A5032565168- Scheduling and Optimization Algorithms
- Advanced Manufacturing and Logistics Optimization
- Metaheuristic Optimization Algorithms Research
- Advanced Algorithms and Applications
- Advanced Control Systems Optimization
- Advanced Sensor and Control Systems
- Fault Detection and Control Systems
- Industrial Technology and Control Systems
- Process Optimization and Integration
- Advanced Multi-Objective Optimization Algorithms
- Robotic Path Planning Algorithms
- Optimization and Search Problems
- Elevator Systems and Control
- Advanced Computational Techniques and Applications
- Assembly Line Balancing Optimization
- Welding Techniques and Residual Stresses
- Optimization and Mathematical Programming
- Image and Signal Denoising Methods
- Neural Networks and Applications
- Adaptive Control of Nonlinear Systems
- Water Quality Monitoring and Analysis
- Manufacturing Process and Optimization
- Evolutionary Algorithms and Applications
- Artificial Immune Systems Applications
- Robotics and Sensor-Based Localization
East China University of Science and Technology
2015-2024
Shandong Institute of Automation
2003-2014
Ministry of Education of the People's Republic of China
2014
Zhejiang Sci-Tech University
2013
Qingdao University of Science and Technology
2007-2012
Shanghai Dianji University
2012
Institute of Automation
1999-2010
Shanghai Institute of Technology
2003-2010
Xiaomi (China)
2009
State Key Laboratory of Chemical Engineering
2007
Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints welding task, and reasonable path to traverse these has significant impact on efficiency. Traditional manual planning techniques can handle few effectively, but when the number is large, it difficult obtain optimal path. The traditional method also time consuming inefficient, cannot guarantee optimality. Double global optimum genetic algorithm–particle swarm optimization...
This article proposes a novel biogeography-based optimization (NBBO) algorithm to solve the distributed assembly permutation flow-shop scheduling problem with sequence-dependent set-up times (DAPFSP-SDST). The objective of this is minimizing maximum completion time (makespan). In initialization phase, NBBO generates two kinds feasible solutions. Secondly, linear migration model replaced sinusoidal and modified product insertion method performed in phase. Then, mutation job used adjust...