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SSRN 2022年

作者： Zhao, Ai Bard, Jonathan F. Bickel, J. Eric Graduate Program in Operations Research & Industrial Engineering The University of Texas 204 E. Dean Keeton St. C2200 AustinTX78717-1591 United States

The paper presents a two-stage approach for minimizing the impact of daily disruptions on an airline’s published flight schedule. The problem is characterized by uncertainty in the duration of the disruption and the point in time when its length becomes known. Both a single-commodity network model and multi-commodity network model with side constraints are developed to first determine the flights that are most likely to be affected, and then to adjust their schedules to achieve system-wide optimality. The overall objective is to minimize the weighted sum of total passenger delay costs, cancellation costs, curfew violation costs, and variation from the original schedule. The two types of uncertainty are addressed by examining a range of scenarios that reflect the most likely outcomes. The results provide guidance and a measure of robustness for the flight director as the disruption unfolds. A rolling horizon approach that closely mimics current procedures used by several airlines is also presented to provide a benchmark for comparisons with the two-stage solutions. © 2022, The Authors. All rights reserved.

关键词： Air transportation

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Fast approximation algorithms for multicommodity flow problems 91

Fast approximation algorithms for multicommodity flow proble...

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23rd Annual ACM Symposium on Theory of Computing, STOC 1991

作者： Leighton, Tom Makedont, Fillia Plotkin, Serge Steins, Clifford Tardost, Eva Tragoudas, Spyros Department of Mathematics and Laboratory for Computer Science MIT CambridgeMA United States Computer Science Program University of Texas at Dallas RichardsonTX75083 United States Deparment of Computer Science StanfordCA United States Laboratories Graduate Fellowship MIT CambridgeMA02139 United States School of Operations Research Cornell University IthacaNY United States Computer Science Program University of Texas at Dallas RichardsonTX75063 United States

ISBN: (纸本)0897913973

All previously known algorithms for solving the multicommodity flow problem with capacities are based on linear programming. The best of these algorithms [14] uses a fast matrix multiplication algorithm and takes O(k25n2m5 log(nDU)) time to find an approximate solution, where κ is the number of commodities, n and m denote the number of nodes and edges in the network, D is the largest demand, and U is the largest edge capacity. Substantially more time is needed to find an exact solution. As a consequence, even multicommodit y flow problems with jnst a few commodities are believed to be much harder than single-commodity maximum-flow or minimum-cost flow problems. In thk paper, we describe the first polynomial-Time combinatorial algorithms for approximately solving the multicommodity flow problem. The running time of our randomized algorithm is (up to ,log factors) the same as the time needed to solve κ single-commodity flow problems, thus giving the surprising result that approximately computing a κ-commodity maximum-flow is not much harder than computing about κ single-commodity maximum-flows in isolation. In fact, we prove that a (simple) κ-commodity flow problem can be approximately solved by approximately solving O(κ log2 n) single-commodity minimum-cost flow problems. Our κ-commodity algorithm runs in O(knm log4 n) time with high probability. We also describe a deterministic algorithm that uses an O(κ)-factor more time. Given any multicommodit y flow problem as input, both rdgorithms are guaranteed to provide a feasible solution to a modified fIOW problem in which all capacities are increased by a (1 + c)-factor, or to provide a proof that there is no feasible solution to the original problem. We also describe faster approximation algorithms for multicommodity flow problems with a special structure, such as those that arise in the 'sparsest cutn problems studied in [8, 10, 9], and the uniform concurrent flow problems studied in [13, 9] if κ ≥ √m. © 1991 ACM.

关键词： Approximation algorithms

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Virtual Wearable Sensor Data Generation with Generative Adversarial Networks

Virtual Wearable Sensor Data Generation with Generative Adve...

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2023 Winter Simulation Conference, WSC 2023

作者： Huang, Yining Wan, Hong Chen, Xi North Carolina State University Operations Research Graduate Program 915 Partners Way RaleighNC27606 United States North Carolina State University Department of Industrial and Systems Engineering 915 Partners Way RaleighNC27606 United States Virginia Tech Grado Department of Industrial and Systems Engineering 1145 Perry Street BlacksburgVA24061 United States

ISBN: (纸本)9798350369663

This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offeringaninnovativealternativetotraditionalmetamodel-basedsimulations. We undertake an in-depth analysis of DoppelGANger, a prominent GAN variant for time series data and metadata generation, evaluating its efficiency and efficacy. The sensor data for this investigation was sourced from the National Health and Nutrition Examination Survey, which served as the foundational training set. We scrutinized the synthesized sensor data corresponding to various physical attributes, focusing on the temporal and multi-dimensional statistical properties. Our empirical findings underscore the potential of GANs to adeptly capture the time-dependent correlations and the intricate statistical characteristics inherent in multi-dimensional data. This insight into GANs' capabilities is a crucial step towards more sophisticated synthetic data generation, with significant implications for future applications in wearable technology and personalized health monitoring systems. © 2023 IEEE.

