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IEEE Global Communications Conference

作者： Yuzhou Li Min Sheng Xijun Wang Yan Shi Yan Zhang Information Science Institute State Key Laboratory of ISN Xidian University

ISBN: (纸本)9781479935130

Green communications are becoming an inevitable trend for future wireless network design, meanwhile, as a promising technique, distributed antenna systems (DAS) cater for this evolution. In this paper, we focus on the problem of devising globally optimal antenna selection and power allocation algorithm in downlink DAS to achieve energy efficiency (EE) maximization. We formulate it as a mixed-integer nonlinear programming (MINLP), which maximizes EE subject to rate requirements, transmit power, and antenna selection constraints. By equivalent transformation, an iterative antenna selection and power allocation algorithm is proposed based on nonlinear fractional programming theory, and branch and bound methods. Our algorithm ensures global optimality and thus, it provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. Simulation results show that the computation complexity can be dramatically reduced comparing with exhaustive search, as well as demonstrate that a significant gain can be obtained in terms of EE against the schemes without antenna selection.

关键词： antenna arrays energy conservation integer programming iterative methods nonlinear programming tree searching DAS MINLP antenna selection constraints branch and bound methods computation complexity downlink distributed antenna systems energy efficiency maximization future wireless network design globally optimal antenna selection green communications iterative antenna selection mixed-integer nonlinear programming nonlinear fractional programming theory power allocation algorithm transmit power Antennas Optimized production technology programming Resource management Upper bound Wireless communication

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Exploring the convex transformations for solving non-convex bilinear integer problems

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COMPUTERS & CHEMICAL ENGINEERING 1999年 23卷 S471-S474页

作者： Harjunkoski, I Pörn, R Westerlund, T Abo Akad Univ Dept Chem Engn Proc Design Lab FIN-20520 Turku Finland Abo Akad Univ Dept Math FIN-20520 Turku Finland

In the present paper some methods to transform a non-convex bilinear mixed-integer programming problem are considered in more detail. The methods have been applied in solving the trim-loss problem with MINLP methods and even if the transformations lead to a global optimal solution, there are some variable properties that affect the solution performance. The properties are explored by systematically solving a set of example problems with modified transformation techniques and analyzing the results.

关键词： mixed-integer nonlinear programming convex transformation trim-loss problem

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Solving reliability redundancy allocation problems with orthogonal simplified swarm optimization

Solving reliability redundancy allocation problems with orth...

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International Joint Conference on Neural Networks

作者： Wei-Chang Yeh Vera Yuk Ying Chung Yun-Zhi Jiang Xiangjian He Department of Industrial Engineering and Engineering Managemen National Tsing Hua University Hsinchu 300 Taiwan School of Information Technology University of Sydney NSW 2006 Australia School of Software Jiangxi Agricultural University Nanchang China 330045 School of Software Engineering South China University of Technology Guangzhou China 510006 Computer Vision and Recognition Laboratory Research Centre for Innovation in IT Services and Applications (iNEXT) University of Technology Sydney (UTS) PO Box 123 Broadway NSW 2007 Australia

ISBN: (纸本)9781479919611

This study applies a penalty guided strategy and the orthogonal array test (OA) based on the Simplified Swarm Optimization algorithm (SSO) to solve the reliability redundancy allocation problems (RRAP) in the series system, the series-parallel system, the complex (bridge) system, and the overspeed protection of gas turbine system. For several decades, the RRAP has been one of the most well known techniques. The maximization of system reliability, the number of redundant components, and the reliability of corresponding components in each subsystem have to be decided simultaneously with nonlinear constraints, acting as one difficulty for the use of the RRAP. In other words, the objective function of the RRAP is the mixed-integer programming problem with the nonlinear constraints. The RRAP is of the class of NP-hard. Hence, in this paper, the SSO algorithm is proposed to solve the RRAP and improve computation efficiency for these NP-hard problems. There are four RRAP problems used to illustrate the applicability and the effectiveness of the SSO. The experimental results are compared with previously developed algorithms in literature. Moreover, the maximum-possible-improvement (MPI) is used to measure the amount of improvement of the solution found by the SSO to the previous solutions. According to the results, the system reliabilities obtained by the proposed SSO for the four RRAP problems are as well as or better than the previously best-known solutions.

关键词： Reliability Redundancy allocation problem RRAP Simplified Swarm Optimization algorithm SSO mixed-integer nonlinear programming

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mixed-integer programming for a new bus-lane reservation problem

Mixed-integer programming for a new bus-lane reservation pro...

