Artificial intelligence search methods for multi-machine two-stage scheduling
Proceedings of the 1999 ACM symposium on Applied computing
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Efficient Genetic Algorithm Based Data Mining Using Feature Selection with Hausdorff Distance
Information Technology and Management
Expert Systems with Applications: An International Journal
A hybrid approach to design efficient learning classifiers
Computers & Mathematics with Applications
Process industry scheduling optimization using genetic algorithm and mathematical programming
Journal of Intelligent Manufacturing
A genetic algorithm-based rule extraction system
Applied Soft Computing
Journal of Intelligent Manufacturing
Multi-product sequencing and lot-sizing under uncertainties: A memetic algorithm
Engineering Applications of Artificial Intelligence
Simultaneous batch splitting and scheduling on identical parallel production lines
Information Sciences: an International Journal
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Genetic algorithms (GAs) have been used widely for such combinatorial optimization problems as the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. We present a novel approach for solving two related problems-lot sizing and sequencing-concurrently using GAs. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GAs to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow line scheduling