Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Proceedings of the 14th annual conference on Computers and industrial engineering
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Minimizing the number of tardy jobs in identical machine scheduling
Proceedings of the 15th annual conference on Computers and industrial engineering
Cell loading in cellular manufacturing systems
Proceedings of the 15th annual conference on Computers and industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Industrial Engineering - Special issue: Selected papers from the 25th international conference on computers & industrial engineering in New Orleans, Louisiana
Pedestrian routing system using genetic algorithms suitable for mobile ad hoc networks
MATH'06 Proceedings of the 10th WSEAS International Conference on APPLIED MATHEMATICS
Computers and Industrial Engineering
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In this paper, the potential application of genetic algorithms to cell loading is discussed. The objective is to minimize the number of tardy jobs. Three different approaches are proposed and later compared. The first approach consists of two steps where (1) genetic algorithms is used to generate a job sequence and (2) a classical scheduling rule is used to assign jobs to the cells. The second approach consists of three steps where steps 1 and 2 are identical to the first approach plus step (3) Local Optimizer is applied to each cell independently. The third approach is very similar to the second approach except that chromosomes are modified to reflect the changes due to learning with local optimizer. Experimentation results show that the number of cells and the crossover strategy adapted affect the number of tardy jobs found. The results also indicate that hybrid GA-local optimizer approach improves the solution quality drastically. However, it has been also shown that GA alone can duplicate the performance of the hybrid approach with increased population size and number of generations in some of the cases. Finally, the impact of learning on the solution quality was not as significant as expected.