Solution representation for job shop scheduling problems in ant colony optimisation

Ant colony optimisation, a constructive metaheuristic inspired by the foraging behaviour of ants, has been applied to a wide range of problems since its inception. Many of these are production 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. This paper considers an alternative approach in which each machine is assigned one of a number of alternative dispatching rules, which heuristically determines the processing order for that machine. This representation creates a substantially smaller search space that likely contains good solutions. The performance of both approaches is compared on a real-world job shop scheduling problem in which processing times and job due dates are modelled with fuzzy sets. Results indicate that the new approach produces better solutions more quickly than the traditional approach.