Constrained Optimization problems are ubiquitous, whether one is interested in the design of an integrated circuit or a car, the production of a factory schedule, or the routing of school buses. One promising approach to solving these problems involves using Simulated Annealing (SA) search. This is a stochastic neighborhood search procedure that moves from one solution to another, while recording the best solution found so far. Typically, the procedure attempts to move to solutions that improve over the current one, though occasionally transitions to lower quality solutions are accepted in an attempt to avoid local optima. SA has been shown to yield near-optimal solutions to many difficult combinatorial optimization problems, if run a sufficiently large number of times.