The Research and Application of a Dynamic Dispatching Rule Selection Approach Based on BPSO-SVM for Semiconductor Production Line

2013 ◽  
pp. 487-495
Author(s):  
Kuo Tian ◽  
Yu-min Ma ◽  
Fei Qiao
Keyword(s):  
Production Line ◽  
Dispatching Rule ◽  
Rule Selection ◽  
Selection Approach ◽  
Dynamic Dispatching ◽  
Semiconductor Production

scholarly journals Dynamic Scheduling of a Semiconductor Production Line Based on Composite Rule Set

2017 ◽  
Author(s):  
Yumin Ma ◽  
Fei Qiao ◽  
Fu Zhao ◽  
John W. Sutherland
Keyword(s):  
Production Line ◽  
Dynamic Scheduling ◽  
Production Performance ◽  
Support Vector ◽  
Dispatching Rule ◽  
Production Schedule ◽  
Rule Based ◽  
Rule Set ◽  
Key Variables ◽  
Semiconductor Production

Various factors and constraints should be considered when developing a manufacturing production schedule, and such a schedule is often based on rules. This paper develops a composite dispatching rule based on heuristic rules that comprehensively consider various factors in a semiconductor production line. The composite rule is obtained by exploring various states of a semiconductor production line (machine status, queue size, etc.), where such indicators as makespan and equipment efficiency are used to judge performance. A model of the response surface, as a function of key variables, is then developed to find the optimized parameters of a composite rule for various production states. Further, dynamic scheduling of semiconductor manufacturing is studied based on support vector regression (SVR). This approach dynamically obtains a composite dispatching rule (i.e. parameters of the composite dispatching rule) that can be used to optimize production performance according to real-time production line state. Following optimization, the proposed dynamic scheduling approach is tested in a real semiconductor production line to validate the effectiveness of the proposed composite rule set.

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.  

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