Analysis of AGV Optimal Path Problem in Smart Factory Based on Genetic Simulated Annealing Algorithm

DOI

10.2991/wartia-18.2018.27How to use a DOI?

Keywords

Genetic algorithm, Simulated annealing algorithm, AGV, MATLAB.

Abstract

This paper analyzes the optimal path problem of AGV for smart factories, and propose a hybrid algorithm combining genetic algorithm and simulated annealing algorithm. The objective function model is established according to the target optimization, the improved genetic algorithm and simulated annealing algorithm can be used to obtain better path and can effectively fall into local optimum. Simulation results show that this improved genetic simulated annealing algorithm is feasible in solving the AGV optimal path of smart factories.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chaonan Fan
AU  - Shixin Li
AU  - Rong Guo
AU  - Yuxin Wu
PY  - 2018/09
DA  - 2018/09
TI  - Analysis of AGV Optimal Path Problem in Smart Factory Based on Genetic Simulated Annealing Algorithm
BT  - Proceedings of the 4th Workshop on Advanced Research and Technology in Industry (WARTIA 2018)
PB  - Atlantis Press
SP  - 166
EP  - 170
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-18.2018.27
DO  - 10.2991/wartia-18.2018.27
ID  - Fan2018/09
ER  -