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- Publisher Website: 10.1016/j.cor.2023.106182
- Scopus: eid_2-s2.0-85148051638
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Article: Stochastic forestry planning under market and growth uncertainty
Title | Stochastic forestry planning under market and growth uncertainty |
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Authors | |
Keywords | Forestry planning OR in natural resources Parallel optimization Progressive hedging Stochastic programming |
Issue Date | 1-May-2023 |
Publisher | Elsevier |
Citation | Computers and Operations Research, 2023, v. 153 How to Cite? |
Abstract | 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. |
Persistent Identifier | http://hdl.handle.net/10722/336534 |
ISSN | 2023 Impact Factor: 4.1 2023 SCImago Journal Rankings: 1.574 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Pais, C | - |
dc.contributor.author | Weintraub, A | - |
dc.contributor.author | Shen, ZJM | - |
dc.date.accessioned | 2024-02-16T03:57:32Z | - |
dc.date.available | 2024-02-16T03:57:32Z | - |
dc.date.issued | 2023-05-01 | - |
dc.identifier.citation | Computers and Operations Research, 2023, v. 153 | - |
dc.identifier.issn | 0305-0548 | - |
dc.identifier.uri | http://hdl.handle.net/10722/336534 | - |
dc.description.abstract | 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. | - |
dc.language | eng | - |
dc.publisher | Elsevier | - |
dc.relation.ispartof | Computers and Operations Research | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Forestry planning | - |
dc.subject | OR in natural resources | - |
dc.subject | Parallel optimization | - |
dc.subject | Progressive hedging | - |
dc.subject | Stochastic programming | - |
dc.title | Stochastic forestry planning under market and growth uncertainty | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.cor.2023.106182 | - |
dc.identifier.scopus | eid_2-s2.0-85148051638 | - |
dc.identifier.volume | 153 | - |
dc.identifier.eissn | 1873-765X | - |
dc.identifier.isi | WOS:000953804500001 | - |
dc.identifier.issnl | 0305-0548 | - |