Simulated annealing is a heuristic search process that is very similar to threshold accepting, with one key difference that is associated with the acceptance rule employed when an inferior solution is proposed. As with threshold accepting, simulated annealing might be considered a refinement of Monte Carlo simulation. During the search process, a single solution is maintained, and a random perturbation is proposed as a change to it (Form 4 in Chap. 7 ). Should the proposed change result in a higher quality, feasible solution, it is automatically accepted. However, should the change result in an inferior solution, it may be acceptable based on a decision that varies by (a) how distant the objective function value of the proposed solution is from the objective function value of the previous (or best) solutions, and (b) how long the heuristic search has been functioning. Some enhancements to the basic structure of a simulated annealing search process allow it to produce high quality results for forest harvest scheduling problems.