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International IEEE Conference on Intelligent Transportation Systems

作者： Peng Wu Feng Chu Ada Che Sch. of Manage. Northwestern Polytech. Univ. Xi'an China

ISBN: (纸本)9781467365970

In this paper, we investigate a new bus-lane reservation problem in transportation network, which aims to optimally decide which lanes to be reserved for the exclusive use of buses and design bus transit paths for bus lines to achieve time-efficient bus transit with stop time window constraints. However, an exclusive bus-lane may cause negative impact on non-bus vehicles running on its adjacent non-reserved lanes as it reduces their available lanes. The objective of the problem is to minimize the total negative impact caused by reserved lanes. To address this problem, we first formulate a mixed-integer non-linear program and its complexity is proved to be NP-hard. Then, the nonlinear model is equivalently transformed into an linear one. Furthermore, properties of the problem are analyzed to reduce the search space for optimal solutions and tighten the proposed linear model. Finally, it is solved using commercial optimization software CPLEX. Computational results on randomly generated instances show the effectiveness of the proposed approach.

关键词： computational complexity integer programming nonlinear programming public transport NP-hard complexity bus-lane reservation problem mixed-integer nonlinear programming transportation network Complexity theory Computational modeling Mathematical model Optimization Roads Vehicles

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Water Resources Planning and Management by Use of Generalized Benders Decomposition Method to Solve Large-scale MINLP Problems

Water Resources Planning and Management by Use of Generalize...

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2015水资源与环境国际会议

作者： André A.Keller Lille 1 University – Science and Technology

Water resources systems(WRS)models involve a large number of continuous and integer *** quality management problems also require the consideration of uncertainties related to the variability of flow streams and *** dynamics are primarily *** characteristic features suggest the use of(stochastic)mixed-integer programming models,as well the use of sensibility analysis and ***,high dimensional real-world models and combinatorial alternatives require adequate tools for large-scale optimization *** techniques consist of decomposition methods such as the generalized Benders decomposition(GBD)and the branch-and-bound enumerative *** contribution introduces the subject of modeling WRSs by use of GBD and branch-and-bound algorithms with numerical applications.

关键词： mixed-integer nonlinear programming Large-Scale Optimization Methods Generalized Benders Decomposition Algorithm Water Resource System Groundwater Management Water Quality Management

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Electric Service Restoration using Microgrids

Electric Service Restoration using Microgrids

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IEEE Power and Energy Society General Meeting

作者： Bananeh Ansari Salman Mohagheghi Electrical Engineering and Computer Science Department Colorado School of Mines

ISBN: (纸本)9781479964161

A Microgrid-assisted methodology is proposed in this paper for electric service restoration in distribution networks. Success of restoration algorithms is closely tied with the availability of sufficient capacity on the restoration path(s). When the restoration path is at or near full capacity, some capacity relief may be achieved through the usage of Microgrids. By increasing the generation level of its micro-generators, reducing the demand of its demand responsive loads, or, as the last resort, by islanding from the grid, a Microgrid can help alleviate the congestion on the restoration path, and therefore assist the service restoration algorithm. The methodology put forth in this paper considers a potential restoration circuit equipped with Microgrids, and provides the most cost-effective operational solution while achieving the targeted restoration capacity. Network operational constraints, demand variations, and fluctuations in energy pricing are all taken into account. The problem is formulated as a mixed-integer nonlinear programming problem, and is implemented on a modified version of the IEEE 123-node test distribution system.

关键词： Distribution network Electric service restoration Demand response Distributed energy resources Microgrid mixed-integer nonlinear programming

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A modified and extended genetic algorithm for optimal distributed generation grid-integration solutions in direct current power grids

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 10卷

作者： Zuluaga-Ríos, Carlos D. Institute for Research in Technology Universidad Pontificia Comillas Madrid Spain

The integration of distributed generation into direct current power grids presents a critical challenge in modern energy systems, as it directly impacts grid reliability, efficiency, and the successful transition to renewable energy. This study addresses the problem of optimizing distributed generation placement and sizing in direct current grids, a key issue for reducing power losses and improving energy distribution. To tackle this, a modified and extended genetic algorithm was developed, capable of handling both continuous and discrete variables simultaneously. The algorithm was tested on two standard direct current grid systems, a 21-bus microgrid and a 69-bus network. The results demonstrated significant improvements over existing methods, reducing power losses by 84.5% in the 21-bus microgrid and by 95% in the 69-bus direct current network, with notably reduced computation times. These findings indicate that the proposed algorithm not only optimizes distributed generation integration effectively but also offers superior performance compared to traditional approaches, without the need for additional methods or software. The novelty of this work lies in its ability to handle complex, nonlinear optimization problems within direct current grids using a single, efficient approach, advancing beyond previous efforts by achieving better results with fewer computational resources. © 2024 The Author

