Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
Future Generation Computer Systems
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
A fuzzy genetic algorithm for real-world job shop scheduling
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Alternative solution representations for the job shop scheduling problem in ant colony optimisation
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
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Production scheduling problems such as the job shop consist of a collection of operations (grouped into jobs) that must be scheduled for processing on different machines. Typical ant colony optimisation applications for these problems generate solutions 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 a dispatching rule, which heuristically determines the order of operations on 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.