TY - GEN
T1 - Ant colony algorithm and simulated annealing algorithm based process route optimization
AU - Zhai, Dehui
AU - Zhang, Faping
AU - Gao, Bo
AU - Han, Wenli
AU - Zhang, Tiguang
AU - Zhang, Jiajun
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/23
Y1 - 2014/12/23
N2 - Process route sequencing is one of the key technologies for computer aided process planning (CAPP). In this paper, manufacturing feature was divided into several feature process elements and the precedence constraint matrix was generated by fully considering the constraint relationship among feature process elements. A hybrid algorithm, based on ant colony algorithm and simulated annealing algorithm, was proposed for process route sequencing. Considering the machine tools, cutting tools and fixtures as the manufacturing resources, minimal number of manufacturing resources changes served as the objective function to optimize the process route. The ant colony algorithm was adopted to traverse under the constraint rules among all the feature process elements, and simulated annealing algorithm was introduced to update pheromones to find the optimal process route meeting the constraint rules. To verify performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show that the proposed approach has achieved significant improvement for process route sequencing.
AB - Process route sequencing is one of the key technologies for computer aided process planning (CAPP). In this paper, manufacturing feature was divided into several feature process elements and the precedence constraint matrix was generated by fully considering the constraint relationship among feature process elements. A hybrid algorithm, based on ant colony algorithm and simulated annealing algorithm, was proposed for process route sequencing. Considering the machine tools, cutting tools and fixtures as the manufacturing resources, minimal number of manufacturing resources changes served as the objective function to optimize the process route. The ant colony algorithm was adopted to traverse under the constraint rules among all the feature process elements, and simulated annealing algorithm was introduced to update pheromones to find the optimal process route meeting the constraint rules. To verify performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show that the proposed approach has achieved significant improvement for process route sequencing.
KW - ant colony algorithm
KW - feature process element
KW - process route
KW - simulated annealing algorithm
UR - http://www.scopus.com/inward/record.url?scp=84922781003&partnerID=8YFLogxK
U2 - 10.1109/ES.2014.43
DO - 10.1109/ES.2014.43
M3 - Conference contribution
AN - SCOPUS:84922781003
T3 - Proceedings - 2nd International Conference on Enterprise Systems, ES 2014
SP - 102
EP - 107
BT - Proceedings - 2nd International Conference on Enterprise Systems, ES 2014
A2 - Xu, Li Da
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Enterprise Systems, ES 2014
Y2 - 2 August 2014 through 3 August 2014
ER -