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Integrated aircraft-path assignment and robust schedule design with cruise speed control

COMPUTERS & OPERATIONS RESEARCH 2017年 84卷 127-145页

作者： Safak, Ozge Gurel, Sinan Akturk, M. Selim Bilkent Univ Dept Ind Engn TR-06800 Bilkent Turkey Middle Tech Univ Dept Ind Engn TR-06800 Ankara Turkey

Assignment of aircraft types, each having different seat capacity, operational expenses and availabilities, critically affects airlines' overall cost. In this paper, we assign fleet types to paths by considering not only flight timing and passenger demand, as commonly done in the literature, but also operational expenses, such as fuel burn and carbon emission costs associated with adjusting the cruise speed to ensure the passenger connections. In response to flight time uncertainty due to the airport congestions, we allow minor adjustments on the flight departure times in addition to cruise speed control, thereby satisfying the passenger connections at a desired service level. We model the uncertainty in flight duration via a random variable arising in chance constraints to ensure the passenger connections. Nonlinear fuel and carbon emission cost functions, chance constraints and binary aircraft assignment decisions make the problem significantly more difficult. To handle them, we use mixed-integer second order cone programming. We compare the performance of a schedule generated by the proposed model to the published schedule for a major U.S. airline. On the average, there exists a 20% overall operational cost saving compared to the published schedule. To solve the large scale problems in a reasonable time, we also develop a two-stage algorithm, which decomposes the problem into planning stages such as aircraft-path assignment and robust schedule generation, and then solves them sequentially. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Fleet type assignment Airline scheduling Cruise time controllability second order conic programming Chance constraints

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Sequential Quadratic programming (SQP) Based Optimal Power Flow Methodologies for Electric Distribution System With High Penetration of DERs

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第3期60卷 4810-4820页

作者： Hasan, Md Shamim Chowdhury, Md Mahmud-Ul-Tarik Kamalasadan, Sukumar Univ North Carolina Charlotte Power Energy & Intelligent Syst Lab Charlotte NC 28223 USA

This article proposes a centralized and distributed model based on nonlinear programming (NLP) for optimal power flow (OPF) analysis in power distribution networks, solved with the Sequential Quadratic programming (SQP) algorithm. The paper proposes the method and illustrates the necessary conditions for the global optimality of the solution. The main advantages of the methods are obtaining global optimal solutions in less than a minute (for more than 2000 nodes with high penetration of DERs) without approximations and relaxations in power flow equations and improving scalability by reducing the number of iterations significantly for both centralized and distributed OPF. The OPF analysis models have been simulated in several standard distribution networks with a wide range of DER penetration. The solution from the proposed model is compared with an interior point method (IPM) based NLP algorithm (NLP-IPM) and second-order conic programming (SOCP) algorithm-based convex OPF analysis method. The analysis confirms the global optimality of the proposed algorithm and demonstrates superior convergence and accuracy compared to other OPF methods.

关键词： Mathematical models Load flow Voltage control Voltage Computational modeling Reactive power Power systems Bus injection model distributed energy resources interior point method optimal power flow (OPF) second order conic programming sequential quadratic programming

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Localized support vector regression for time series prediction

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NEUROCOMPUTING 2009年第10-12期72卷 2659-2669页

作者： Yang, Haiqin Huang, Kaizhu King, Irwin Lyu, Michael R. Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

Time series prediction, especially financial time series prediction, is a challenging task in machine learning. In this issue, the data are usually non-stationary and volatile in nature. Because of its good generalization power, the support vector regression (SVR) has been widely applied in this application. The standard SVR employs a fixed epsilon-tube to tolerate noise and adopts the l(p)-norm (p = 1 or 2) to model the functional complexity of the whole data set. One problem of the standard SVR is that it considers data in a global fashion only. Therefore it may lack the flexibility to capture the local trend of data;this is a critical aspect of volatile data, especially financial time series data. Aiming to attack this issue, we propose the localized support vector regression (LSVR) model. This novel model is demonstrated to provide a systematic and automatic scheme to adapt the margin locally and flexibly;while the margin in the standard SVR is fixed globally. Therefore, the LSVR can tolerate noise adaptively. The proposed LSVR is promising in the sense that it not only captures the local information in data, but more importantly, it establishes connection with several models. More specifically: (1) it can be regarded as the regression extension of a recently proposed promising classification model, the Maxi-Min Margin Machine: (2) it incorporates the standard SVR as a special case under certain mild assumptions. We provide both theoretical justifications and empirical evaluations for this novel model. The experimental results on synthetic data and real financial data demonstrate its advantages over the standard SVR. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.

