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A SELECTIVE ASSEMBLY METHOD FOR TANDEM STRUCTURE COMPONENT OF PRODUCT BASED ON THE kuhn-munkres algorithm

JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第4期21卷 2596-2624页

作者： Xiao, Yifan Wang, Shuxia Zhang, Mingrui Li, Jianghong Northwestern Polytech Univ Sch Mech Engn Lab Cyber Phys Interact Xian 710072 Peoples R China

Achieving the high performance of products generally requires high-precision assembly. Advanced machining technologies can meet the majority of precision assembly requirements, but at the cost of increased manufacturing expenses. An alternative approach is selective assembly, which can obtain high-precision assemblies by optimizing the combination means of relatively low-precision parts. Aiming at the selective assembly problem of the tandem structure component of products, the selective assembly model of tandem structure component was built by analyzing the assembly process characteristics of tandem structure component, and a novel selective assembly method based on the kuhn-munkres (KM) algorithm was proposed. The assembly of the rolling bearing, radial fixed shaft, and locating holes in a typical arc-shaped structure seeker servo mechanism serve as the validation object of the proposed method in this paper. The results show that the proposed selective assembly method based on the kuhn-munkres (KM) algorithm has a good applicability to the tandem structure component of products, which can effectively reduce surplus parts to enhance the assembly success rate and has a higher solving efficiency than the selective assembly method based on the traditional Hungarian algorithm and evolutionary algorithms.

关键词： Selective assembly tandem structure optimal combination surplus parts kuhn-munkres algorithm

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kuhn-munkres algorithm Based Hybrid Beamforming for Wideband Millimeter Wave MIMO Systems With Dynamic Subarrays 15

Kuhn-Munkres Algorithm Based Hybrid Beamforming for Wideband...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Wang, Gengshan Yang, Zhijia Gong, Tierui Zheng, Meng Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Singapore University of Technology and Design Singapore

ISBN: (纸本)9798350324662

Dynamic subarrays (DSs) is an energy-efficient and cost-effective hybrid beamforming structure for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications. In this paper, we investigate the nonconvex hybrid beam-forming problem of maximizing the average spectral efficiency of the point-to-point wideband mmWave MIMO system with DSs, where the beam squints are also considered. To address the nonconvexity of this hybrid beamforming problem, we first exploit the singular value decomposition of each subcarrier channel and water-filling strategy to get the optimal fully-digital beamforming. We then propose to leverage the kuhn-munkres algorithm to get the hybrid beamforming from the obtained fully-digital solutions, aiming to guarantee that every radio-frequency chain is connected to at least one antenna. Besides, we analyze the computational complexity of the proposed design. Numerical results demonstrate the superiority of this work over the state of the art. © 2023 IEEE.

关键词： dynamic subarrays hybrid beamforming kuhn-munkres algorithm MIMO spectral efficiency Wideband millimeter wave

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Bubble tracking method based on kuhn-munkres algorithm for boiling two-phase flow study

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2024年 226卷

作者： Liu, Qian Wu, Yongyong Gui, Nan Yang, Xingtuan Tu, Jiyuan Jiang, Shengyao Tsinghua Univ Inst Nucl & New Energy Technol Collaborat Innovat Ctr Adv Nucl Energy Technol Key Lab Adv Reactor Engn & SafetyMinist Educ Beijing 100084 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3083 Australia

The study of bubble dynamics is important in various engineering applications as it can provide a fundamental understanding of the bubbly flow behavior. This paper presents a novel method for tracking bubble trajectories and classifying bubble behaviors, including formation, extinction, continuous movement, fragmentation, and coalescence. The method is based on the theory of perfect matching in bipartite graphs. By establishing the perfect matching between bubbles identified at adjacent time instants, judging the continuity of bubble motion, and simply calculating the number of bubbles in the neighbor of matching bubbles at adjacent time instants, the method can deal with the challenging situation where the bubble position and volume significantly change between two adjacent time instants. The method is validated in a simulated vertical pipe boiling flow, showing that the tracking accuracy is 100% and classification accuracies for continuous movement, fragmentation, and coalescence events are estimated to be larger than 90% in a wide range of time steps between two adjacent time instants.

