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A two-stage algorithm for School Bus Stop Location and Routing Problem With Walking Accessibility and Mixed Load

IEEE ACCESS 2019年 7卷 119519-119540页

作者： Ren, Jingxuan Jin, Wenzhou Wu, Weitiao South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510641 Guangdong Peoples R China

This paper proposes a School Bus Stop location and Routing Problem with Walking Accessibility and Mixed Load (SBSLRP-WA-ML), where the individual difference of walking accessibilities among students and the possibility of serving students attending different schools with the same bus simultaneously are considered. We first develop a mixed integer programming model for SBSLRP-WA-ML with the objective of minimizing the total commuting time, including walking time from the residence to school, in-vehicle travel time, and service time at stops. A two-stage solution method is then developed. In stage 1, an iterative clustering method based on k-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to locate bus stops aiming at minimizing the number of stops subject to various walking accessibilities. In stage 2, an improved ant colony optimization algorithm (IACO) integrating two local search operators is devised, which is used to generate bus routes with minimal total commuting time. A number of instances of different sizes are generated to verify the solution approach, and the influential factors with respect to total commuting time are analyzed. The model is also compared to the door-to-door school bus services. Comparison to similar methods and sensitivity analysis of parameters are also conducted to analyze the performance and robustness of the proposed approach.

关键词： Bus routing public transportation school bus stop location two-stage algorithm

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A Robust and Efficient two-stage algorithm for Power Flow Calculation of Large-Scale Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2019年第6期34卷 5012-5022页

作者： Tang, Kunjie Dong, Shufeng Shen, Jie Zhu, Chengzhi Song, Yonghua Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China State Grid Zhejiang Elect Power Corp Hangzhou 310027 Zhejiang Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China Univ Macau Dept Elect & Comp Engn Macau 999078 Peoples R China

To satisfy the robust and real-time requirements of power flow calculation for large-scale power systems, a globally convergent method is proposed with trust-region techniques, which shows satisfying robustness and convergence. Then, this method is combined with Newton's method to achieve a two-stage algorithm, benefiting from their different advantages. In the first stage, the proposed globally convergent method is used for searching power flow solution. When the values of state variables in a certain iteration are close enough to the real operational point, the algorithm enters the second stage to use Newton's method to achieve the solution. This two-stage algorithm can achieve an accurate solution for solvable cases, and can also achieve a least-square solution, which is an approximate solution for unsolvable cases. Numerical experiments show that the proposed globally convergent method and two-stage algorithm have better robustness and efficiency than the existing methods in previous research. They have universality for well- and ill-conditioned systems as well as the cases under ill-conditioned operational modes, heavy loads and inappropriate initial values. They can also handle different limit violations, such as reactive power limit in PV buses and voltage limit with tap changers action, which could significantly benefit real practice.

关键词： Ill-conditioned large-scale power flow calculation robustness trust-region two-stage algorithm

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A lexicographic-based two-stage algorithm for vehicle routing problem with simultaneous pickup-delivery and time window

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 95卷 103901-103901页

作者： Shi, Yong Zhou, Yanjie Boudouh, Toufik Grunder, Olivier Univ Paris Saclay INRAE UMR SADAPT 16 Rue Claude Bertand F-75005 Paris France Univ Bourgogne Franche Comte Nanomed Lab UTBM Rue Mieg F-90010 Belfort France Pusan Natl Univ Dept Ind Engn Busan 46241 South Korea Univ Bourgogne Franche Comte UTBM CNRS ICB UMR 6303 Rue Leupe F-90400 Sevenans France

Vehicle routing problem with simultaneous pickup-delivery and time window (VRPSPDTW) is computationally challenging as it generalizes the classical and NP-hard vehicle routing problem. According to the state-of-the-art, VRPSPDTW usually has two hierarchical optimization objectives: a primary objective of minimizing the number of vehicles (NV) and a secondary objective of reducing the transportation distance (TD). Given the existing research and our trial results, we find that the optimization of TD is not necessarily a promotion for reducing NV. In this paper, an effective learning-based two-stage algorithm, which has never been studied before, is proposed to solve the VRPSPDTW. In the first stage, a modified variable neighborhood search with a learning-based objective function is proposed to minimize the primary objective with retaining the potential structures. In the second stage, a bi-structure based tabu search (BSTS) is designed to optimize the primary and secondary objectives further. The experimental results on 93 benchmark instances demonstrate that the proposed algorithm performs remarkably well both in terms of computational efficiency and solution quality. In particular, the proposed two-stage algorithm improve several best known solutions (either a better NV or a better TD when NV are the same) from the state-of-the-art. To our knowledge, this is the first learning-based two-stage algorithm for solving VRPSPDTW reaching such a performance. Finally, we empirically analyze several critical components of the algorithm to highlight their impacts on the performance of the proposed algorithm.

