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Research on the balancing problem of human-robot collaborative assembly line in SMEs considering ergonomic risk and cost

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 204卷

作者： Yin, Xiyan Yang, Yuanquan Li, Xixing Wu, Rui Guo, Ao Zhao, Qingqing Hubei Univ Technol Sch Mech Engn Hubei Key Lab Modern Mfg & Qual Engn Wuhan 430068 Peoples R China

As the development of Industry 5.0, an increasing number of collaborative robots (cobots) are integrated into manufacturing systems to assist workers. The inappropriate allocation of tasks and resources (workers and cobots) in human-robot collaborative assembly lines will result in problems such as production efficiency decrease. Due to the lack of research on assembly line balancing problem with human-robot collaboration (ALBP-HRC) for small and medium-sized enterprises (SMEs), this study develops a novel multi-objective optimization mathematical model based on the cost-conscious characteristics of SMEs. The model aims to simultaneously minimize cycle time, ergonomic risk, and cost. To solve the model, a problem-specific improved multi-objective particle swarm optimization algorithm (IMOPSO) with a specialized initialization scheme and efficient local search method is developed, and a novel three-vector encoding and decoding scheme is proposed. Subsequently, the computational tests demonstrate that the IMOPSO algorithm outperforms the comparison algorithms in most results (accounting for 87.7%), while exhibiting superior average performance across all test cases. Finally, human-robot collaborative balancing schemes for a real electric vehicle air conditioner assembly line are designed, providing a feasible solution for the assembly line balancing problem of similar electromechanical products in SMEs.

关键词： Human-robot collaboration Assembly line balancing particle swarm algorithm Multi-objective optimization Cost Ergonomic risk

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Two-Stage Hierarchical 4D Low-Risk Trajectory Planning for Urban Air Logistics

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DRONES 2025年第4期9卷

作者： Zheng, Yuan Li, Yichao Cheng, Jie Li, Chenglong Hu, Shichen Civil Aviat Flight Univ China Sch Comp Sci Guanghan 618307 Peoples R China Civil Aviat Flight Univ China Coll Air Traff Management Guanghan 618307 Peoples R China Jinan Inst Supercomp Technol Jinan 250000 Peoples R China Shanghai Univ Int Business & Econ Sch Int Econ & Trade Shanghai 201620 Peoples R China

The rapid development of the drone industry has facilitated the emergence of concepts such as urban air mobility (UAM), driving a wave of air logistics in urban very low-level (VLL) airspace. However, existing trajectory planning algorithms do not adequately consider the ground risks and secondary conflicts arising from high-density operations in urban VLL airspace. To address these challenges, this paper proposes a two-stage hierarchical 4D trajectory planning method to minimize multiple risks. Specifically, the method consists of a risk-aware global planning module (RAGPM) for preflight trajectory planning and a non-secondary conflict local planning module (NCLPM) for in-flight conflict avoidance. Consequently, low-risk trajectory without secondary conflict can be found in complex environments with high-density operations, as illustrated by extensive experiments.

关键词： multi-UAV 4D trajectory planning ETA A* algorithm particle swarm algorithm artificial potential field method

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Reconfiguration of low-voltage distributed power sources within electric power's distribution network based on improved particle swarm-fish swarm fusibility algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-14页

作者： Xu, Xiaowei Nie, Ding Xu, Wenhua Xiang, Enxin Chen, Shan Nie, Yongjie Fu, Xiao Xu, Wan Han, Yiming Yunnan Elect Power Grid Res Inst Kunming 650217 Peoples R China Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650504 Peoples R China

With the development of distributed power sources in the distribution network, the algorithm of distribution network reconfiguration is gaining attention from experts and scholars. Its goal is to reduce the power loss during power transmission, so as to reduce the power grid loss during power transmission. And weaken the electric heating effect in the process of electric energy transmission, thus maintaining the safety of the surrounding residents. Due to the wire impedance effect, a lot of electric energy of the circuit is lost to electric heating, which is easy to cause local overheating and lead to fire. This will not only cause power loss, but also endanger the safety of surrounding residents. To address the issue, experiments on distribution grid reconstruction are performed using the enhanced particle swarm-fish swarm algorithm with the Elecgrid self-constructed dataset. Initially, low-voltage distributed power sources in parallel are connected to the circuit, thereby decreasing internal resistance and electrical heat. Then, by controlling the circuit in the system, the double separation relay adjusts the inductance and capacitance of the conductor, thus reducing the reactance length. Additionally, particle swarm particles are mutated to enable them to jump out of the local optimum, and elite fish approach is used to expand the search area. Finally, the proposed fusion algorithm is applied to the self-built data set of Elecgrid and compared with the other three algorithms. The fusion algorithm serves as the standard test system for this comparison. The active power loss of the hybrid algorithm is 63 kW at an operating voltage of 0.74 V. The loss work of the other three algorithms is 74 kW, 97 kW and 109 kW respectively. The mixed algorithm has the lowest loss among the four algorithms. The experiments are repeated for six times, and the linear fitting degrees of the four algorithms are 0.9804, 0.9527, 0.9612 and 0.9503, respectively. The experimental results s

关键词： Distributed power supply Distribution network reconfiguration particle swarm algorithm Artificial fish swarm algorithm Objective function

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Virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization

Virtual network mapping algorithm based on load balancing mu...