关键词： Direct current grids Genetic algorithms Meta-heuristic optimization methods mixed-integer nonlinear programming

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A genetic algorithm to integrate a comprehensive dynamic cellular manufacturing system with aggregate planning decisions

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2020年第2期15卷 138-154页

作者： Kia, Reza German Univ Technol GUtech Fac Business & Econ Dept Logist Tourism & Serv Management Muscat Iran Islamic Azad Univ Dept Ind Engn Firoozkooh Branch Firoozkooh Iran

In this paper, to design a Cellular Manufacturing System (CMS) under a dynamic situation and make Aggregate Planning (AP) decisions simultaneously, a mixed-integer nonlinear programming (MINLP) model is designed. The distinctive features of the comprehensive Dynamic CMS (DCMS) model under consideration are: i) an extensive coverage of significant manufacturing characteristics in designing a DCMS in addition to the main strategies of AP, ii) integration of cost elements addressing structural, operational and planning issues in the design of DCMS, and iii) capable of developing better DCMS design decisions by incorporating more detailed and realistic parameters when compared to the literature. An illustrative numerical example is solved by CPLEX 12.6 to demonstrate the achievements obtained by the integrated model. Since the proposed model belongs to NP-hard category, a Genetic Algorithm (GA) improved by an elaborately designed matrix-based chromosome representation to represent all decision variables, as well as a sequential procedure generating initial solutions is developed. Several test problems either generated randomly or taken from the literature with various sizes are solved and the results are compared with the solutions gained using CPLEX solver. The comparisons results show that the designed GA is capable of evolving optimal or near-optimal solutions with relative gap less than 1% in a computationally satisfactory manner.

关键词： Dynamic cellular manufacturing system mixed-integer nonlinear programming aggregate planning genetic algorithm

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A nonlinear model for a capacitated stochastic transportation network using multiple linear regression

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2016年第4期11卷 252-261页

作者： Shiripour, Saber Mahdavi, Iraj Mahdavi-Amiri, Nezam Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Sharif Univ Technol Fac Math Sci Tehran Iran

Here, a capacitated location-multi-allocation-routing problem with intelligent stochastic travel times is considered. In our study, the concept of intelligent stochastic travel times incurs two issues: (1) consideration of some random factors in computing the travel times and (2) impact of the traveling population on these random factors simultaneously. Here, a deterministic factor of road length and two random factors of the time spent in traffic and the number of accidents are considered. It is assumed that the time spent in traffic is distributed based on an exponential distribution function and the other factor is distributed based on a Poisson distribution function. Also, the capacities of server nodes and arcs for accepting the population are assumed to be limited. Using multiple linear regression, we formulate the problem as a mixed-integer nonlinear programming model. The objective is to find appropriate locations as server locations among the candidate locations, allocate the existing population in each demand node to server locations and determine the movement path of each member to reach its corresponding server with respect to the simultaneous change of the stochastic travel times so that the expected total transportation time is minimized. Also, to investigate the validation and behavior of the proposed probabilistic model, a network example is provided and computational results are analyzed.

关键词： Intelligent stochastic travel times multiple linear regression multi-allocation mixed-integer nonlinear programming

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Two phases method for optimization distributing in a gas pipeline

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Beijing Huagong Daxue Xuebao (Ziran Kexueban)/Journal of Beijing University of Chemical Technology (Natural Science Edition) 2007年第3期34卷 329-332页

作者： Huang, Yingqi Wei, Yufeng Hu, Yunjiao School of Sciences Beijing University of Chemical Technology Beijing 100029 China

An improved method for solving the optimal distribution problem of a gas piping system is introduced. Mathematical programming has been used to solve for the optimal distribution and the problem represented by a mathematical model. The optimization problem is a mixed-integer non-linear programming problem, and in general discrete non-linear programming problems are difficult to solve. In this paper, several continuous variables are introduced to replace the discrete decision variables, most importantly the pipe segment length, and in this way the original problem becomes a nonlinear programming problem. The original nonlinear programming problem is decomposed into two optimization subsystems, phase 1 and phase 2. The interconnection between phase 1 and phase 2 involves the gas flow rate and the segment length. The output variables of phase 1 are the input variables for phase 2, and the output variables of phase 2 are also the input variables for Phase 1.

关键词： Discrete decision variable mixed-integer nonlinear programming Segment length

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