A comparison of a neighborhood search technique for forest spatial harvest scheduling problems: A case study of the simulated annealing algorithm

• The usefulness of the s-metaheuristic neighborhood search of simulated annealing was explored.
• The 2-opt moves can significantly improve the quality of solutions than 1-opt moves.
• The maximum solution values were usually more than 98% of the estimated optimal values.
• The spatial constraint types had significant effects on the ultimate economic benefits.

In this research application paper, the usefulness of the s-metaheuristic neighborhood search technique of simulated annealing algorithm when applied to forest management planning problems was explored. We concentrated on tactical forest spatial harvest scheduling problems where the net present value of management activities over thirty 1-yr periods was to be maximized. Constraints mainly included those related to the need for an even-flow of scheduled wood products and the need for spatial constraint types, i.e., unit restriction model and area restriction model, respectively. Four hypothetical grid datasets with different age class distributions (i.e., young, normal, older and spatially organized) and one real dataset from northeastern China were used to illustrate how a 2-opt moves can intensify a search within high-quality areas of a solution space and thus produce higher-valued solutions as compared to the sole use of 1-opt moves. Finally, extreme value theory was employed to estimate the global optimum solution and to evaluate the quality of the heuristic solutions. We found that the 2-opt technique not only produced consistently better solutions than the 1-opt technique in terms of the mean and maximum solutions values, but also significantly decreased the standard deviations associated with the sets of solutions. The maximum solution values were usually more than 98% of the estimated optimal values. The motivation for using a 2-opt technique is found in the generation of more efficient solutions that will allow a forestry organization to produce higher returns to its owners.