Quality of Threshold Accepting Heuristic Search Results WhenAttempting to Solve Forest Harvest Scheduling Problems

Threshold accepting is a s-metaheuristic, local search process that moves from one solution to another within the feasible region of the solution space via a random change to one or more elements of the solution. Threshold accepting can be further characterized as an aspirational combinatorial optimization process that does not guarantee optimality. The quality of outcomes from a threshold accepting search process varies when applied to forest harvest scheduling problems depending on the parameter value assumptions and sub-processes employed. Two relatively small but realistic case study forests are subjected to four management scenarios and outcomes are examined to illustrate how the quality of solutions may differ when the parameter values and processes employed within threshold accepting are adjusted. Statistically significant improvements in solution quality were generally evident with a slowing of the rate of change in the threshold value and the enhancement of the search process by using 2-opt moves and search reversion. While it was rarely observed, the threshold accepting heuristic search process located the optimal solution of most of the problems modeled. In cases where the problems involved maximizing an economic objective, about 47% of the heuristic search solutions had an objective function value that was within 1% of the optimal solutions.