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Collinear groupwise feature selection via discrete fusion group regression

PATTERN RECOGNITION 2018年 83卷 1-13页

作者： Kim, Younghoon Kim, Seoung Bum Korea Univ Sch Ind Management Engn 145 Anam Dong Seoul 02841 South Korea

We propose a method to select the subset of features in multiple linear regression models that considers the collinearity between features. The proposed method first detects collinear groups of features and then uses collinear groupwise feature selection constraints to estimate the coefficients of the regression model. The constraints simultaneously control the number of features selected and predefined collinear feature groups. We manage the multicollinearity in the regression model by controlling the parameters of the fusion group constraint. To address the NP-hard problem of the proposed method, we propose a modified discrete first-order algorithm. We use simulation and real-world data to demonstrate the usefulness of the proposed method by comparing it to existing regularization and discrete optimization-based methods in terms of predictive accuracy, bias, and variance. The comparison confirms that the proposed method outperforms the alternatives. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Multiple linear regression Machine learning Feature selection Multicollinearity mixed-integer quadratic programming Best subset selection

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A Polynomial-Time Algorithm with Tight Error Bounds for Single-Period Unit Commitment Problem

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Journal of the Operations Research Society of China 2023年第1期11卷 1-28页

作者： Ruo-Tian Gao Shu-Cherng Fang Cheng Lu Wen-Xun Xing Big Data Center of State Grid Corporation of China Beijing 100053China Department of Industrial and System Engineering North Carolina State UniversityRaleigh 27695-7906USA School of Economics and Management North China Electric Power UniversityBeijing 102206China Department of Mathematical Sciences Tsinghua UniversityBeijing 100084China

This paper proposes a Lagrangian dual-based polynomial-time approximation algorithm for solving the single-period unit commitment problem,which can be formulated as a mixed-integer quadratic programming problem and proven to be *** theoretical bounds for the absolute errors and relative errors of the approximate solutions generated by the proposed algorithm are *** results support the effectiveness and efficiency of the proposed algorithm for solving large-scale problems.

关键词： Nonlinear programming Lagrangian dual Unit commitment problem mixed-integer quadratic programming Convex relaxation

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Disassembly line optimization with reinforcement learning

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2024年第4期32卷 1115-1142页

作者： Kegyes, Tamas Sule, Zoltan Abonyi, Janos Univ Pannonia HUN REN PE Complex Syst Monitoring Res Grp Egyet Utca 10 H-8200 Veszprem Hungary Univ Pannonia Dept Comp Sci & Syst Technol Egyet Utca 10 H-8200 Veszprem Hungary

As the environmental aspects become increasingly important, the disassembly problems have become the researcher's focus. Multiple criteria do not enable finding a general optimization method for the topic, but some heuristics and classical formulations provide effective solutions. By highlighting that disassembly problems are not the straight inverses of assembly problems and the conditions are not standard, disassembly optimization solutions require human control and supervision. Considering that Reinforcement learning (RL) methods can successfully solve complex optimization problems, we developed an RL-based solution for a fully formalized disassembly problem. There were known successful implementations of RL-based optimizers. But we integrated a novel heuristic to target a dynamically pre-filtered action space for the RL agent (dlOptRL algorithm) and hence significantly raise the efficiency of the learning path. Our algorithm belongs to the Heuristically Accelerated Reinforcement Learning (HARL) method class. We demonstrated its applicability in two use cases, but our approach can also be easily adapted for other problem types. Our article gives a detailed overview of disassembly problems and their formulation, the general RL framework and especially Q-learning techniques, and a perfect example of extending RL learning with a built-in heuristic.

关键词： Disassembly line optimization Reinforcement learning Q-learning mixed-integer quadratic programming Heuristically accelerated reinforcement learning dlOptRL algorithm

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Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI

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IEEE ACCESS 2024年 12卷 15419-15437页

作者： Zhao, Meihui Yu, Hanxiao Pan, Jianxiong Jin, Yifeng Lv, Guocheng Jin, Ye Peking Univ Sch Elect Beijing 100871 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China China Acad Space Technol Inst Telecommun & Nav Satellites Beijing 100094 Peoples R China

The densification of low-Earth orbit (LEO) constellations and the development of inter-satellite links enable the cooperative transmission of multiple satellites. This paper investigates a decentralized resource allocation strategy for multi-satellite cooperation networks under statistical channel state information (CSI). Our formulation aims at minimizing the total matching error between non-uniform traffic requests and achievable throughput by jointly optimizing transmit power and beam illumination pattern. The original problem is a stochastic mixed-integer quadratic programming with the long-term accumulation nature, whose stochastic objective is first addressed by incorporating the outage probability constraint. Then we decompose the deterministic problem into a series of single-timeslot subproblems on the greedy basis, each of which is further decomposed for each satellite following the decentralized decision principle with a limited number of inter-satellite interactions required to obtain the overall resource allocation. Finally, the optimization problem at each satellite is solved by sequential quadratic programming (SQP)-based alternating optimization algorithm that iterates between the transmit power allocation and beam illumination design. Numerical results show the effectiveness of the proposed decentralized algorithm in reducing the total matching error by at least 27% over other baseline approaches.

