%0 Research article
%T Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems
%A Bettinger, Pete
%A Graetz, David
%A Boston, Kevin
%A Sessions, John
%A Chung, Woodam
%D 2002
%J Silva Fennica
%V 36
%N 2
%R doi:10.14214/sf.545
%U https://silvafennica.fi/article/545
%X As both spatial and temporal characteristics of desired future  conditions are becoming important measures of forest plan success,  forest plans and forest planning goals are becoming complex. Heuristic  techniques are becoming popular for developing alternative forest plans  that include spatial constraints. Eight types of heuristic planning  techniques were applied to three increasingly difficult forest planning  problems where the objective function sought to maximize the amount of  land in certain types of wildlife habitat. The goal of this research was  to understand the relative challenges and opportunities each technique  presents when more complex difficult goals are desired. The eight  heuristic techniques were random search, simulated annealing, great  deluge, threshold accepting, tabu search with 1-opt moves, tabu search  with 1-opt and 2-opt moves, genetic algorithm, and a hybrid tabu search /  genetic algorithm search process. While our results should not be  viewed as universal truths, we determined that for the problems we  examined, there were three classes of techniques: very good (simulated  annealing, threshold accepting, great deluge, tabu search with 1-opt and  2-opt moves, and tabu search / genetic algorithm), adequate (tabu  search with 1-opt moves, genetic algorithm), and less than adequate  (random search). The relative advantages in terms of solution time and  complexity of programming code are discussed and should provide planners  and researchers a guide to help match the appropriate technique to  their planning problem. The hypothetical landscape model used to  evaluate the techniques can also be used by others to further compare  their techniques to the ones described here.