AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
The ant colony optimization meta-heuristic
New ideas in optimization
Future Generation Computer Systems
Solving Car Sequencing Problems by Local Optimization
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Performance of a Comprehensive and Efficient Constraint Library Based on Local Search
AI '98 Selected papers from the 11th Australian Joint Conference on Artificial Intelligence on Advanced Topics in Artificial Intelligence
CSPLIB: A Benchmark Library for Constraints
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Tackling car sequencing problems using a generic genetic algorithm
Evolutionary Computation
Combining genetic algorithms with squeaky-wheel optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A GRASP approach for the extended car sequencing problem
Journal of Scheduling
Solution bias in ant colony optimisation: Lessons for selecting pheromone models
Computers and Operations Research
Integration of ACO in a Constraint Programming Language
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Integrated production and distribution planning for single-period inventory products
International Journal of Computer Integrated Manufacturing
Crossover operators for the car sequencing problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
BPM'07 Proceedings of the 5th international conference on Business process management
Car sequencing with constraint-based ACO
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A decomposition approach for the car resequencing problem with selectivity banks
Computers and Operations Research
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This paper describes and compares several heuristic approaches for the car sequencing problem. We first study greedy heuristics, and show that dynamic ones clearly outperform their static counterparts. We then describe local search and ant colony optimization (ACO) approaches, that both integrate greedy heuristics, and experimentally compare them on benchmark instances. ACO yields the best solution quality for smaller time limits, and it is comparable to local search for larger limits. Our best algorithms proved one instance being feasible, for which it was formerly unknown whether it is satisfiable or not.