Future Generation Computer Systems
Ant Colony Optimization
Structural advantages for ant colony optimisation inherent in permutation scheduling problems
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Solution bias in ant colony optimisation: Lessons for selecting pheromone models
Computers and Operations Research
Solution representation for job shop scheduling problems in ant colony optimisation
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
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Ant colony optimisation (ACO), a constructive metaheuristic inspired by the foraging behaviour of ants, has frequently been applied to shop scheduling problems such as the job shop, in which a collection of operations (grouped into jobs) must be scheduled for processing on different machines. In typical ACO applications solutions are generated by constructing a permutation of the operations, from which a deterministic algorithm can generate the actual schedule. An alternative approach is to assign each machine one of a number of alternative dispatching rules to determine its individual processing order. This representation creates a substantially smaller search space biased towards good solutions. A previous study compared the two alternatives applied to a complex real-world instance and found that the new approach produced better solutions more quickly than the original. This paper considers its application to a wider set of standard benchmark job shop instances. More detailed analysis of the resultant search space reveals that, while it focuses on a smaller region of good solutions, it also excludes the optimal solution. Nevertheless, comparison of the performance of ACO algorithms using the different solution representations shows that, using this solution space, ACO can find better solutions than with the typical representation. Hence, it may offer a promising alternative for quickly generating good solutions to seed a local search procedure which can take those solutions to optimality.