Threshold accepting, or threshold search, might be characterized as a refinement of Monte Carlo simulation, whereby random changes to the character of a solution to a problem are proposed and found acceptable (a) if the changes improve the quality of the solution, or (b) if the changes result in a different solution whose quality is not much different than the previous (or best) solution. The acceptance criteria, or the threshold, are described in the same units as the units employed in the objective function of a problem. The manner in which the process decides whether to continue forward with an inferior solution suggests that threshold accepting may be more logical to use and more practical to understand than other heuristic search processes. With tactical enhancements that are now common among other heuristic methods, threshold accepting has been shown to produce high quality results for forest planning problems.