  Determining the optimal location of control cabinet components requires the
exploration of a large configuration space. For real-world control cabinets it
is impractical to evaluate all possible cabinet configurations. Therefore, we
need to apply methods for intelligent exploration of cabinet configuration
space that enable to find a near-optimal configuration without evaluation of
all possible configurations. In this paper, we describe an approach for
multi-objective optimization of control cabinet layout that is based on Pareto
Simulated Annealing. Optimization aims at minimizing the total wire length used
for interconnection of components and the heat convection within the cabinet.
We simulate heat convection to study the warm air flow within the control
cabinet and determine the optimal position of components that generate heat
during the operation. We evaluate and demonstrate the effectiveness of our
approach empirically for various control cabinet sizes and usage scenarios.
