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2 results on '"Han, Zengliang"'

1. Research of multi-AGV scheduling system based on a new mixed regional control model

Author

Dongqing Wang and Han Zengliang

Subjects
0209 industrial biotechnology, 021103 operations research, 020901 industrial engineering & automation, Computer science, Distributed computing, 0211 other engineering and technologies, 02 engineering and technology, Deadlock, Scheduling system, Scheduling (computing)
Abstract

In order to reduce the inherent defects and complexity of the multi-AGV system, a new guide-path configuration of mixed regional control module and its path scheduling is investigated. By a scheme of the shortest waiting time for the task, the efficiency of the system running time is achieved in the multi-AGV system. By a distributed coordination control approach, the conflicts and deadlocks between AGVs are solved online. The simulation results show the effectiveness and feasibility of this method.

Published
2017

2. Multi-AGV path planning with double-path constraints by using an improved genetic algorithm

Author

Feng Liu, Dongqing Wang, Han Zengliang, and Zhao Zhiyong

Subjects
0209 industrial biotechnology, Heredity, Computer science, Crossover, lcsh:Medicine, 02 engineering and technology, Any-angle path planning, 020901 industrial engineering & automation, Software, 0202 electrical engineering, electronic engineering, information engineering, Natural Selection, lcsh:Science, Multidisciplinary, Heuristic, Chromosome Biology, Population size, Applied Mathematics, Simulation and Modeling, Physical Sciences, 020201 artificial intelligence & image processing, Algorithms, Research Article, Optimization, Mathematical optimization, Evolutionary Processes, Population Size, Automated guided vehicle, Research and Analysis Methods, Chromosomes, Chromosomal Inheritance, Population Metrics, Genetic algorithm, Genetics, Computer Simulation, Motion planning, Molecular Biology Techniques, Molecular Biology, Evolutionary Biology, Population Biology, business.industry, Genetic Algorithms, lcsh:R, Gene Mapping, Biology and Life Sciences, Computational Biology, Reproducibility of Results, Cell Biology, Models, Theoretical, Path (graph theory), lcsh:Q, business, Mathematics
Abstract

This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm.

Published
2017

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