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Phase balancing using mixed-integer programming

IEEE TRANSACTIONS ON POWER SYSTEMS 1998年第4期13卷 1487-1492页

作者： Zhu, J Chow, MY Zhang, F N Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA ABB Elect Syst Technol Inst Raleigh NC 27606 USA

Unbalanced feeder systems cause deteratorating power quality and increase investment and operating costs. Feeder reconfiguration and phase swapping are two popular methods to balance the systems. For an unbalanced feeder system, feeder reconfiguration is difficult to meet the phase balancing criterion due to the limited number of sectionalizing switches available. Phase swapping is another alternative and direct approach for phase balancing. Phase swapping has not received its deserved attention due to the complexity of feeder systems, the dimension of problems, and totally overlooking the impacts of phase imbalance. Phase swapping can economically and effectively balance the feeder systems to improve power quality and reduce power system total cost. Deregulation arises competition on power quality, service reliability, and electricity price. Therefore phase swapping can enhance utilities competitive capability. This paper proposes a mixed-integer programming formulation for phase swapping optimization. Single-phase loads are treated differently with three-phase loads. Nodal phase swapping and lateral phase swapping are also introduced. An example is used to illustrate the proposed method.

关键词： phase balancing feeder reconfiguration phase swapping mix-integer programming deregulation power quality electricity price nodal phase swapping lateral phase swapping

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A two-phase case-based distance approach for multiple-group classification problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2012年第1期63卷 89-97页

作者： Ma, Li-Ching Natl United Univ Dept Informat Management Miaoli 36003 Taiwan

Classification is a procedure to separate data or alternatives into two or more classes. In practice, the need to classify alternatives involving multiple criteria into distinct classes is considerable. Therefore, determining how to assist decision makers in classifying alternatives into multiple classes is an important issue in the field of multiple-criteria decision aids. This study proposes a two-phase case-based distance approach used to assist decision makers to classify alternatives into multiple groups. By incorporating the advantages of the case-based distance method, the proposed two-phase approach can classify alternatives by evaluating a set of cases selected by decision makers, reduce the number of misclassifications, improve multiple solution problems, and lessen the impact of outliers. An interactive classification procedure is also proposed to provide flexibility in such a way that decision makers can check and adjust classification results iteratively. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Multiple-group classification problems Case-based distance method ABC analysis mix-integer programming MCDA

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Robust routing for a mixed fleet of heavy-duty trucks with pickup and delivery under energy consumption uncertainty

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APPLIED ENERGY 2024年 368卷

作者： Wang, Ruiting Keyantuo, Patrick Zeng, Teng Sandoval, Jairo Vishwanath, Aashrith Borhan, Hoseinali Moura, Scott Univ Calif Berkeley Berkeley CA 94720 USA Cummins Inc Columbus IN 47201 USA

Electrification of the truck fleet has the potential to reduce the "harder-to-abate"emissions of logistics significantly, but is generally considered to be very challenging. In this study, we focus on the energy- efficient routing of a mixed fleet of conventional and electric heavy-duty trucks with pickup and delivery under energy consumption uncertainty. We propose an energy consumption model that accounts for realistic driving dynamics, road conditions, weight, and distances. Integrating this model into the routing problem, we address energy consumption uncertainty using second-order cone mixed-integer programming. A quantitative case study is then performed on the operating costs and CO2 2 emissions benefits of electrifying heavy-duty trucks, which demonstrates improved fleet performance with optimal operating results. Scenarios with different parameter settings are tested to compare different performance metrics and provide practical insights. We evaluate routing decisions to demonstrate that stochastic optimization is necessary for reliable truck routing and produces robust results that significantly reduce capacity violations in route execution.

关键词： Vehicle routing problem Heavy-duty trucks electrification mix-integer programming Second-order cone programming

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Fast Solution Method for the Large-scale Unit Commitment Problem with Long-term Storage

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Chinese Journal of Electrical Engineering 2023年第3期9卷 39-49页

作者： Bo Li Chunjie Qin Ruotao Yu Wei Dai Mengjun Shen Ziming Ma Jianxiao Wang School of Electrical Engineering Guangxi UniversityNanning 530004China State Grid Sichuan Electric Power Company Chengdu 610041China National Power Dispatching and Control Center State Grid Corporation of ChinaBeijing 100031China National Engineering Laboratory for Big Data Analysis and Applications(Peking University) Beijing 100871China