关键词： Bubble tracking Bubble behavior classification Flow boiling kuhn-munkres algorithm Lattice Boltzmann method

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Solving the Many to Many assignment problem by improving the kuhn-munkres algorithm with backtracking

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THEORETICAL COMPUTER SCIENCE 2016年 618卷 30-41页

作者： Zhu, Haibin Liu, Dongning Zhang, Siqin Zhu, Yu Teng, Luyao Teng, Shaohua Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Guangdong Peoples R China Nipissing Univ Dept Comp Sci & Math North Bay ON Canada Univ Waterloo Fac Math Waterloo ON N2L 3G1 Canada Victoria Univ Coll Engn & Sci Melbourne Vic 8001 Australia

The Many to Many (M-M) assignment problem is an important open problem where one task is assigned to many, but different, agents and one agent may undertake many, but different, tasks. The kuhn-munkres (K-M) algorithm is a famous and traditional process used in dealing with assignment problems. In this paper, we propose a solution to the M-M assignment problem by improving the K-M algorithm with backtracking (KMB). To demonstrate the solution's suitability, we prove that the proposed KMB algorithm is valid and that the worst time complexity of the KMB algorithm is O ((Sigma L-a[i])(3)), where L-a[i] denotes the maximum number of tasks that can be assigned to agent i. After that, we discuss several critical problems related to the algorithm and provide the necessary and sufficient conditions of solving the M-M assignment problem. Finally, we demonstrate, through experimentation, the validity, practicality and efficiency of the KMB algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Assignment problem Many to Many assignment kuhn-munkres algorithm Hungarian method

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QoE-Aware Resource Allocation for Mixed Traffics in Heterogeneous Networks Based on kuhn-munkres algorithm

QoE-Aware Resource Allocation for Mixed Traffics in Heteroge...

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IEEE International Conference on Communication Systems (ICCS)

作者： Wang, Niwei Fei, Zesong Kuang, Jingming Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

ISBN: (纸本)9781509034239

This paper studies the resource allocation for the downlink transmission in a heterogeneous network with mixed traffics. From the users' side of view, the quality of experience (QoE) is a subjective measurement, based on which the network utility function is defined and taken as a valuation of the system performance. Because the macrocell and picocell are overlaid, the pico base station (PBS) compensates the performance loss of the macrocell by paying for the assigned subcarriers. For the subcarrier allocation, the weighted bipartite graph (WBG) is constructed and a revised kuhn-munkres (KM) algorithm is exploited to find the perfect matching. For the power allocation, the first-order derivative of the network utility function is discussed and the optimal power solution is achieved. Simulation results demonstrate that the proposed algorithm always outperforms the average power allocation algorithm and the proportional fairness (PF) algorithm. In addition, this advantage becomes more obvious with the increasing of the cell radius. Moreover, as the cell radius increases, the normalized QoE performance becomes worse.

关键词： QoE utility function mixed traffics resource allocation kuhn-munkres algorithm

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The kuhn-munkres algorithm for efficient vertical takeoff of UAV swarms 93

The Kuhn-Munkres algorithm for efficient vertical takeoff of...

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IEEE 93rd Vehicular Technology Conference (VTC-Spring)

作者： Hernandez, Daniel Cecilia, Jose M. Calafate, Carlos T. Cano, Juan-Carlos Manzoni, Pietro Univ Politecn Valencia UPV Dept Comp Engn DISCA Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9781728189642

The field of Unmanned Aerial Vehicles (UAVs) is gaining momentum thanks to the amazing capabilities of these flying devices. In particular, small aircrafts using vertical takeoff and landing (VTOL) are among the preferred solutions in the civilian sector thanks to their low cost, simplicity of operation, and the ability to carry powerful sensing devices. When combined to create a swarm, the potential of such UAVs is further extended by allowing to perform more complex missions efficiently. However, as the number of UAVs involved becomes higher, many issues arise that can result into mission failures. In this paper, we specifically address the swarm takeoff problem from an optimization perspective. We propose a new takeoff scheme based on the munkres algorithm that solves the assignment problem in polynomial time. Our evaluation studies the taking off complexity of large swarms and analyze the computational and quality trade-off of our proposal. Experiments show that the munkres algorithm offers optimal solution with a low computation overhead.

关键词： UAV Assignment problem kuhn-munkres algorithm swarm safe takeoff optimization heuristic

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Solving large-scale assignment problems by kuhn-munkres algorithm

Solving large-scale assignment problems by Kuhn-Munkres algo...

