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Knowledge-based ant colony optimization for the flexible job shop scheduling problems. (English) Zbl 1201.90091

Summary: Integrating knowledge model and heuristic searching model can be seen as a useful tool in the search of an optimal solution. It proposed a Knowledge-based Ant Colony Optimization (KACO) for solving the Flexible Job Shop Scheduling Problem (FJSSP) in this work. Knowledge model and Ant Colony Optimization (ACO) model are two modules of KACO. The ACO model takes charge of searching through the vast solution space and identifying an optimal solution. The knowledge model learns some available knowledge from the evolution, and then applies the existing knowledge to guide the current heuristic searching. The optimization performance of the proposed approach has been improved largely by efficaciously integrating scheduling knowledge with ACO. The experimental results suggest that the proposed algorithm is a feasible and effective approach for the Flexible Job Shop Scheduling Problem.

MSC:

90B35 Deterministic scheduling theory in operations research
90C27 Combinatorial optimization
90C59 Approximation methods and heuristics in mathematical programming

Software:

FJSSP
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