Long-term storage(LTS)can provide various services to address seasonal fluctuations in variable renewable energy by reducing energy ***,long-term unit commitment(UC)with LTS involves mixed-integer programming with large-scale coupling constraints between consecutive intervals(state-of-charge(SOC)constraint of LTS,ramping rate,and minimum up/down time constraints of thermal units),resulting in a significant computational ***,an iterative-based fast solution method is proposed to solve the long-term UC with ***,the UC with coupling constraints is split into several sub problems that can be solved in ***,the solutions of the sub problems are adjusted to obtain a feasible solution that satisfies the coupling ***,a decoupling method for long-term time-series coupling constraints is proposed to determine the global optimization of the SOC of the *** price-arbitrage model of the LTS determines the SOC boundary of the LTS for each sub ***,the sub problem with the SOC boundary of the LTS is iteratively solved *** proposed method was verified using a modified IEEE 24-bus *** results showed that the computation time of the unit combination problem can be reduced by 97.8%,with a relative error of 3.62%.

关键词： Constraint splitting long-term storage mix-integer programming unit commitment

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Optimizing limited-stop services with vehicle assignment

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2019年 129卷 228-246页

作者： Chew, Joanne S. C. Zhang, Lele Gan, Heng S. Univ Melbourne Sch Math & Stat Melbourne Vic 3010 Australia ARC Ctr Excellence Math & Stat Frontiers ACEMS Melbourne Vic Australia

This paper studies limited-stop operations for rail transit systems and presents an optimization model, which aims to minimize passengers' travel times and on-board crowdedness. The model combines the design of service itinerary for each vehicle and the allocation of vehicles of various types among multiple lines. Three methods are proposed including a mixed-integer program and two column generation algorithms. Numerical results on generic scenarios show that the column generation algorithm can provide sub-optimal solutions and is computationally efficient. In a case study on Melbourne's railway network, its solution significantly improves in the passenger travel time compared to the current practice.

关键词： Limited-stop schedule Load excess Column generation mix-integer programming Optimization

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A new capacitated vehicle routing problem with split service for minimizing fleet cost by simulated annealing

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2007年第5期344卷 406-425页

作者： Tavakkoli-Moghaddam, R. Safaei, N. Kah, M. M. O. Rabbani, M. Univ Tehran Fac Engn Dept Ind Engn Tehran Iran Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Abti Amer Univ Nigeria Sch Informat Technol & Commun Yola Adamawa State Nigeria

We address a capacitated vehicle routing problem (CVRP) in which the demand of a node can be split on several vehicles celled split services by assuming heterogeneous fixed fleet. The objective is to minimize the fleet cost and total distance traveled. The fleet cost is dependent on the number of vehicles used and the total unused capacity. In most practical cases, especially in urban transportation, several vehicles transiting on a demand point occurs. Thus, the split services can aid to minimize the number of used vehicles by maximizing the capacity utilization. This paper presents a mix-integer linear model of a CVRP with split services and heterogeneous fleet. This model is then solved by using a simulated annealing (SA) method. Our analysis suggests that the proposed model enables users to establish routes to serve all given customers using the minimum number of vehicles and maximum capacity. Our proposed method can also find very good solutions in a reasonable amount of time. To illustrate these solutions further, a number of test problems in small and large sizes are solved and computational results are reported in the paper. (c) 2006 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： vehicle routing problem split services mix-integer programming simulated annealing

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A Parallel integer Relative Robust Mean-RCVaR Model for Portfolio Optimization 6

A Parallel Integer Relative Robust Mean-RCVaR Model for Port...

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6th IEEE Int Conf on Big Data Security on Cloud (IEEE BigDataSecurity) / 6th IEEE Int Conf High Performance and Smart Computing (IEEE HPSC) / 5th IEEE Int Conf on Intelligent Data and Security (IEEE IDS)

作者： Yang, Xueying Lu, Zhonghua Qiu, Meikang Hu, Yonghong Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Texas A&M Univ Dept Comp Sci Commerce TX 75428 USA Cent Univ Finance & Econ Sch Stat & Math Beijing 100081 Peoples R China

ISBN: (纸本)9781728168739

Portfolio optimization models are widely adopted in asset management, quantitative trading, and other applications. Relative robust portfolio optimization further considers the situation that the optimization result of the absolute robust optimization model only depends on the worst case. To apply the relative robust portfolio model to inseparable assets, this paper proposes an integer relative robust optimization model based on mixed -integer programming. The experimental results show that the integer relative robust portfolio model can achieve a higher rate of return, lower relative risk, and superior balance between robustness and profitability. Furthermore, to deal with massive computing loads of the model when applied to large-scale assets and largescale historical data, a parallel version of the integer relative robust optimization model is implemented with NIPI, that can achieve excellent speedup ratio and scalability.