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11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Jun Zhao Zhong Ma Wuhan Digital Engineering Institute China

ISBN: (纸本)9781510821279

Existing virtual network mapping algorithms does not consider resource consumption of intermediate node on communication path usually. Minimum resource consumption or shortest path of physical network is regarded as objective, thereby leading to bottleneck due to insufficient resource of intermediate node on communication path, and affecting performance of the whole physical network and subsequent success rate of virtual network. A virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization is proposed in the paper aiming at the problem. Resource consumption of intermediate node is sufficiently considered in the algorithm, double balance of node load and link load is regarded as objective. Meanwhile, the optimal path of particle swarm optimization algorithm is adopted. Experiments show that the algorithm proposed in the paper can not only realize double balance of node load and link load, but also effectively improve request receiving success rate, overall resource load balance and long-term operation income.

关键词： Load Balancing particle swarm Optimization algorithm Virtual Network Mapping Load balancing intermediate node Link Load Telecommunication lines particle swarm optimization Virtual networks particle swarm algorithm Resource utilization physical network

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Thermal layout optimization of stacked chips based on hybrid algorithm of simulated annealing and particle swarm

Thermal layout optimization of stacked chips based on hybrid...

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Mingxiang Zang Meng Wang Xinquan Lai Huisen He School of Computer Science and Technology XiDian University Xian China School of Electronic Engineering XiDian University Xian China

ISBN: (纸本)9781467329637

As the density of chip package increases, the thermal layout optimization of stacked chips has become more and more important. The thermal layout optimization of stacked chips is studied in this paper. Firstly, a hybrid algorithm of simulated annealing and particle swarm (SA-PSO) is proposed for thermal layout optimization. Secondly, the fitness function is obtained based on thermal superposition model and heat conduction equation. Then the temperature of chips is chosen as the evaluation index and the corresponding optimization scheme is achieved. Finally, finite element analysis (FEA) software is used to evaluate the thermal field distribution of stacked chips and the result shows the temperatures of stacked chips are lowered after optimization. Thus, we can prove that the chip layout optimization algorithm proposed in this paper is effective and robust in thermal layout optimization of stacked chips.

关键词： Stacked chips Simulated annealing algorithm particle swarm algorithm Thermal layout optimization Finite element analysis particle swarm algorithm simulated annealing algorithm Computer chips Finite element method particle swarm optimization simulated annealing layouts Thermal chip package

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Distributed particle swarm Optimization Using an Average Consensus algorithm

Distributed Particle Swarm Optimization Using an Average Con...

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IEEE Annual Conference on Decision and Control

作者： Yuji Wakasa Sosuke Nakaya Graduate School of Science and Engineering Yamaguchi University 2-16-1 Tokiwadai Ube Yamaguchi 755-8611 Japan

ISBN: (纸本)9781479978878

In order to improve the efficiency of distributed systems over a network, various distributed optimization algorithms have been developed recently. In particular, for optimization problems with convex and differentiable functions, sophisticated algorithms have been proposed, motivated by energy network systems such as smart grid. As an algorithm with easier implementation and wider range of applications, this paper proposes a distributed optimization algorithm that can deal with optimization problems with nonconvex and nondifferentiable functions by combining a particle swarm optimization algorithm and an average consensus algorithm. Moreover, the convergence property of the proposed algorithm is proven under mild assumptions. Through numerical experiments, the effectiveness of the proposed algorithm is illustrated.

关键词： Optimization algorithms particle swarm optimization algorithms Smart grids Distribute Distributed Systems Convexity energy grid particle swarm algorithm DIFFERENTIABLE FUNCTIONS

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Optimization of electric and magnetic field intensities in proximity of power lines using genetic and particle swarm algorithms

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ARCHIVES OF ELECTRICAL ENGINEERING 2018年第4期67卷 829-843页

作者： Krol, Krzysztof Machczynski, Wojciech Poznan Univ Tech Inst Elect Engn & Elect Piotrowo 3A PL-60965 Poznan Poland

The paper presents optimization of power line geometrical parameters aimed to reduce the intensity of the electric field and magnetic field intensity under an overhead power line with the use of a genetic algorithm (AG) and particle swarm optimization (PSO). The variation of charge distribution along the conductors as well as the sag of the overhead line and induced currents in earth wires were taken into account. The conductor sag was approximated by a chain curve. The charge simulation method (CSM) and the method of images were used in the simulations of an electric field, while a magnetic field were calculated using the Biot-Savart law. Sample calculations in a three-dimensional system were made for a 220 kV single - circuit power line. A comparison of the used optimization algorithms was made.

