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Meeting Agenda 02-18-14

Meeting Agenda 02-18-14

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Meeting Agenda 02-18-14

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  1. Meeting Agenda 02-18-14 • Overview (概观) • Questions Recap and Discussion (上周遗留问题) • Priority Rule (零件优先级) • Optimization (优化) • Optimization steps (优化步骤)

  2. (1) Overview (概观) • Previous Week and Current Week: • Implemented GA + LM for SEAS case study. (遗传算法+莱文贝格 - 马夸特方法结果) • Priority rule has been included in the feasibility check. (零件优先级可行性分析 已完成) • Initial research on optimization. • Questions from SEAS last week: • Priority rule to be included in feasibility check. (零件优先级可行性分析) • How to deal with the problem when input sheet is the same and KPIs are different? This is due to non-critical parts. (关键零件范围的确定) • Feasibility on multi-machines. (可行性分析针对多台非关键设备加工) • Next Week: • Continue our research on different optimization algorithms. (继续研究不同的优化算法) • Establish simple priority rules in order to implement one of the optimization techniques. (建立简单约束条件 )

  3. (2) Questions Recap (上周遗留问题) 1、Priority rule to be included in feasibility check. (零部件加工的优先级的可行性分析) Refer to slide 4 and 5 2、How to deal with the problem when input sheet is the same and KPIs are different? This is due to non-critical parts? (关键零件范围的确定) To be discussed. 3、Feasibility on multi-machines (对于关键零件采用多台非关键设备加工时,非关键设备的可行性分析) Input as [9.1 9.2 9.3]

  4. (3) Priority Rule (零件优先级) • Ensures that higher priority work orders are processed before lower priority work orders. • We have ensured that the last step of a higher priority work order is completed before the lower priority work order can begin. • Every first and last steps are defined as critical steps. • This will be done on a sub-work order level

  5. 401 701 101 501 801 601 201 3201 1101 301 901 1201 1301 1001 1401 1501 1601 1701 3301 1801 3401 1901 2001 2101 2201 Red color indicates the part index (1-34) Blue color indicates the sub-work order (1-20) 2301 2401 2501 2601 2701 2801 2901 3001 3101

  6. (4) Optimization Algorithms (优化)

  7. (5) Optimization steps (优化步骤) • Establish some basic constraints on the decisions. (建立简单约束条件) • Train the neural network with 20 scenarios (20组情境作为训练) • Optimize the decisions of the other 8 scenarios based on their Inputs (given) in order to improve the KPI (其余8组优化比较) Optimization Genetic Algorithm/Particle Swarm Generation of suggested decision KPI/Optimized Schedule Inputs (given) Trained Neural Network Repeat until stopping criteria satisfied