer programming model for the N job/single machine scheduling problem with possibly sequence‐dependent setup times, differing earliness/tardiness cost penalties, and variable due dates is proposed and evaluated for computational efficiency. Results indicated that the computational effort required to reach optimality rose with the number of jobs to be scheduled and with decreased variance in due dates. Though computational effort was significant for the largest problems solved, the model remained viable for optimizing research scale problems. Copyright © 1992 Production and Operations Management Society

关键词： DETERMINISTIC JOB SHOP mixed integer programming PRODUCTION/SCHEDULING SINGLE MACHINE

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Development and evaluation of an adaptive transit signal priority system using connected vehicle technology

Development and evaluation of an adaptive transit signal pri...

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作者： Zeng, Xiaosi Texas A&M University

学位级别：Ph.D.

Transit signal priority (TSP) can be a very effective preferential treatment for transit vehicles in congested urban networks. There are two problems with the current practice of the transit signal priority. First, random bus arrival time is not sufficiently accounted for, which've become the major hindrance in practice for implementing active or adaptive TSP strategies when a near-side bus stop is present. Secondly, most research focuses on providing bus priority at local intersection level, but bus schedule reliability should be achieved at route level and relevant studies have been lacking. In the first part of this research, a stochastic mixed-integer nonlinear programming (SMINP) model is developed to explicitly to account for uncertain bus arrival time. A queue delay algorithm is developed as the supporting algorithm for SMINP to capture the delays caused by the interactions between vehicle queues and buses entering and exiting near-side bus stops. A concept of using signal timing deviations to approximate the impacts of TSP operations on other traffic is proposed for the first time in this research. In the second part of the research, the deterministic version of the SMINP model is extended to the arterial setting, where a route-based TSP (R-TSP) model is develop to optimize for schedule-related bus performances on the corridor level. The R-TSP model uses the real-time data available only from the connected vehicle communications technology. Based on the connected vehicle technology, a real-time signal control system that implements the proposed TSP models is prototyped in the simulation environment. The connected vehicle technology is also used as the main detection and monitoring mechanism for the real-time control of the adaptive TSP signal system. The adaptive TSP control module is designed as a plug-in module that is envisioned to work with a modern fixed-time or adaptive signal controller with connected vehicle communications capabilities. Using this TS

关键词： Transit Signal Priority Adaptive Signal Control Connected Vehicle Stochastic programming mixed integer programming Uncertain Arrival Time Timing Deviation Thesis

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Comparative Study on Minimum Uptime and Downtime Constraints Modeling for the Unit Commitment Problem 5

Comparative Study on Minimum Uptime and Downtime Constraints...

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5th International Conference on Power and Energy Technology, ICPET 2023

作者： Xin, Kuo Gu, Huijie Peng, Chaoyi He, Yubin Liang, Shouyu Chen, Yanguang Yu, Pengrui Dispatch and Control Center China Southern Power Grid Guangzhou China Co. Ltd Research and Development Department Beijing China Shanghai University of Finance and Economics School of Information Management Engineering Shanghai China

ISBN: (纸本)9798350339673

This paper examines the impact of different modeling approaches for minimum uptime and downtime constraints in safety-constrained unit commitment (SCUC) problems. Six different approaches are tested on four power generation systems with varying levels of simulated and actual data, as well as small and large-scale optimization problems. The results demonstrate that an increased number of minimum uptime and downtime constraints can hinder problem-solving effectiveness. Furthermore, significant differences in solving effectiveness are observed among the different modeling approaches, even when they have similar constraint numbers. © 2023 IEEE.

关键词： minimum uptime and downtime mixed integer programming SCUC.

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On Algorithmic Advances for Maximum-Entropy Sampling

On Algorithmic Advances for Maximum-Entropy Sampling

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作者： Chen, Zhongzhu University of Michigan

学位级别：Ph.D., Doctor of Philosophy

The maximum-entropy sampling problem (MESP) is a fundamental and challenging combinatorial-optimization problem, at the intersection of information theory, machine learning, and optimization. The goal is to find a maximum-entropy subset of s random variables, from a universe of n correlated Gaussian random variables, which is a means of choosing the s-subset with maximum information. MESP finds application in experimental design (Shewry and Wynn, 1987), spatial statistics (Zidek, Sun, and Le, 2000), financial portfolio selection (Bera and Park, 2008), feature selection (Song and Li`o, 2010), active learning (Qiu, Miller, and Kesidis, 2016), and many references in (Fampa and Lee, 2022, Chapter 4).MESP is an NP-hard problem. Research in this area often focuses on exact algorithms within a branch-and-bound (B&B) framework, as introduced by (Ko, Lee, and Queyranne, 1995). This framework involves the implicit enumeration of potential solutions while maintaining upper and lower bounds on the optimal value of MESP to efficiently discard nonoptimal solutions. Consequently, the solution speed of the B&B method heavily relies on the tightness of these bounds. In this dissertation, we propose enhanced upper- and lower-bounding techniques to expedite the branch-and-bound framework when applied to MESP, facilitating the handling of large-size instances. Our approach includes a “mixing” methodology for combining multiple convex-relaxation upper bounds of MESP to derive superior bounds. We also present a generalization of upper bounds from (Nikolov, 2015; Li and Xie, 2023), which turns out to be the best for many instances. Moreover, we introduce a “general scaling” technique for reducing the integrality gap further, compared to one of the most powerful techniques, “scaling” (Anstreicher, Fampa, Lee, and Williams, 1996). We also address the theoretical void in the “masking” technique, a promising method without much exploration. Additionally, we introduce an efficient, limited-mem

关键词： Maximum-entropy sampling problem mixed integer programming Combinatorial optimization Information theory

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Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach

Predicting COVID-19 Severity Using a Cut-and-Solve Feature S...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Smith, Kenneth Chan, Michael Brandenburg, John Jones, Katarina A Campagna, Shawn R. Garvin, Michael Templeton, Alan R. Jacobson, Daniel Cruchaga, Carlos Climer, Sharlee University of Missouri - St. Louis Department of Computer Science Saint Louis United States Optimization GotSport Jacksonville Beach United States Stord United States University of Tennessee Knoxville Department of Chemistry Knoxville United States The University of Tennessee Department of Chemistry Knoxville United States Oak Ridge National Laboratory Computational Predictive Biology Oak Ridge United States Washington University Department of Biology Saint Louis United States Oak Ridge National Laboratory Biosciences Division Oak Ridge United States Washington University School of Medicine Department of Psychiatry Saint Louis United States

ISBN: (纸本)9798350337488

Individuals with coronavirus disease 2019 (COVID-19) infection present in a variety of ways, ranging from asymptomatic or mild cough, to organ failure or death. One of the major challenges for the medical community is the quick and accurate determination of how COVID-19 will progress in an individual. Herein, we introduce a new Cut-and-Solve based feature selection program for identifying predictive feature sets in heterogeneous data. We analyze proteomics data from Washington University to identify models ranging in size from a single feature up to five. Validation of logistic regression models using area under the curve (AUC) were applied for both a holdout data set and an independent data set from Massachusetts General Hospital. A variety of known and novel biomarkers for COVID-19 severity were identified. The best model for predicting severe (ventilation or death) vs. non-severe infection is achieved for CALCOCO2 and STC1, with an average AUC=0.81. Based on the known severity markers, several different proteomic pathways are identified. Enrichment analysis indicates activity associated with inflammatory response, as well as myelination and cardiac function. © 2023 IEEE.

关键词： COVID-19 Feature Selection mixed integer programming

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