关键词： Support vector regression second order conic programming Time series prediction

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A novel auto-pruned ensemble clustering via SOCP

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2024年第3期32卷 819-841页

作者： Ucuncu, Duygu Akyuz, Sureyya Gul, Erdal Bahcesehir Univ Fac Engn & Nat Sci Dept Math TR-34353 Istanbul Turkiye Yildiz Tech Univ Fac Arts & Sci Dept Math TR-34220 Istanbul Turkiye

Operations Research (OR) plays a crucial role in strategic decision-making in today's business world;it uses complex algorithms and data analytic to provide decision-makers with the necessary information. The proposed study presents a novel ensemble clustering for decision making in various disciplines including OR problems which introduce a second-order conic optimization model. This method provides a significant advantage over the traditional difference of convex programming by continuously and convexly solving integer programming. The model optimizes the balance between accuracy and diversity, resulting in the selection of the best candidates for prediction. The study's remarkable contribution lies in the automatic sub-ensemble selection while optimizing for accuracy and diversity. The model has been verified using real data and achieves competitive prediction performance. Furthermore, this approach illustrates how OR can be utilized to enhance ensemble clustering and decision-making.

关键词： Strategic decision support Ensemble clustering Auto-pruning Machine learning second order conic programming

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conic programming approach for static voltage stability analysis in radial networks

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IET GENERATION TRANSMISSION & DISTRIBUTION 2007年第2期1卷 203-208页

作者： Jabr, R. A. Pal, B. C. Univ Notre Dame Elect Comp & Commun Engn Dept Zouk Mikhael Zouk Mosbeh Lebanon Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2BT England

That the problem of computing the capacity limit of a radial distribution system can be formulated as a second-order cone program is shown. The implications of the conic programming formulation are 2-fold. First, the load capability of the radial system can be obtained using existing efficient implementations of polynomial time interior-point algorithms, thus avoiding the need for running a sequence of load flow solutions. secondly, the conic objective function yields a voltage stability indicator (SI). This indicator quantifies the maximum percentage by which the current load profile can be uniformly increased before voltage collapse occurs. The proposed method is validated by computing the load capability and voltage SIs of 11 different distribution systems. Comparisons are carried out with five previously published voltage SIs.

关键词： static voltage stability distribution networks Power system control Distribution networks power system stability load capability load flow radial distribution network capacity optimisation second order conic programming polynomial time interior-point algorithms Optimisation techniques voltage stability indicator

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Energy Efficient Coordinated Beamforming Design in Multi-Cell Multicast Networks

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IEEE COMMUNICATIONS LETTERS 2015年第6期19卷 985-988页

作者： He, Shiwen Huang, Yongming Jin, Shi Yang, Luxi Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Jiangsu Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China

In this letter, we study energy efficient physical layer multicasting in multi-cell networks where each base station is equipped with multiple antennas, and transmits a common message using a single beamformer to multiple users in the same cell. The goal of our coordinated beamforming design is to maximize the worst case system energy efficiency while guaranteeing that received signal-to-interference-plus-noise ratio (SINR) at each user is above a predetermined threshold. The considered optimization problem is in a non-convex fractional form and hard to tackle. We propose to first transform the original problem into a tractable optimization problem which is then solved by developing an iterative optimization algorithm with provable convergence. Numerical results further validate the effectiveness of our proposed algorithm and show that the developed algorithm converges to a stable point in a limited number of iterations.

关键词： Physical multicasting energy efficiency maximization fractional programming beamforming and power allocation second order conic programming

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On second order cone programming approach to two-stage network data envelopment analysis

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期309卷 953-956页

作者： Roshdi, Israfil Mahdiloo, Mahdi Arjomandi, Amir Margaritis, Dimitris Islamic Azad Univ Dept Basic Sci Semnan Branch Semnan Iran Univ Wollongong Fac Business & Law Sch Business Wollongong Australia Univ Auckland Dept Accounting & Finance Auckland New Zealand

In two recent papers, [Guo, C., Wei, F., & Chen, Y. (2017). A note on second order cone programming ap-proach to two-stage network data envelopment analysis. European Journal of Operational Research, 263(2), 733-735;and Chen, K., & Zhu, J. (2017). second order cone programming approach to two-stage network data envelopment analysis. European Journal of Operational Research, 262(1), 231238] converted the gen-eral multiplicative two-stage DEA (data envelopment analysis) network model into a parametric/single second order conic programming (SOCP) problem. In this paper, we (i) note that the proposed transfor-mation cannot result in an equivalent mathematical program in view of the fact that the general multi-plicative two-stage DEA model is basically a nonconvex optimisation problem, possibly with several local and global optimums, whereas a single SOCP is a convex optimisation;(ii) demonstrate that there is a subtle technical error contained in the main transformation process proposed in both papers;(iii) illus-trate, by providing a numerical counterexample, that the optimal solution obtained from the resulting single SOCP problem might be substantially different from the original model's optimum;and (iv) offer some computational remark for solving the multiplicative/additive two-stage DEA model. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Data envelopment analysis Network production process Two-stage efficiency measurement second order conic programming Nonconvex optimisation

