A MULTI-OBJECTIVE MATHEMATICAL OPTIMIZATION APPROACH FOR THE FOREST PLANNING PROBLEM

PIEDRA-JIMENEZ, F; TASSIN, N; NOVAS, JM; BROZ, D.; RODRÍGUEZ, MA

Buenos Aires

Conferencia; CLAIO 2022 -XXI Latin Ibero-American Conference on Operations Research; 2022

Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires

A general mathematical framework based on a Generalized Disjunctive Pro-gramming (GDP) approach for optimal forest planning problems is proposed in this work. For this purpose, a Multi-objective GDP (MO-GDP) model is con-structed and it is reformulated as a Multi-objective Mixed Integer Linear Pro-gramming (MO-MILP) model. The model determines the optimal forest man-agement alternative (combination of silvicultural treatments), the proportion of land area to be harvested, and the flow of timber products from harvesting nodes to forest industries. The proposed mathematical formulation simultaneously ad-dresses two conflicting objectives: the maximization of the net present value and the minimization of the absolute deviations of timber assortment production be-tween consecutive periods. SisPinus® growth simulator is used to estimate tim-ber yields, and the MO-MILP developed model is solved in GAMS. In addition, two alternative iterative procedures, the so-called, ϵ-constraint and the AUGMECON methods are used to obtain the Pareto optimal solutions. The fea-sibility of the proposed model is tested using real data from a company located in the north of the province of Misiones. Computational results show that the de-signed framework serves as a decision-making tool to provide diverse solutions with different trade-offs among the considered criteria.