Informed Development of Meta Heuristics for Spatial Forest Planning Problems

In this research application paper, the usefulness of an intelligent mechanism (a cubic spline smoothing technique) for determining when to switch from one algorithm to another within a meta heuristic search process is explored. We concentrated on a typical planning problem for a southern United States forestry company where the net present value of management activities is maximized subject to wood flow and harvest adjacency constraints. We found that more than 75% of the 3-algorithm meta heuristics examined produced consistently better solutions than the best standard heuristic (threshold accepting) in terms of mean and maximum solution values. However, a 2-algorithm meta heuristic (threshold accepting + tabu search) performed the best in terms of the average solution value and the absolute maximum solution value, improving solution quality 1.4% over the best standard heuristic solution value. Results also indicate meta heuristics which began a search with a relatively fast, stochastic search process (simulated annealing or threshold accepting) and end a search with a relatively slow, deterministic search process (e.g., tabu search) produced better solutions than other model configurations for the problem examined. Further, results suggest that the time to switch from one heuristic to another should be based on when the improvement in solution quality stagnates. Without recognizing this point, a search process may switch prematurely or be computationally wasteful.