The shifting bottleneck procedure for job shop scheduling
Management Science
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
Fitness inheritance in genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
A combined dispatching criteria approach to scheduling semiconductor manufacturing systems
Computers and Industrial Engineering
Journal of Global Optimization
Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
Computers and Operations Research
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
A multi-criteria approach for scheduling semiconductor wafer fabrication facilities
Journal of Scheduling
Computers and Industrial Engineering
Scheduling jobs on parallel machines with setup times and ready times
Computers and Industrial Engineering
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
Multiple-objective scheduling and real-time dispatching for the semiconductor manufacturing system
Computers and Operations Research
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
Expensive multiobjective optimization by MOEA/D with Gaussian process model
IEEE Transactions on Evolutionary Computation
Parallel hybrid metaheuristics for the flexible job shop problem
Computers and Industrial Engineering
A new dispatching rule based genetic algorithm for the multi-objective job shop problem
Journal of Heuristics
Computers and Operations Research
GPU computation in bioinspired algorithms: a review
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Multiobjective scheduling of jobs with incompatible families on parallel batch machines
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
Multiobjective GA optimization using reduced models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Genetic algorithms to solve a single machine multiple orders per job scheduling problem
Proceedings of the Winter Simulation Conference
Generating dispatching rules for semiconductor manufacturing to minimize weighted tardiness
Proceedings of the Winter Simulation Conference
Computers and Industrial Engineering
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Scheduling is a critical and challenging task in manufacturing systems, especially in large-scale complex systems like wafer fabrication facilities. Although evolutionary algorithms (EAs) have demonstrated many successful applications in the field of manufacturing scheduling, there are very few studies on scheduling of wafer fabs using EAs. Dispatching rules are one of the most common techniques for fab scheduling. In this paper, we present six ways of applying EAs for enhancing the rule-based scheduling system. We provide potential EA-based solutions and review relevant literature. Many of the mentioned viewpoints can serve as new research topics for both researchers in the fields of scheduling and evolutionary computation (EC). Several general EC techniques including multiobjective optimization, expensive optimization, and parallelization are also introduced and shown to be helpful to fab scheduling.