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Alternate second order conic program reformulations for hub location under stochastic demand and congestion

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ANNALS OF OPERATIONS RESEARCH 2021年第1-2期304卷 481-527页

作者： Bhatt, Sneha Dhyani Jayaswal, Sachin Sinha, Ankur Vidyarthi, Navneet Indian Inst Management Ahmedabad Prod & Quantitat Methods Ahmadabad 380015 Gujarat India Concordia Univ John Molson Sch Business Montreal PQ H3G 1M8 Canada Concordia Univ CIRRELT Montreal PQ H3G 1M8 Canada

In this paper, we study the single allocation hub location problem with capacity selection in the presence of congestion at hubs. Accounting for congestion at hubs leads to a non-linear mixed integer program, for which we propose 18 alternate mixed integer second order conic program (MISOCP) based reformulations. Based on our computational studies, we identify the best MISOCP-based reformulation, which turns out to be 20-60 times faster than the state-of-the-art. Using the best MISOCP-based reformulation, we are able to exactly solve instances up to 50 nodes in less than half-an-hour. We also theoretically examine the dimensionality of the second order cones associated with different formulations, based on which their computational performances can be predicted. Our computational results corroborate our theoretical findings. Such insights can be helpful in the generation of efficient MISOCPs for similar classes of problems.

关键词： Hub-and-spoke network Congestion Capacity selection Stochastic demand Single allocation second order conic programming

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Optimal robust insurance with a finite uncertainty set

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INSURANCE MATHEMATICS & ECONOMICS 2019年 87卷 67-81页

作者： Asimit, Alexandru V. Hu, Junlei Xie, Yuantao City Univ London Cass Business Sch London EC1Y 8TZ England Univ Essex Dept Math Sci Colchester CO4 3SQ Essex England Univ Int Business & Econ Sch Insurance & Econ Beijing 100029 Peoples R China

Decision-makers who usually face model/parameter risk may prefer to act prudently by identifying optimal contracts that are robust to such sources of uncertainty. In this paper, we tackle this issue under a finite uncertainty set that contains a number of probability models that are candidates for the "true", but unknown model. Various robust optimisation models are proposed, some of which are already known in the literature, and we show that all of them can be efficiently solved via second order conic programming (SOCP). Numerical experiments are run for various risk preference choices and it is found that for relatively large sample size, the modeler should focus on finding the best possible fit for the unknown probability model in order to achieve the most robust decision. If only small samples are available, then the modeler should consider two robust optimisation models, namely the Weighted Average Model or Weighted Worst-case Model, rather than focusing on statistical tools aiming to estimate the probability model. Amongst those two, the better choice of the robust optimisation model depends on how much interest the modeler puts on the tail risk when defining its objective function. These findings suggest that one should be very careful when robust optimal decisions are sought in the sense that the modeler should first understand the features of its objective function and the size of the available data, and then to decide whether robust optimisation or statistical inferences is the best practical approach. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Optimal reinsurance Risk measure Robust optimisation second order conic programming Uncertainty modelling

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Ring Distribution System Expansion Planning using Scenario Based Mixed Integer programming

Ring Distribution System Expansion Planning using Scenario B...

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IEEE/PES Transmission and Distribution Conference and Exposition (T and D)

作者： Trpovski, Andrej Hamacher, Thomas TUMCREATE 1 CREATE Way10-02 CREATE Tower Singapore 138602 Singapore Tech Univ Munich TUM Renewable & Sustainable Energy Syst D-85748 Garching Germany

ISBN: (纸本)9781728145471

With the necessity to meet the strict grid regulations, cope with the smart grid trends and improve customer satisfaction, DSOs are required to provide grid expansions and enhancements in a cost-effective manner. A common approach to enhance the reliability of supply is for planning engineers to consider ring configurations as an addition to the standard radial feed structure. In this paper, a scenario based mixed integer programming method for ring distribution system expansion planning is proposed. The proposed model extends to consider multiple cable size options for each line corridor and offers the flexibility to select different ring configuration layouts. The proposed model is tested on a realistic 96 bus case study of the Singaporean distribution system. The results of two different test cases with and without considering emergency grid operation are presented and analyzed.

关键词： Power System Planning Distribution System Ring Configuration Mixed Integer programming Optimization second order conic programming

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