원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.9 No.5
2016.05
pp.303-310
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Considering the complexity of flexible workshop scheduling, combined with plant production process characteristics and constraints, we constructed a multi-agent system model to solve multi-objective flexible workshop scheduling problems. This paper proposed an algorithm which was a combination of the ant colony algorithm and Q-learning algorithm. This paper also analyzed and implemented how to solve the workshop scheduling optimization problem. Finally, this paper proved the validity of methods to solve the multi-objective flexible workshop scheduling optimization problems with examples on JADE platform.
목차
Abstract
1. Introduction
2. Multi-objective Flexible Workshop Scheduling
2.1 Problem Description
2.2. Mathematical Model
2.3. Restrictions
3. Workshop Scheduling Multi-agent Mode
4. Q-Learning Algorithm
4.1 Algorithm Description
4.2. Algorithm Steps
5. Global Ant Colony Optimization Algorithm
5.1. Algorithm Description
5.2. Algorithm Steps
6. Example Applications
6.1. Example Overview
6.2. Experimental Environment
6.3 Computational Results
7. Summary
References
1. Introduction
2. Multi-objective Flexible Workshop Scheduling
2.1 Problem Description
2.2. Mathematical Model
2.3. Restrictions
3. Workshop Scheduling Multi-agent Mode
4. Q-Learning Algorithm
4.1 Algorithm Description
4.2. Algorithm Steps
5. Global Ant Colony Optimization Algorithm
5.1. Algorithm Description
5.2. Algorithm Steps
6. Example Applications
6.1. Example Overview
6.2. Experimental Environment
6.3 Computational Results
7. Summary
References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보