Stochastic forestry planning under market and growth uncertainty

被引:2
作者
Pais, Cristobal [1 ]
Weintraub, Andres [2 ]
Shen, Zuo-Jun Max [1 ]
机构
[1] Univ Calif Berkeley, Ind Engn & Operat Res Dept, Berkeley, CA 94720 USA
[2] Univ Chile, Ind Engn Dept, Santiago, Chile
关键词
OR in natural resources; Forestry planning; Stochastic programming; Progressive hedging; Parallel optimization; HARVEST SCHEDULING SUBJECT; AREA RESTRICTIONS; PROGRAMMING-MODEL; MANAGEMENT; DECOMPOSITION; OPTIMIZATION; SYSTEM;
D O I
10.1016/j.cor.2023.106182
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The forest planning problem with road construction consists of managing the timber production of a forest divided into harvest cells for a given planning horizon. Subject to uncertainty, it becomes a complex large-scale multi-stage stochastic problem expressed through scenarios. A suitable algorithm for these problems is progressive hedging (PH), which decomposes the problem by scenarios. A two-phase solving approach, in which PH is used as a heuristic method to obtain a directly optimized restricted model with fixed variables, is implemented. Multiple adjustments to improve the performance of the method are adopted and tested in a tactical case study. The performance of the proposed method is compared with those of traditional approaches. Thanks to these enhancements, we solved a real original problem including all the complexities of a practical problem not addressed in previous studies. Comprehensive computational results indicate the advantages of the method, including its ability to efficiently solve instances of up to 1000 scenarios by exploiting its parallel implementation.
引用
收藏
页数:16
相关论文
共 50 条
[1]   Forestry management under uncertainty [J].
Alonso-Ayuso, Antonio ;
Escudero, Laureano F. ;
Guignard, Monique ;
Quinteros, Martin ;
Weintraub, Andres .
ANNALS OF OPERATIONS RESEARCH, 2011, 190 (01) :17-39
[2]   A multicriteria stochastic optimization framework for sustainable forest decision making under uncertainty [J].
Alvarez-Miranda, Eduardo ;
Garcia-Gonzalo, Jordi ;
Pais, Cristobal ;
Weintraub, Andres .
FOREST POLICY AND ECONOMICS, 2019, 103 :112-122
[3]   A problem of forest harvesting and road building solved through model strengthening and Lagrangean relaxation [J].
Andalaft, N ;
Andalaft, P ;
Guignard, M ;
Magendzo, A ;
Wainer, A ;
Weintraub, A .
OPERATIONS RESEARCH, 2003, 51 (04) :613-628
[4]   Stochastic optimization models in forest planning: a progressive hedging solution approach [J].
Badilla Veliz, Fernando ;
Watson, Jean-Paul ;
Weintraub, Andres ;
Wets, Roger J. -B. ;
Woodruff, David L. .
ANNALS OF OPERATIONS RESEARCH, 2015, 232 (01) :259-274
[5]  
Bertsekas D.P., 2014, Constrained optimization and Lagrange multiplier methods
[7]   Dual decomposition in stochastic integer programming [J].
Caroe, CC ;
Schultz, R .
OPERATIONS RESEARCH LETTERS, 1999, 24 (1-2) :37-45
[8]   A new mixed-integer programming model for harvest scheduling subject to maximum area restrictions [J].
Constantino, Miguel ;
Martins, Isabel ;
Borges, Jose G. .
OPERATIONS RESEARCH, 2008, 56 (03) :542-551
[9]   Logistics capacity planning: A stochastic bin packing formulation and a progressive hedging meta-heuristic [J].
Crainic, Teodor Gabriel ;
Gobbato, Luca ;
Perboli, Guido ;
Rei, Walter .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2016, 253 (02) :404-417
[10]   Progressive Hedging-Based Metaheuristics for Stochastic Network Design [J].
Crainic, Teodor Gabriel ;
Fu, Xiaorui ;
Gendreau, Michel ;
Rei, Walter ;
Wallace, Stein W. .
NETWORKS, 2011, 58 (02) :114-124