The research and application of a dynamic dispatching strategy selection approach based on BPSO-SVM for semiconductor production line

2014 ◽  
Author(s):  
Yu-min Ma ◽  
Xi Chen ◽  
Fei Qiao ◽  
Kuo Tian ◽  
Jian-feng Lu
Keyword(s):  
Production Line ◽  
Strategy Selection ◽  
Selection Approach ◽  
Dynamic Dispatching ◽  
Semiconductor Production

Dynamic Reentrant Scheduling Simulation for Assembly and Test Production Line in Semiconductor Industry

2010 ◽  
Vol 97-101 ◽  
pp. 2418-2422 ◽  
Author(s):  
Ai Jun Liu ◽  
Yu Yang ◽  
Xue Dong Liang ◽  
Ming Hua Zhu ◽  
Hao Yao
Keyword(s):  
Production Scheduling ◽  
Production Line ◽  
Dynamic Scheduling ◽  
Semiconductor Industry ◽  
Product Variety ◽  
Maximum Output ◽  
Resource Conflict ◽  
Conflict Resolution Strategies ◽  
Scheduling Models ◽  
Semiconductor Production

Production scheduling in semiconductor production line is a complex combinatorial optimization problem. It is featured by reentrant production, product variety, frequent machine breakdown and especially reentrant phenomenon and dynamic randomness, which conduces enormous complexity to production line scheduling and management. Based on the resource conflict resolution strategies, dynamic scheduling models for a semiconductor production system are proposed here, aiming at finding maximum machine utilization, optimal man-machine ratio (MMR) and maximum output. Then the validity of the model is illustrated by a simulation case.

scholarly journals Real-time Big Data Processing System to Improve Semiconductor Production Efficiency in Smart Factory

2018 ◽  
Vol 7 (3.33) ◽  
pp. 243
Author(s):  
Hyeopgeon Lee ◽  
Young-Woon Kim ◽  
Ki-Young Kim
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Production Line ◽  
Production Efficiency ◽  
Processing System ◽  
Data Processing System ◽  
Big Data Processing ◽  
Defect Rate ◽  
Semiconductor Production

Semiconductor production efficiency is closely related to the defect rate in the production process. The temperature and humidity control in the production line are very important because these affect the defect rate. So many smart factory of semiconductor production uses sensor. It is installed in the semiconductor process, which send huge amounts of data per second to a central server to carry out temperature and humidity control in each production line. However, big data processing systems that analyze and process large-scale data are subject to frequent delays in processing, and transmitted data are lost owing to bottlenecks and insufficient memory caused by traffic concentrated in the central server. In this paper, we propose a real-time big data processing system to improve semiconductor production efficiency. The proposed system consists of a production line collection system, task processing system and data storage system, and improves the productivity of the semiconductor manufacturing process by reducing data processing delays as well as data loss and discarded data.  

Optimal strategy selection approach to moving target defense based on Markov robust game

2019 ◽  
Vol 85 ◽  
pp. 63-76 ◽  
Author(s):  
Jing-lei Tan ◽  
Cheng Lei ◽  
Hong-qi Zhang ◽  
Yu-qiao Cheng
Keyword(s):  
Optimal Strategy ◽  
Moving Target ◽  
Strategy Selection ◽  
Moving Target Defense ◽  
Selection Approach

scholarly journals Not everything looks like a nail: Learning to select appropriate decision strategies in multiple environments

2016 ◽  
Author(s):  
Hrvoje Stojic ◽  
Henrik Olsson ◽  
Maarten Speekenbrink
Keyword(s):  
Task Demands ◽  
The Other ◽  
Strategy Selection ◽  
Decision Strategy ◽  
Decision Strategies ◽  
Context Sensitive ◽  
Multiple Tasks ◽  
Selection Approach ◽  
Different Types ◽  
Out Of Sample

How do people choose which decision strategy to use? When facing singletasks, research shows that people can learn to select appropriatestrategies. However, what happens when, as is typical outside thepsychological laboratory, they face multiple tasks? Participants werepresented with two interleaved decision tasks, one from a nonlinearenvironment, the other from a linear environment. The environments wereinitially unknown and participants had to learn their properties. Throughcognitive modeling, we examined the types of strategies adopted in bothtasks. Based on out of sample predictions, most participants adopted acue-based strategy in the linear environment and an exemplar-based strategyin the nonlinear environment. A context-sensitive reinforcement learningmodel accounts for this process. Thus, people associated differentstrategies to different types of environments through a trial-and-errortype of process, and learned to flexibly switch between the strategies asneeded. This evidence further supports the strategy selection approach todecision making which assumes that people pick and apply strategiesavailable to them according to task demands.

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