Evaluating neighborhood search techniques of simulated annealing based on forest spatial harvest scheduling problems

Neighborhood search techniques have become one of the most important strategies to improve the resolution efficiency of heuristics in forestry, however a drastically debate on the resolution efficiency of this search strategy has been put forward recently. Simulated annealing algorithm, as an example of heuristics, has been employed in a wide set of forestry planning problems. Therefore, the overall goals of this research were to evaluate the performances of different neighborhood search techniques of simulated annealing in forest spatial planning problems. The objective function was to maximize the harvest volume over ten 5-year planning periods, which mainly included timber volume flow constraints, ending inventory constraints, unit restriction model and green-up constraints. The tested neighborhood search techniques were 1-opt moves, and 2-opt moves of simulated annealing which have been widely used in forestry planning, in which the candidate solutions of 1-opt moves were generated by randomly changing the treatment of just one unit, however the candidate solutions of 1-opt moves were generated by randomly changing the treatments of two units simultaneously. The planning problems were applied to three hypothetical datasets, which encompassed 3293 (forestⅠ), 29536 (forestⅡ)and 81625 (forest Ⅲ) binary decision variables. The results showed that the 2-opt technique of simulated annealing can locate the maximum solutions for all the three planning problems, however increasing the number of units for changing the treatment schedule simultaneously in more than one unit did not improve the performance of simulated annealing if the combinatorial problems were very large (i.e., the number of management units within a forest was larger than 3600). Since the planning results highly depend on the sizes of planning problems, thus forest managers and planners should pick up the optimization techniques carefully when they plan to make forest plans in practices.