Abstract
An important part of modern integrated decision support systems in any area is scheduling. At the same time, domain knowledge can be extremely complex and includes numerous restrictions and process rules, which makes the corresponding optimization problem highly complicated. This paper is based on a hierarchical problem structure where the top-level problem is the travelling salesman problem, and the nested resource-constrained project scheduling problem is replaced by the simulation model. Cooperative ant colony optimization as well as other biology-inspired algorithms were evaluated on a set of generated problems, and the obtained experimental results are presented here.
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