Job-shop scheduling using automated reasoning: a case study of the car-sequencing problem
Journal of Automated Reasoning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Combining genetic algorithms with squeaky-wheel optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Tackling car sequencing problems using a generic genetic algorithm
Evolutionary Computation
A study of greedy, local search, and ant colony optimization approaches for car sequencing problems
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
On the complexity of the car sequencing problem
Operations Research Letters
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The car sequencing problem involves scheduling cars along an assembly line while satisfying as many assembly line requirements as possible. The car sequencing problem is NP-hard and is applied in industry as shown by the 2005 ROADEF Challenge. In this paper, we introduce three new crossover operators for solving this problem efficiently using a genetic algorithm. A computational experiment compares these three operators on standard car sequencing benchmark problems. The best operator is then compared with state of the art approach for this problem. The results show that the proposed operator consistently produces competitive solutions for most instances.