关键词： Simultaneous pickup-delivery problem Time window Tabu search Variable neighborhood search Vehicle routing problem two-stage algorithm Learning-based approach

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Convergence of a two-stage Proximal algorithm for the Equilibrium Problem in Hadamard Spaces

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CYBERNETICS AND SYSTEMS ANALYSIS 2020年第5期56卷 784-792页

作者： Vedel, Ya. I. Sandrakov, G. V. Semenov, V. V. Chabak, L. M. Taras Shevchenko Natl Univ Kyiv Kiev Ukraine State Univ Infrastruct Technol Kiev Ukraine

An iterative two-stage proximal algorithm for approximate solution of equilibrium problems in Hadamard spaces is considered. This algorithm is an analog of the already studied two-stage algorithm for equilibrium problems in a Hilbert space. For Lipschitz-type pseudo-monotone bifunctions, a theorem on the weak convergence of sequences generated by the algorithm is proved.

关键词： Hadamard space equilibrium problem pseudo-monotonicity two-stage algorithm convergence

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two-stage Fault Classification algorithm for Real Fault Data in Transmission Lines

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IEEE ACCESS 2024年 12卷 121156-121168页

作者： Lim, Se-Heon Kim, Taegeun Lee, Kyeong-Yeong Song, Kyung-Min Yoon, Sung-Guk Soongsil Univ Dept Elect Engn Seoul 06978 South Korea

Fault classification in power transmission lines is important in distance relaying for identifying the accurate phases implicated in the fault occurrence. Generally, the accuracy of fault classification algorithms is evaluated by simulation data, which shows quite different characteristics from real fault data. Also, most of the previous works on fault classification used a single-stage method such as a rule-based algorithm or machine learning-based algorithm. Because of the diverse characteristics of real fault data, the performance of the single-stage method is limited. To address these issues, this paper proposes a novel two-stage algorithm that combines the strengths of rule-based and machine-learning algorithms to improve the accuracy of real fault data. A case study using real fault data shows that the proposed two-stage algorithm outperforms other conventional single-stage algorithms.

关键词： Classification algorithms Artificial neural networks Accuracy Random forests Machine learning algorithms Reviews Prediction algorithms Root mean square Power transmission lines two-stage algorithm rule-based artificial neural network root mean square

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A two-stage algorithm to Estimate the Fundamental Frequency of Asynchronously Sampled Signals in Power Systems

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ENERGIES 2015年第9期8卷 9282-9295页

作者： Moon, Joon-Hyuck Kang, Sang-Hee Ryu, Dong-Hun Chang, Jae-Lim Nam, Soon-Ryul Myongji Univ Dept Elect Engn Yongin 449728 South Korea Korea Testing Lab Ind Phys Instrument Ctr Ansan 426901 South Korea

A two-stage algorithm is proposed for the estimation of the fundamental frequency of asynchronously sampled signals in power systems. In the first stage, time-domain interpolation reconstructs the power system signal at a new sampling time and the reconstructed signal passes through a tuned sine filter to eliminate harmonics. In the second stage, the fundamental frequency is estimated using a modified curve fitting, which is robust to noise. The evaluation results confirm the efficiency and validity of the two-stage algorithm for accurate estimation of the fundamental frequency even for asynchronously sampled signals contaminated with noise, harmonics, and an inter-harmonic component.

关键词： fundamental frequency estimation two-stage algorithm time-domain interpolation tuned sine filter modified curve fitting

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A two-stage Time-Optimal Trajectory Planning algorithm with No Inverse Kinematics for Manipulator 43

A Two-Stage Time-Optimal Trajectory Planning Algorithm with ...

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43rd Chinese Control Conference (CCC)

作者： Fan, Wensong Cai, Tao Lu, Kebing Yang, Panpan Qing, Haimei Beijing Inst Technol Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

This paper studies the time-optimal trajectory planning for manipulator. Conventional time-optimal trajectory planning utilizes the inverse solution, interpolation and intelligent optimization algorithms to search for the shortest time, making the process rather complex. To address this issue, we incorporate the kinematic equations and state transition equations into the existing two-stage algorithm used in unmanned aerial vehicles (UAVs), resulting in significantly improved computational speed and accuracy of the robotic arm's operation. The first stage predefines some resulting points related to path points and optimizes the robotic arm's kinematics and motion constraints as soft constraints to provide an initial solution for the second stage. In the second stage, based on the predetermined points of each trajectory segment provided by the first stage, the optimization problem is solved without the need for kinematic inverse solutions, in which the time intervals of each trajectory segment serve as optimization variables and the robotic arm's kinematics and motion constraints are used as hard constraints for optimization. Subsequently, the obtained joint points are interpolated using fifth-degree polynomials to form continuous joint trajectories. Comparative experiments with an improved particle swarm optimization algorithm verify that the proposed method exhibits higher optimization efficiency and achieves shorter optimized times.