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Hybrid flowshops: an LP based heuristic for the planning level problems

Hybrid flowshops: an LP based heuristic for the planning lev...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： E.H. Aghezzaf A. Artiba S.E. Elmaghraby Department d'informatique et de gestion quantitative Facultés Universitaires Catholiques de Mons Belgium Graduate program in operations research North Carolina State University Raleigh NC USA

In this paper we present an LP based heuristic to a class of planning level problems in hybrid flowshops. The shopfloor under consideration has a certain number of centers arranged in a serial way, each composed of a group of machines working in parallel. We first present the general planning problem modeled as multi-level, parallel processors, multi-item, capacitated, lot-sizing problem with set up times. We suggest an hierarchical approach where parallel processors, multi-item, capacitated, lot-sizing problems with set up times are to be solved. We show how the later problems, in case of identical processors, may be reformulated and solved as trans-shipment problems when the amount of capacity lost in setups are fixed for each period and each processor.

关键词： Lot sizing Electronic mail operations research Process planning Production planning Linear programming Parallel machines Costs

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Journal of Scheduling: Editorial

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Journal of Scheduling 2006年第3期9卷 199-201页

作者： Bard, Jonathan F. Barnhart, Cynthia Graduate Program in Operations Research 1 University Station C2200 University of Texas Austin TX 78712 United States Civil and Environmental Engineering and Engineering Systems Massachusetts Institute of Technology Cambridge MA 02139 United States

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Improved decision rule approximations for multi-stage robust optimization via copositive programming

arXiv

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arXiv 2018年

作者： Xu, Guanglin Hanasusanto, Grani A. Institute for Mathematics and its Applications University of Minnesota MinneapolisMN55455 United States Graduate Program in Operations Research and Industrial Engineering The University of Texas at Austin AustinTX78712-1591 United States

We study decision rule approximations for generic multi-stage robust linear optimization problems. We examine linear decision rules for the case when the objective coefficients, the recourse matrices, and the right-hand sides are uncertain, and examine quadratic decision rules for the case when only the right-hand sides are uncertain. The resulting optimization problems are NP-hard but amenable to copositive programming reformulations that give rise to tight, tractable semidefinite programming solution approaches. We further enhance these approximations through new piecewise decision rule schemes. Finally, we prove that our proposed approximations are tighter than the state-of-the-art schemes and demonstrate their superiority through numerical experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Linear programming

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Distributionally Robust Chance-Constrained Optimal Transmission Switching for Renewable Integration

arXiv

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arXiv 2021年

作者： Zhou, Yuqi Zhu, Hao Hanasusanto, Grani A. The Department of Electrical & Computer Engineering The University of Texas at Austin AustinTX78712 United States The Graduate Program in Operations Research & Industrial Engineering The University of Texas at Austin AustinTX78712 United States

Increasing integration of renewable generation poses significant challenges to ensure robustness guarantees in real-time energy system decision-making. This work aims to develop a robust optimal transmission switching (OTS) framework that can effectively relieve grid congestion and mitigate renewable curtailment. We formulate a two-stage distributionally robust chance-constrained (DRCC) problem that assures limited constraint violations for any uncertainty distribution within an ambiguity set. Here, the second-stage recourse variables are represented as linear functions of uncertainty, yielding an equivalent reformulation involving linear constraints only. We utilize moment-based (mean-mean absolute deviation) and distance-based (∞-Wasserstein distance) ambiguity sets that lead to scalable mixed-integer linear program (MILP) formulations. Numerical experiments on the IEEE 14-bus and 118-bus systems have demonstrated the performance improvements of the proposed DRCC-OTS approaches in terms of guaranteed constraint violations and reduced renewable curtailment. In particular, the computational efficiency of the moment-based MILP approach, which is scenario-free with fixed problem dimensions, has been confirmed, making it suitable for real-time grid operations. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational efficiency

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Adaptive feature selection for hyperspectral data analysis using a binary hierarchical classifier and tabu search

Adaptive feature selection for hyperspectral data analysis u...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： D. Korycinski M. Crawford J.W. Barnes J. Ghosh Center for Space Research University of Texas Austin Austin TX USA Graduate Program in Operations Research and Industrial Engineering University of Texas Austin Austin TX USA Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA

