Stochastic optimization models in forest planning: a progressive hedging solution approach

• Published in: Annals of operations research Vol. 232; no. 1; pp. 259 - 274
• Main Authors: Veliz, Fernando Badilla, Watson, Jean-Paul, Weintraub, Andres, Wets, Roger J.-B., Woodruff, David L.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2015; Springer; Springer Nature B.V
• Subjects: Analysis; Business and Management; Combinatorics; Computer simulation; Decision making; Forest management; Forestry; Forests; Harvest; Hedging; Highway construction; Integer programming; Integrated approach; Learning models (Stochastic processes); Mathematical models; Mathematical optimization; Mathematical programming; Methods; Operations research; Operations Research/Decision Theory; Optimization; Road construction; Roads & highways; Serials; Solution strengthening; Studies; Theory of Computation; Timber; Trees; United States; Forest harvest planning; Progressive hedging; Forestry; Stochastic programming
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-014-1608-4

We consider the important problem of medium term forest planning with an integrated approach considering both harvesting and road construction decisions in the presence of uncertainty modeled as a multi-stage problem. We give strengthening methods that enable the solution of problems with many more scenarios than previously reported in the literature. Furthermore, we demonstrate that a scenario-based decomposition method (Progressive Hedging) is competitive with direct solution of the extensive form, even on a serial computer. Computational results based on a real-world example are presented.