关键词： robust optimization parallel computing MeanRCVaR model mix-integer programming portfolio construction

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Sewer Network Layout Selection and Hydraulic Design Using a Mathematical Optimization Framework

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WATER 2020年第12期12卷

作者： Duque, Natalia Duque, Daniel Aguilar, Andres Saldarriaga, Juan Eawag Swiss Fed Inst Aquat Sci & Technol CH-8600 Dubendorf Switzerland Swiss Fed Inst Technol Inst Environm Engn CH-8093 Zurich Switzerland Univ Andes Water Distribut & Sewerage Syst Res Ctr Bogota 111711 Colombia Univ Los Andes Dept Ind Engn Ctr Optimizat & Appl Probabil Bogota 111711 Colombia Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Univ Andes Dept Civil & Environm Engn Water Distribut & Sewerage Syst Res Ctr Bogota 111711 Colombia

This paper proposes an iterative mathematical optimization framework to solve the layout and hydraulic design problems of sewer networks. The layout selection model determines the flow rate and direction per pipe using mixed-integer programming, which results in a tree-like structured network. This network layout parametrizes a second model that determines hydraulic features including the diameter and the upstream and downstream invert elevations of pipes using a shortest path algorithm. These models are embedded in an iterative scheme that refines a cost function approximation for the first model upon learning the actual design cost from the second model. The framework was successfully tested on two sewer network benchmarks from the literature and a real sewer network located in Bogota, Colombia, that is proposed as a new instance. For both benchmarks, the proposed methodology found a better solution with up to 42% cost reduction compared to the best methodologies reported in the literature. These are near-optimal solutions with respect to construction cost that satisfy all hydraulic and pipe connectivity constraints of a sewer system.

关键词： sewer network design layout selection hydraulic design mix-integer programming dynamic programming

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Sewer network design methodology for low-cost, resilient, and reliable designs

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URBAN WATER JOURNAL 2023年第7期20卷 943-952页

作者： Saldarriaga, Juan Herran, Juana Univ Andes Water Distribut & Sewerage Syst Res Ctr Dept Civil & Environm Engn Bogota Colombia Univ Andes Water Distribut & Sewerage Syst Res Ctr Dept Civil & Environm Engn Bogota Colombia

When designing a sewer system, the goal is to find the design with the lowest cost while complying with the hydraulic constraints. However, with the emergence of new concerns like climate change and high-density urbanization, those designs must also be reliable and resilient. This is the objective of the present research, to provide an approach for finding low-cost, sewer networks design while considering the reliability and the resilience as a criterion in the selection of the best design. For that purpose, a method is proposed for evaluating resilience and reliability in a variety of designs produced by modifying the objective function of the network's layout selection model. The methodology was tested in four scenarios using two cost equations and two sewer network benchmarks from the literature. In most of the scenarios, it was possible to find designs with lower cost and higher resilience and reliability than those previously published.

关键词： sewer network design resilience reliability mix-integer programming dynamic programming SWMM

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A Parallel integer Relative Robust Mean-RCVaR Model for Portfolio optimization

A Parallel Integer Relative Robust Mean-RCVaR Model for Port...

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： Xueying Yang Zhonghua Lu Meikang Qiu Yonghong Hu Computer Network Information Center Chinese Academy of Sciences Beijing China Dept of Computer Science Texas A&M University Commerce Commerce TX USA School of Statistics and Mathematics Central University of Finance and Economics Beijing China

ISBN: (数字)9781728168739

ISBN: (纸本)9781728168746

Portfolio optimization models are widely adopted in asset management, quantitative trading, and other applications. Relative robust portfolio optimization further considers the situation that the optimization result of the absolute robust optimization model only depends on the worst case. To apply the relative robust portfolio model to inseparable assets, this paper proposes an integer relative robust optimization model based on mixed-integer programming. The experimental results show that the integer relative robust portfolio model can achieve a higher rate of return, lower relative risk, and superior balance between robustness and profitability. Furthermore, to deal with massive computing loads of the model when applied to large-scale assets and largescale historical data, a parallel version of the integer relative robust optimization model is implemented with MPI, that can achieve excellent speedup ratio and scalability.

关键词： robust optimization parallel computing Mean-RCVaR model mix-integer programming portfolio construction PARALLEL PROCESSING (COMPUTERS) Outcomes of Education integer Robustness Family Members

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