Evolution program for bicriteria transportation problem
ICC&IE-94 Selected papers from the 16th annual conference on Computers and industrial engineering
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms
Data mining for yield enhancement in semiconductor manufacturing and an empirical study
Expert Systems with Applications: An International Journal
Modeling semiconductor testing job scheduling and dynamic testing machine configuration
Expert Systems with Applications: An International Journal
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
A fuzzy-knowledge resource-allocation model of the semiconductor final test industry
Robotics and Computer-Integrated Manufacturing
Scheduling algorithms for a semiconductor probing facility
Computers and Operations Research
A robust genetic algorithm for scheduling realistic hybrid flexible flow line problems
Journal of Intelligent Manufacturing
Measuring relative performance of wafer fabrication operations: a case study
Journal of Intelligent Manufacturing
A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem
Knowledge-Based Systems
Hi-index | 0.00 |
To improve capital effectiveness in light of demand fluctuation, it is increasingly important for high-tech companies to develop effective solutions for managing multiple resources involved in the production. To model and solve the simultaneous multiple resources scheduling problem in general, this study aims to develop a genetic algorithm (bvGA) incorporating with a novel bi-vector encoding method representing the chromosomes of operation sequence and seizing rules for resource assignment in tandem. The proposed model captured the crucial characteristics that the machines were dynamic configuration among multiple resources with limited availability and sequence-dependent setup times of machine configurations between operations would eventually affect performance of a scheduling plan. With the flexibility and computational intelligence that GA empowers, schedule planners can make advanced decisions on integrated machine configuration and job scheduling. According to a number of experiments with simulated data on the basis of a real semiconductor final testing facility, the proposed bvGA has shown practical viability in terms of solution quality as well as computation time.