High dimensional inputs coupled with scarcity of labeled data are among the greatest challenges for classification of hyperspectral data. These problems are exacerbated if the number of classes is large. High dimensional output classes can often be handled effectively by decomposition into multiple two-(meta)class problems, where each sub-problem is solved using a suitable binary classifier, and outputs of this collection of classifiers are combined in a suitable manner to obtain the answer to the original multi-class problem. This approach is taken by the binary hierarchical classifier (BHC). The advantages of the BHC for output decomposition can be further exploited for hyperspectral data analysis by integrating a feature selection methodology with the classifier. Building upon the previously developed best bases BHC algorithm with greedy feature selection, a new method is developed that selects a subset of band groups within metaclasses using reactive tabu search. Experimental results obtained from analysis of Hyperion data acquired over the Okavango Delta in Botswana are superior to those of the greedy feature selection approach and more robust than either the original BHC or the BHC with greedy feature selection.

关键词： Hyperspectral imaging Data analysis Tree data structures Training data Feature extraction operations research Industrial engineering Robustness Parameter estimation Computational complexity

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A TWO-STAGE FEDERATED LEARNING APPROACH FOR INDUSTRIAL PROGNOSTICS USING LARGE-SCALE HIGH-DIMENSIONAL SIGNALS

arXiv

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arXiv 2024年

作者： Su, Yuqi Fang, Xiaolei Operations Research Graduate Program North Carolina State University RaleighNC United States Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University RaleighNC United States

Industrial prognostics aims to develop data-driven methods that leverage high-dimensional degradation signals from assets to predict their failure times. The success of these models largely depends on the availability of substantial historical data for training. However, in practice, individual organizations often lack sufficient data to independently train reliable prognostic models, and privacy concerns prevent data sharing between organizations for collaborative model training. To overcome these challenges, this article proposes a statistical learning-based federated model that enables multiple organizations to jointly train a prognostic model while keeping their data local and secure. The proposed approach involves two key stages: federated dimension reduction and federated (log)-location-scale regression. In the first stage, we develop a federated randomized singular value decomposition algorithm for multivariate functional principal component analysis, which efficiently reduces the dimensionality of degradation signals while maintaining data privacy. The second stage proposes a federated parameter estimation algorithm for (log)-location-scale regression, allowing organizations to collaboratively estimate failure time distributions without sharing raw data. The proposed approach addresses the limitations of existing federated prognostic methods by using statistical learning techniques that perform well with smaller datasets and provide comprehensive failure time distributions. The effectiveness and practicality of the proposed model are validated using simulated data and a dataset from the NASA repository. © 2024, CC BY.

关键词： Principal component analysis

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Cargo vehicle combinations: An economic and environmental approach to soybean supply chain in Mato Grosso, Brazil 8

Cargo vehicle combinations: An economic and environmental ap...

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8th International Conference on Information Systems, Logistics and Supply Chain: Interconnected Supply Chains in an Era of Innovation, ILS 2020

作者： Toloi, Rodrigo Carlo Toloi, Marley Nunes Vituri Dos Reis, João Gilberto Mendes De Oliveira Machado, Antônio Carlos Machado, Sivanilza Teixeira Dos Santos Rabelo, Audiene Correia Bonilla, Silvia Helena Federal Institute of Mato Grosso Campus Rondonópolis Mato Grosso Brazil RESUP - Supply Chain Research Group Post-Graduate Program in Production Engineering Paulista University 1212 Dr. Bacelar St. São Paulo SP Brazil NAPOLE - Advanced Production Operations Logistics and Strategy Center Federal Institute of São Paulo 1501 Mogi das Cruzes Avenue Suzano SP Brazil

There is a growing concern about food production, the rational use of energy, and the impacts caused by anthropogenic greenhouse gas emissions on transport systems. With this concern in mind, the objective of this paper is to discuss the economic and environmental performance of different combinations of cargo vehicles. To do so, we explored the flow of soybean from Mato Grosso state to the ports. The results show that the combination of the stump semi-trailer presented the best economic performance, while the combination of the 'rodotrem' semi-trailer presented the best environmental performance. Moreover, this work points out the importance of the quality of the highways on the economic and environmental performance of soybean transportation. © Interconnected Supply Chains in an Era of Innovation - Proceedings of the 8th International Conference on Information Systems, Logistics and Supply Chain, ILS 2020. All rights reserved.

关键词： Greenhouse gases

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