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Meeting Agenda 02-11-14. Overview ( 概观 ) 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 ) Priority Rule ( 零件优先级 ) Optimization ( 优化 ). (1) Overview. Previous Week and Current Week:
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Meeting Agenda 02-11-14 • Overview (概观) • 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) • Priority Rule (零件优先级) • 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 7 1 5 8 6 2 32 11 3 9 12 13 10 14 15 16 17 33 18 34 19 20 21 • 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 22 23 24 25 26 27 28 29 30 31
(4) Optimization (优化) Trained weight values/neural network Inputs/Outputs (given, decision) Neural Network Optimization Genetic Algorithm/Particle Swarm Generation of suggested decision KPI/Optimized Schedule Inputs (given) Trained Neural Network Repeat until stopping criteria satisfied