Meeting Agenda 02-11-14
(1) Overview (概观)
(2) Genetic Algorithm and Levenberg-Marquardt Algorithm
for SEAS scenario (遗传算法和莱文贝格 - 马夸特方法
结果)
(3) Priority Rule (零件优先级)
(4) Optimization (优化)
(1) Overview
• Previous Week and Current Week:
– Implemented SA + LM for Neural Network weight training. (使用退
火法+莱文贝格 - 马夸特方法训练人工神经网路)
– Compared the results obtained by using SA + LM to those
obtained by just using LM. (成果比较)
– Implemented GA + LM for SEAS case study. (遗传算法+莱文贝格 马夸特方法结果)
– Included a priority rule in the feasibility check. (建立零件优先级)
• Next Week:
– Continue our initial research on different optimization algorithms
(持续研究不同的优化算法)
(2) Genetic Algorithm and Levenberg-Marquardt
Algorithm (遗传算法+莱文贝格 - 马夸特方法)
• Simulation results with 28 scenarios for
training, validation and testing
• Refer to PDF
(3) Priority Rule (零件优先级)
• Ensures that higher priority work orders are processed
before lower priority work orders.
• Please see figure on next page.
4
5
6
11
12
1
2
3
32
13
34
7
8
9
10
14
15
16
17
18
19
20
21
33
22
23
24
25
26
27
28
29
30
31
Six priority rules are established:
- 7 and 8 need to be completed before 11 starts
- 9 and 10 need to be completed before 12 starts
- 4-6 and 11-12 need to be completed before 13
starts
- 1-3 and 13 needs to be completed before 32
starts
- 14-21 needs to be completed before 33 starts
- 22- 33 need to be completed before 34 starts
(4) Optimization (优化)
Inputs/Outputs
(given, decision)
Trained weight
values/neural
network
Neural Network
Optimization
Inputs
(given)
Genetic
Algorithm/Particle
Swarm
Generation of
suggested
decision
Trained Neural
Network
Repeat until stopping criteria
satisfied
KPI/Optimized
Schedule