关键词： Multi-satellite cooperation network statistical CSI mixed-integer quadratic programming stochastic objective decentralized resource allocation

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Integrated Energy Management Scheme Considering Transportation Flexibility for Highway Service Zones

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第1期60卷 1357-1368页

作者： Ding, Zhaohao Zhang, Yu Yang, Dechang Dai, Liang Wang, Dawei Xu, Hongke Zhao, Long Dehghanian, Payman North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China South Dakota Sch Mines & Technol Rapid City SD 57702 USA George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA

The highway transportation system exhibits immense potential for adaptive functioning due to its distinctive operational attributes. This capability permits to harness more renewable energy, leading to a reduction in energy consumption costs. In this article, an integrative energy system optimization model is introduced from the standpoint of highway service zone operators. To simulate the behavior of electric vehicles on highways, an expanded network flow model is formulated considering driving, queueing, and charging actions. This model comprehensively integrates dimensions of time, space, and the state of charge of the vehicles. Consequently, the proposed model is proficient in representing the spatiotemporal and flexible operational traits inherent to electric vehicles. Through a series of numerical case studies executed on a real-world highway system, the effectiveness of the proposed model is demonstrated. These findings substantiate the potential utility and efficacy of our model in enhancing the operation optimization of energy systems within highway transportation systems.

关键词： Distributed renewable resource electric vehicles highway system mixed-integer quadratic programming network flow model

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Optimal household appliances scheduling for smart energy management considering inclining block rate tariff and net-metering system

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 190卷

作者： Shaban, Ahmed Salhen, Mohamed Shalaby, Mohamed A. Abdelmaguid, Tamer F. Sultan Qaboos Univ Coll Engn Dept Mech & Ind Engn Alkhoud Muscat 123 Oman Fayoum Univ Fac Engn Mech Engn Dept Al Fayyum 63514 Egypt Cairo Univ Fac Engn Dept Mech Design & Prod Giza 12613 Egypt Sultan Qaboos Univ Coll Engn Dept Mech Ind Engn POB 33Alkhoud Muscat 123 Oman

Smart grids that integrate household renewable energy sources and share information with households can help create and maintain a smarter data-driven environment. Within this environment, flexible home energy management policies that minimize household energy costs can be adopted. This paper considers a smart home with a renewable energy source that favors satisfying its energy needs at minimum cost. This is achievable by smartly scheduling the use of its domestic appliances to match a given energy grid tariff. Focusing on the case of Egypt in which an inclining block rate (IBR) tariff is imposed, this paper fills a gap in the literature regarding the load scheduling models aiming to minimize energy cost at the household level whenever such a tariff exists. A new mixed integer quadratic programming (MIQP) model is formulated for this scheduling problem, considering the adopted net metering system with installed domestic photovoltaic (PV) systems in Egypt. The model generates the optimal household load schedule and the optimal amounts of energy to exchange with the grid while considering all the system and consumer utility constraints. To assess the applicability of the proposed model, a survey is conducted to identify the diversity and characteristics of using the electrical appliances by the Egyptian households. Based on the collected survey results, the effectiveness of the proposed MIQP model is investigated. Results confirm the effectiveness of the proposed model to minimize energy cost for different categories of the Egyptian households.

关键词： Smart Energy management Household appliances scheduling mixed-integer quadratic programming Inclining block rate tariff Net-metering system Egypt

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Breaker Status Identification

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IEEE TRANSACTIONS ON POWER SYSTEMS 2010年第2期25卷 694-702页

作者： Caro, Eduardo Conejo, Antonio J. Abur, Ali Univ Castilla La Mancha E-13071 Ciudad Real Spain Northeastern Univ Boston MA 02115 USA

This paper presents a procedure to identify the on/off statuses of breakers at substations throughout a power network. It is intended as a structural data preprocessor prior to running a state estimator. The procedure relies on a dc model of power lines and busbars, and requires the solution of a well-behaved mixed-integer quadratic programming problem. The proposed procedure is illustrated using a simple example and a realistic case study.

