  This research concerns a type of configuration optimization problems
frequently encountered in engineering design and manufacturing, where the
envelope volume in space occupied by a number of components needs to be
minimized along with other objectives such as minimizing connective lines
between the components under various constraints. Since in practical
applications the objectives and constraints are usually complex, the
formulation of computationally tractable optimization becomes difficult.
Moreover, unlike conventional multi-objective optimization problems, such
configuration problems usually comes with a number of demanding constraints
that are hard to satisfy, which results in the critical challenge of balancing
solution feasibility with optimality. In this research, we first present the
mathematical formulation for a representative problem of configuration
optimization with multiple hard constraints, and then develop two versions of
an enhanced multi-objective simulated annealing approach, referred to as
MOSA/R, to solve this problem. To facilitate the optimization computationally,
in MOSA/R, a versatile re-seed scheme that allows biased search while avoiding
pre-mature convergence is designed. Our case study indicates that the new
algorithm yields significantly improved performance towards both constrained
benchmark tests and constrained configuration optimization problem. The
configuration optimization framework developed can benefit both existing
design/manufacturing practices and future additive manufacturing.