关键词： power line electric field magnetic field optimization genetic algorithm particle swarm algorithm

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Kinematics inverse solution of assembly robot based on improved particle swarm optimization

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ROBOTICA 2024年第3期42卷 833-845页

作者： Zhang, Shixiong Li, Ang Ren, Jianxin Ren, Ruilong Henan Univ Technol Sch Mech & Elect Eng Zhengzhou Peoples R China

Inverse kinematics of robot is the basis of robot assembly, which directly determines the pose of robot. Because the traditional inverse solution algorithm is limited by the robot topology structure, singular pose and inverse solution accuracy, it affects the use of robots. In order to solve the above problems, an improved particle swarm optimization (PSO) algorithm is proposed to solve the inverse problem of robot. This algorithm initializes the particle population based on joint angle limitations, accelerating the convergence speed of the algorithm. In order to avoid falling into local optima and premature convergence, we have proposed a nonlinear weight strategy to update the speed and position of particles, enhancing the algorithm's search ability, in addition introducing a penalty function to eliminate particles exceeding joint limits. Finally, the positions of common points and singular points are selected on PUMA 560 robot and redundant robot for inverse kinematics simulation verification. The results show that, compared with other algorithms, the improved PSO algorithm has higher convergence accuracy and better convergence speed in solving the inverse solution, and the algorithm has certain universality, which provides a new solution for the inverse kinematics solution of the assembly robot.

关键词： assembly robot inverse kinematics particle swarm algorithm nonlinear weight strategy

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Optimal design of district metered areas based on improved particle swarm optimization method for water distribution systems

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WATER SUPPLY 2022年第11期22卷 7930-7944页

作者： Yu, Tingchao Zhang, Xiangqiu Long, Zhihong Zhou, Hua Liu, Xiaowei Zhejiang Univ Zhejiang Key Lab Drinking Water Safety & Distribut Hangzhou 310058 Peoples R China Zhejiang Univ Inst Municipal Engn Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Guangzhou Water Supply Co Ltd Guangzhou 510600 Peoples R China Huadong Engn Corp Ltd Hangzhou 311122 Peoples R China Zhejiang Univ Inst Marine technol & Engn Ocean Coll Hangzhou 310058 Peoples R China

Although partitioning of water distribution systems (WDSs) into district metered areas (DMAs) is challenging, it can be effectively used for refined management and leakage control. A two-step novel process for DMA partitioning is proposed in this study, i.e. clustering and dividing. The first step is to cluster nodes through improved METIS graph partitioning method. The second step is to optimize the location of flow meters and gate valves on boundary pipes by obtaining the feasible solutions. The good solutions that constitute the Pareto front was produced,which could be tough and time-consuming task. The paper proposed the innovative and efficient dividing phase: (a) selecting the important boundary pipes by hydraulic analysis;(b) using the improved particle swarm optimization algorithm;(c) proposing three-objective functions. The proposed method is applied to Modena and EXNET networks to demonstrate its feasibility.

关键词： district metered areas flowmeter gate valves particle swarm algorithm water distribution system

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On-site efficiency evaluation of three-phase induction motor based on particle swarm optimization

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ENERGY 2011年第3期36卷 1713-1720页

作者： Sakthivel, V. P. Subramanian, S. Annamalai Univ Fac Engn & Technol Dept Elect Engn Chidambaram 608002 Tamil Nadu India

On-site efficiency determination of induction motor is essential in industrial plants for saving the energy consumption. This paper presents a new application of particle swarm optimization (PSO) approach for field efficiency evaluation of induction motor based on a modified induction motor equivalent circuit. The stray-load loss is considered in the equivalent circuit by adding an equivalent resistor in series with the rotor circuit and its value is derived from the assumed stray-load loss recommended in IEEE Std. 112. The PSO approach uses the information about the stator current, stator voltage, input power, stator resistance and speed of the motor and determines the equivalent circuit parameters. Once these parameters are known, the efficiency of motor can be evaluated. The simulation results on a 3.75 kW motor are presented and compared with the results of torque gauge method (TGM), equivalent circuit method (ECM), slip method (SM), current method (CM) and segregated loss method (SLM). The results reveal that the proposed method can evaluate the efficiencies of motor with less than 3% error under normal load conditions. Consequently, the method can be used in motor energy management system for improving the overall energy savings in industry. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Efficiency evaluation Induction motor Cost savings Equivalent circuit particle swarm algorithm Payback analysis

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