Fitness landscapes and memetic algorithm design
New ideas in optimization
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Direct Chromosome Representation and Advanced Genetic Operators for Production Scheduling
Proceedings of the 5th International Conference on Genetic Algorithms
Repair and Brood Selection in the Traveling Salesman Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Heuristic Combination Method for Solving Job-Shop Scheduling Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
New Crossover Methods For Sequencing Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Genetic Algorithm for Job-Shop Problems with Various Schedule Quality Criteria
Selected Papers from AISB Workshop on Evolutionary Computing
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We propose a hybrid approach for solving hybrid-flow-shop problems based on the combination of genetic algorithms and a modified Giffler & Thompson (G&T) algorithm. Several extensions of the hybrid-flow-shop are considered and discussed in the context of a real-world example. The genome in the GA encodes a choice of rules to be used to generate production schedules via the G&T algorithm. All constraints to the scheduling task are observed by the G&T algorithm. Therefore, it provides a well suited representation for the GA and leads to a decoupling of domain specific details and genetic optimization. The proposed method is apphed to the optimization of a batch annealing plant.