Meeting Agenda (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.

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 need to be completed before 13 starts -1-3 and 13 needs to be completed before 32 starts needs to be completed before 33 starts need to be completed before 34 starts

(4) Optimization ( 优化 ) Inputs/Outputs (given, decision) Neural Network Trained weight values/neural network Inputs (given) Genetic Algorithm/Particle Swarm Generation of suggested decision Trained Neural Network KPI/Optimized Schedule Repeat until stopping criteria satisfied Optimization