关键词： Manipulator Time-Optimal Trajectory Planning two-stage algorithm Kinematics

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two-stage Vehicle Routing Optimization for Logistics Distribution Based on HSA-HGBS algorithm

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IEEE ACCESS 2022年 10卷 99646-99660页

作者： Sun, Qi Zhang, Haifei Dang, Jianwu Lanzhou Jiaotong Univ Key Lab Railway Ind BIM Engn & Intelligent Elect Lanzhou 730070 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou 730070 Peoples R China

Aiming at the problems of complex urban road network, low efficiency of logistics distribution, and the difficulty of large-scale logistics distribution area division and routing planning, this paper proposes a two-stage logistics distribution vehicle routing optimization (VRP) method based on the establishment of a multi-factor complex road network constrained logistics distribution mathematical model. Considering the complex traffic elements and road network topological structure in logistics and distribution, in the first stage, a heuristic simulated annealing (HSA) distribution region partitioning algorithm is proposed with the objective of balancing vehicle task load to divide the urban logistics distribution network under complex road networks, so as to reduce the region scale and path search cost. In the second stage of route decision making, aiming at minimizing the total cost of logistics distribution, combining the VRP problem with complex road network conditions, a heuristic path search method combined with complex road network model constraints is proposed. In this stage, a hybrid genetic beam search(HGBS) algorithm is used to plan the path nodes, reduce the randomness of the model in the initial search for paths by heuristic genetic algorithms, then combine with Beam Search methods to reduce the space and time used for the search, and use optimization algorithms to improve the accuracy of independent sub-region routing optimization and the rationality of overall physical distribution route selection. Finally, the proposed method is validated in this paper with two practical cases. The experimental results show that the two-stage decision-making algorithm proposed in this paper has certain advantages in partitioning schemes, minimizing total cost and iteration times. Through comparison, the optimization ability of this method for logistics distribution networks is proved.

关键词： Logistics Road traffic Optimization Heuristic algorithms Search problems Vehicle routing Partitioning algorithms Urban areas Vehicle routing optimization complex road network two-stage algorithm heuristic simulated annealing hybrid genetic beam search

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A two-stage Time-Optimal Trajectory Planning algorithm with No Inverse Kinematics for Manipulator

A Two-Stage Time-Optimal Trajectory Planning Algorithm with ...

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第43届中国控制会议

作者： Wensong Fan Tao Cai Kebing Lu Panpan Yang Haimei Qing National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology School of Automation Beijing Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper studies the time-optimal trajectory planning for manipulator. Conventional time-optimal trajectory planning utilizes the inverse solution, interpolation and intelligent optimization algorithms to search for the shortest time, making the process rather complex. To address this issue, we incorporate the kinematic equations and state transition equations into the existing two-stage algorithm used in unmanned aerial vehicles(UAVs), resulting in significantly improved computational speed and accuracy of the robotic arm's operation. The first stage predefines some resulting points related to path points and optimizes the robotic arm's kinematics and motion constraints as soft constraints to provide an initial solution for the second stage. In the second stage, based on the predetermined points of each trajectory segment provided by the first stage, the optimization problem is solved without the need for kinematic inverse solutions, in which the time intervals of each trajectory segment serve as optimization variables and the robotic arm's kinematics and motion constraints are used as hard constraints for optimization. Subsequently, the obtained joint points are interpolated using fifth-degree polynomials to form continuous joint trajectories. Comparative experiments with an improved particle swarm optimization algorithm verify that the proposed method exhibits higher optimization efficiency and achieves shorter optimized times.

关键词： Manipulator Time-Optimal Trajectory Planning two-stage algorithm Kinematics

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two-stage Multi-objective Optimization and Decision-making Method for Integrated Energy System Under Wind Generation Disturbances

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CSEE Journal of Power and Energy Systems 2024年第6期10卷 2564-2576页

作者： Bin Deng Xiaosheng Xu Mengshi Li Tianyao Ji Q.H.Wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China

Although integrated energy systems(IES)are currently modest in size,their scheduling faces strong challenges,stemming from both wind generation disturbances and the system’s complexity,including intrinsic heterogeneity and pronounced *** this reason,a two-stage algorithm called the Multi-Objective Group Search Optimizer with Pre-Exploration(MOGSOPE)is proposed to efficiently achieve the optimal solution under wind generation *** optimizer has an embedded trainable surrogate model,Deep Neural Networks(DNNs),to explore the common features of the multiscenario search space in advance,guiding the population toward a more efficient search in each ***,a multiscenario Multi-Attribute Decision Making(MADM)approach is proposed to make the final decision from all alternatives in different wind *** reflects not only the decisionmaker’s(DM)interests in other indicators of IES but also their risk preference for wind generation disturbances.A case study conducted in Barry Island shows the superior convergence and diversity of MOGSOPE in comparison to other optimization *** respect to numerical performance metrics HV,IGD,and SI,the proposed optimizer exhibits improvements of 3.1036%,4.8740%,and 4.2443%over MOGSO,and 4.2435%,6.2479%,and 52.9230%over NSGAII,***’s more,the effectiveness of the multi-scenario MADM in making final decisions under uncertainty is demonstrated,particularly in optimal scheduling of IES under wind generation disturbances.

关键词： Decision making integrated energy systems(IES) two-stage algorithm wind generation disturbances

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