Combining random and systematic search heuristic procedures for solving spatially constrained forest management scheduling models

Abstract

This article analyzes three heuristics for solving forest management scheduling models encompassing both timber production and landscape structure objectives. Emphasis is on developing an efficient solution approach that may address complex temporal and spatial interactions of forest management scheduling decisions. Two random search approaches, simulated annealing and evolution programs, are compared with a new heuristic, sequential quenching and tempering, that combines random and systematic search techniques. The three heuristics were applied to four large eucalyptus forest management problems. The test forests encompassed 300 to 900 stands. The number of management alternatives ranged from 33,000 to 220,000. Model building encompassed the generation of binary decision variables for all the problems considered. In order to address economic and ecological management objectives, all the models included timber volume flow constraints, minimum and maximum clearcut opening constraints and constraints on the minimum number of old forest patches with minimum area requirements. All constraints were defined over a temporal horizon extending to thirty I yr periods. Results from over 1,300 test computer runs are discussed for application to these large problems. Results show that the new strategy can be compared favorably to the random search approaches. They suggest that in order to find feasible solutions to such a complex problem, random search may be combined with a systematic search component within a heuristic procedure.