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Hong Cui Jingjing Zhang Chunfeng Cui Qinyu Chen Fiberhome Telecommunication Technologies Co. Ltd College of Computer National University of Defense Technology Chinese Academy of Sciences ***

ISBN: (纸本)9781510823808

kuhn-munkres algorithm is one of the most popular polynomial time algorithms for solving classical assignment *** assignment problem is to find an assignment of the jobs to the workers that has minimum cost,given a cost matrix X ∈ R,where the element in the i-th row and j-th column represents the cost of assigning the i-th job to the j-th *** time complexity of kuhn-munkres algorithm is O（mn）,which brings prohibitive computational burden on large scale matrices,limiting the further usage of these methods in real *** by this observation,a series of acceleration skills and parallel techniques have been studied on special *** this paper,we improve the original kuhn-munkres algorithm by utilizing the sparsity structure of the cost matrix,and propose two algorithms,sparsity based KM（sKM） and parallel KM（p KM）.Furthermore,numerical experiments are given to show the efficiency of our *** empirically evaluate the proposed algorithm sKM) and（pKM） on random generated largescale *** have shown that s KM) greatly improves the computational *** the same time,（pKM） provides a parallel way to solve assignment problem with considerable accuracy loss.

关键词： Assignment problem kuhn-munkres algorithm sparse-KM parallel-KM

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Evolutionary Many-Objective Optimization Based on kuhn-munkres' algorithm 8

Evolutionary Many-Objective Optimization Based on Kuhn-Munkr...

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8th International Conference on Evolutionary Multi-Criterion Optimization (EMO)

作者： Molinet Berenguer, Jose A. Coello Coello, Carlos A. CINVESTAV IPN Dept Comp Sci Mexico City 07300 DF Mexico

ISBN: (纸本)9783319158921;9783319158914

In this paper, we propose a new multi-objective evolutionary algorithm (MOEA), which transforms a multi-objective optimization problem into a linear assignment problem using a set of weight vectors uniformly scattered. Our approach adopts uniform design to obtain the set of weights and kuhn-munkres' (Hungarian) algorithm to solve the assignment problem. Differential evolution is used as our search engine, giving rise to the so-called Hungarian Differential Evolution algorithm (HDE). Our proposed approach is compared with respect to a MOEA based on decomposition (MOEA/D) and with respect to an indicator-based MOEA (the S metric selection Evolutionary Multi-Objective algorithm, SMS-EMOA) using several test problems (taken from the specialized literature) having from two to ten objective functions. Our preliminary experimental results indicate that our proposed HDE outperforms MOEA/D and is competitive with respect to SMS-EMOA, but at a significantly lower computational cost.

关键词： Many-objective optimization Multi-Objective Evolutioanry algorithms kuhn-munkres algorithm

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KFTO: kuhn-munkres based fair task offloading in fog networks

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COMPUTER NETWORKS 2021年 195卷 108131-108131页

作者： Yao, Yingbiao Qin, Yuancheng Feng, Wei Li, Pei Xu, Xiaorong Xu, Xin Liang, Xuesong Hangzhou Dianzi Univ Coll Commun Engn Hangzhou 310018 Peoples R China

In the fog network with multiple terminals and fog nodes, how to make a tradeoff between energy consumption fairness among fog nodes (FNs) and task processing delay of terminal nodes (TNs) is still a challenging problem. To solve this problem, this paper proposes a kuhn-munkres (KM) based Fair Task Offloading (KFTO) scheme, which includes two optimization models: FN selection model and task offloading decision model. FN selection model employs the KM algorithm to obtain the optimal matching scheme between TNs and FNs that maximizes the total network potential. The second model is used to obtain the task size offloaded to FN that minimizes the task processing delay of TN while satisfying the energy consumption constraint. Numerical simulation results reveal that the proposed KFTO scheme achieves a satisfactory tradeoff between the energy fairness among FNs and the task processing delay of TNs.

关键词： Fog network task offloading kuhn-munkres algorithm delay energy fairness

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Constrained maximum weighted bipartite matching:a novel approach to radio broadcast scheduling

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Science China(Information Sciences) 2019年第7期62卷 156-169页

作者： Shaojiang WANG Tianyong WU Yuan YAO Dongbo BU Shaowei CAI State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences

Given a set of radio broadcast programs, the radio broadcast scheduling problem is to allocate a set of devices to transmit the programs to achieve the optimal sound quality. In this article, we propose a complete algorithm to solve the problem, which is based on a branch-and-bound(BnB) algorithm. We formulate the problem with a new model, called constrained maximum weighted bipartite matching(CMBM),i.e., the maximum matching problem on a weighted bipartite graph with constraints. For the reduced matching problem, we propose a novel BnB algorithm by introducing three new strategies, including the highest quality first, the least conflict first and the more edge first. We also establish an upper bound estimating function for pruning the search space of the algorithm. The experimental results show that our new algorithm can quickly find the optimal solution for the radio broadcast scheduling problem at small scales, and has higher scalability for the problems at large scales than the existing complete algorithm.

关键词： radio broadcast scheduling branch-and-bound algorithm constrained maximum weighted bipartite matching kuhn-munkres algorithm strategy combinations

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