Job-shop scheduling using automated reasoning: a case study of the car-sequencing problem
Journal of Automated Reasoning
Constraint satisfaction using constraint logic programming
Artificial Intelligence - Special volume on constraint-based reasoning
Self-Organization in Biological Systems
Self-Organization in Biological Systems
High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid?
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Computers and Operations Research
Beam-ACO Based on Stochastic Sampling for Makespan Optimization Concerning the TSP with Time Windows
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Hybridizing Beam-ACO with Constraint Programming for Single Machine Job Scheduling
HM '09 Proceedings of the 6th International Workshop on Hybrid Metaheuristics
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
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Revisiting the sequence constraint
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Ants can solve constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
On the complexity of the car sequencing problem
Operations Research Letters
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Hybrid methods for solving combinatorial optimization problems have become increasingly popular recently. The present paper is concerned with hybrids of ant colony optimization and constraint programming which are typically useful for problems with hard constraints. However, the original algorithm suffered from large CPU time requirements. It was shown that such an integration can be made efficient via a further hybridization with beam search resulting in CP-Beam-ACO. The original work suggested this in the context of job scheduling. We show here that this algorithm type is also effective on another problem class, namely the car sequencing. We consider an optimization version, where we aim to optimize the utilization rates across the sequence. Car sequencing is a notoriously difficult problem, because it is difficult to obtain good bounds via relaxations. We show that stochastic sampling provides superior results to well known lower bounds for this problem when combined with CP-Beam-ACO.