Modeling semiconductor testing job scheduling and dynamic testing machine configuration

Authors:
Jei-Zheng Wu;Chen-Fu Chien
Affiliations:
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101 Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan, ROC;Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101 Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 10
Cited 7

The shifting bottleneck procedure for job shop scheduling

Management Science
Efficient algorithms for scheduling semiconductor burn-in operations

Operations Research
New search spaces for sequencing problems with application to job shop scheduling

Management Science
A genetic algorithm for flowshop sequencing

Computers and Operations Research - Special issue on genetic algorithms
Evolution based learning in a job shop scheduling environment

Computers and Operations Research - Special issue on genetic algorithms
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation

Computers and Industrial Engineering
Optimal design of system reliability using interval programming and genetic algorithms

CIE '96 Proceedings of the 19th international conference on Computers and industrial engineering
A tutorial survey of job-shop scheduling problems using genetic algorithms, part II: hybrid genetic search strategies

Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
An Efficient Genetic Algorithm for Job Shop Scheduling Problems

Proceedings of the 6th International Conference on Genetic Algorithms
Data mining for yield enhancement in semiconductor manufacturing and an empirical study

Expert Systems with Applications: An International Journal

A fuzzy-knowledge resource-allocation model of the semiconductor final test industry

Robotics and Computer-Integrated Manufacturing
Optimizing patrol force deployment using a genetic algorithm

Expert Systems with Applications: An International Journal
Estimating production test properties from test measurement data

Applied Stochastic Models in Business and Industry
A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem

Journal of Intelligent Manufacturing
Network modeling and evolutionary optimization for scheduling in manufacturing

Journal of Intelligent Manufacturing
Inventory write-down prediction for semiconductor manufacturing considering inventory age, accounting principle, and product structure with real settings

Computers and Industrial Engineering
A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem

Knowledge-Based Systems

Quantified Score

Hi-index	12.05

Visualization

Abstract

The overall flow of the final test of integrated circuits can be represented by the job shop model with limited simultaneous multiple resources in which various product mixes, jobs recirculation, uncertain arrival of jobs, and unstable processing times complicate the problem. Rather than relying on domain experts, this study aims to develop a hybrid approach including a mathematical programming model to optimize the testing job scheduling and an algorithm to specify the machine configuration of each job and allocate specific resources. Furthermore, a genetic algorithm is also developed to solve the problem in a short time for implementation. The results of detailed scheduling can be graphically represented as timetables of testing resources in Gantt charts. The empirical results demonstrated viability of the proposed approach.