%0 Research article
%T A new heuristic method for solving spatially constrained forest planning problems based on mitigation of infeasibilities radiating outward from a forced choice
%A Bettinger, Pete
%A Zhu, Jianping
%D 2006
%J Silva Fennica
%V 40
%N 2
%R doi:10.14214/sf.477
%U https://silvafennica.fi/article/477
%X A new heuristic method to mitigate infeasibilities when a choice is  forced into a solution was developed to solve spatially constrained  forest planning problems. One unique aspect of the heuristic is the  introduction of unchosen decision choices into a solution regardless of  the resulting infeasibilities, which are then mitigated by selecting  next-best choices for those spatial units that are affected, but in a  radiating manner away from the initial choice. As subsequent changes are  made to correct the affected spatial units, more infeasibilities may  occur, and these are corrected as well in an outward manner from the  initial choice. A single iteration of the model may involve a number of  changes to the status of the decision variables, making this an n-opt  heuristic process. The second unique aspect of the search process is the  periodic reversion of the search to a saved (in computer memory) best  solution. Tests have shown that the reversion is needed to ensure better  solutions are located. This new heuristic produced solutions to spatial  problems that are of equal or comparable in quality to traditional  integer programming solutions, and solutions that are better than those  produced by two other basic heuristics. Three small hypothetical forest  examples illustrate the performance of the heuristic against standard  versions of threshold accepting and tabu search. In each of the three  examples, the variation in solutions generated from random starting  points is smaller with the new heuristic, and the difference in solution  values between the new heuristic and the other two heuristics is  significant (p<0.05) when using an analysis of variance. However,  what remains to be seen is whether the new method can be applied  successfully to the broader range of operations research problems in  forestry and other fields.