关键词： Breaker status identification mixed-integer quadratic programming state estimation substations

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QUEST - A new quadratic decision model for the multi-satellite scheduling problem

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COMPUTERS & OPERATIONS RESEARCH 2020年 115卷 104822-000页

作者： Berger, J. Lo, N. Barkaoui, M. Def Res Dev Canada 2459 Pie XI Blvd North Quebec City PQ Canada WARWII Solut Inc Quebec City PQ Canada Univ Laval Dept Informat & Genie Logiciel Laval PQ Canada

Demand for earth observation satellite imagery is pervasive and increasing rapidly across multiple domains, highlighting the key problem to optimally schedule satellite image acquisition/collection, subject to ground and on-board constraints. Despite the variety of reported approaches to solving the NP-hard multi-satellite scheduling problem (m-SatSP), serious limitations still prevail, largely overlooking problem structure exploitation and/or domain knowledge. Assuming a few constraints, problem modelling-or scope-is often confined to a simple trailing satellite constellation composition perspective and tends to conveniently oversimplify footprint coverage intricacies. As a result, adequate problem task decomposition properly reflecting complex kinematic behaviour for an ad hoc satellite constellation remains elusive. Known approaches also fail to provide valuable solution optimality gap estimations to objectively qualify best computed solution and/or to control run-time execution in solving hard problem instances. In this paper, a novel approach to solving the single objective static m-SatSP is proposed. It is based on network flow optimization using mathematical programming. Unlike previous methods, QUEST (quadratically constrainEd program Solver Technology) relies on a sound alternative approximate objective function and, the exploitation of exact problem-solving techniques. Derived from domain knowledge and problem structure considerations, QUEST generalizes problem modelling to successfully handle virtual constellation, avoiding unsuitable utilization of traditional area coverage decomposition scheme. The proposed decision model concurrently captures coverage approximation, imaging success uncertainty and quality for a variety of tasks. It also includes new and optional constraints while embracing an acceptable upper bound on collection value. A QUEST variant alternatively relying on "delayed reward" to bridge promising search regions on move selection, furthe

关键词： Multi-satellite scheduling Collection mixed-integer quadratic programming

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Location choice and risk attitude of a decision maker

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2017年第PartA期66卷 170-181页

作者： Berman, Oded Sanajian, Nima Wang, Jiamin Univ Toronto Joseph L Rotman Sch Management 105 St George St Toronto ON M5S 3E6 Canada Long Isl Univ Coll Management Post Campus720 Northern Blvd Greenvale NY 11548 USA

In this paper we study the effect of a decision maker's risk attitude on the median and center problems, two well-known location problems, with uncertain demand in the mean-variance framework. We provide a mathematical programming formulation for both problems in the form of quadratic programming and develop solution procedures. In particular, we consider the vertex and absolute median problems separately, and identify a dominant set for the center problem. Glover's linearization method is applied to solve the vertex median problem. We also develop a branch and bound algorithm and a heuristic as the linearization technique takes too long for the vertex median problem on large networks. A computational experiment is conducted to compare the performance of the algorithms. We demonstrate the importance of taking into account the volatility and correlation structure when a location decision is made. The closest assignment property is also discussed for these location problems under the mean-variance objective. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Facility location mixed-integer quadratic programming quadratic assignment problem Mean-variance Risk-averse Risk-seeking

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Transmission Investments in a Multilateral Context

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IEEE TRANSACTIONS ON POWER SYSTEMS 2012年第1期27卷 475-483页

作者： Buijs, Patrik Belmans, Ronnie Katholieke Univ Leuven Dept Elect Engn ESAT Res Grp Electa B-3001 Heverlee Belgium

More and more transmission planning has to cope with a multilateral context due to increased electricity exchanges between zones. Whereas investments are decided upon and approved at the zonal level, policy goals can be formulated at a higher supranational level. The zonal level, however, can have its own objectives or way to reach the policy goals. This is possibly conflicting with the supranational or other zones' viewpoints. In this paper, different transmission investment planners are compared. Conflicting outcomes are observed. As a possible compromise, a Pareto-planner is proposed. He maximizes overall welfare while guaranteeing that all zones can at least keep their initial level of welfare. These constraints ensure that the multilateral context is explicitly taken into account. All planners are modeled as bilevel optimization problems and solved using mixed-integer quadratic programming and genetic algorithm. The models are applied on a three-node and 14-node example.

关键词： Bilevel model genetic algorithm mixed-integer quadratic programming transmission expansion planning

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