Pitfalls and potential of particle swarm optimization for contemporary spatial forest planning

Shan, Yong; Bettinger, Pete; Cieszewski, Chris J.; Wang, Weilin

doi:10.5424/fs/2012213-03692

Cited by 6 publications

(4 citation statements)

References 30 publications

(34 reference statements)

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“…Devido à complexidade na resolução de problemas de planejamento espacial envolvendo a abordagem ARM, uma série de heurísticas foram aplicadas com objetivo de obter soluções em tempos razoáveis de processamento (VIELMA et al, 2007;DONG et al, 2015;GÓMEZ et al, 2011;SHAN et al, 2012;GOMIDE et al, 2013;KAŠPAR et al, 2013;CROWE;NELSON, 2005;BOYLAND et al, 2004;BETTINGER, 2008). Entretanto, com o avanço dos softwares de otimização, formulações exatas foram propostas (GOYCOOLEA et al, 2005).…”

Section: Introductionunclassified

Planejamento Espacial Da Colheita Considerando Áreas Máximas Operacionais

Augustynczik

Arce

Silva

2015

CERNE

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PLANEJAMENTO ESPACIAL DA COLHEITA CONSIDERANDO ÁREAS MÁXIMAS OPERACIONAISRESUMO: Um dos maiores desafios com que se deparam os gestores na realização do planejamento da produção florestal se refere às questões logísticas das operações de colheita. O planejamento operacional da colheita florestal deve ser realizado de forma a conciliar as questões econômicas e ambientais, buscando aumentar a eficiência e mitigar danos relacionados à abertura da cobertura florestal. A inclusão de relações espaciais no planejamento da colheita florestal usualmente é realizada por meio de restrições de adjacência. Para resolver este tipo de problema os principais enfoques utilizados são a abordagem URM (Unit Restriction Model) e a abordagem ARM (Area Restriction Model). Neste contexto, o objetivo do estudo foi realizar o planejamento florestal otimizado dos 236 talhões de uma floresta composta por Pinus taeda e Eucalyptus sp., incorporando ao modelo restrições clássicas como área disponível e equilíbrio, entre outras, e as restrições de adjacência do tipo URM e ARM, avaliando seu efeito no resultado da otimização. Com os resultados da otimização é possível concluir que abordagens ARM e URM são alternativas viáveis para tratar de problemas de agendamento da colheita florestal, sendo que para a área em questão a abordagem ARM é a mais adequada, devido à possibilidade de obter maiores retornos econômicos, respeitando áreas limites de colheita. SPATIAL FOREST HARVEST PLANNING CONSIDERING MAXIMUM OPERATIONAL AREASABSTRACT: One of the biggest challenges that forest managers face in forest planning is related to logistic issues of forest harvesting. The operational planning of forest harvesting must consider simultaneously the economic and environmental concerns, searching for increasing the efficiency and mitigating environmental damage related to the opening of forest cover. The inclusion of spatial aspects in forest harvesting is usually done through adjacency constraints. The main approaches to solve this sort of problem are ARM (Area restriction Model) and URM (Unit Restriction Model) models. In this context, the objective of this study was to perform the optimized forest planning of 236 stands of a Pinus taeda and Eucalyptus sp. forest, incorporating classical constraints like available area, timber flow, among others, and ARM and URM constraints, evaluating their effects on the optimization results. With the optimization outcomes, it is possible to conclude that ARM and URM model approaches are viable alternatives for solving forest harvest scheduling problems, being the ARM approach most suited for our case study, due to the possibility of higher profitability, while respecting maximum harvesting areas.

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Section: Introductionunclassified

Planejamento Espacial Da Colheita Considerando Áreas Máximas Operacionais

Augustynczik

Arce

Silva

2015

CERNE

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“…These heuristics utilize intensification and diversification search strategies to modify the solution in an attempt to locate the global optimum; these processes can further assist in escaping local optima. However, p-metaheuristics, such as genetic algorithms (Lu and Eriksson 2000) and particle swarm optimization (Shan et al 2012), maintain a set of feasible solutions per iteration. With these heuristics, strategies are employed to improve the entire population (or sub-set) of solutions so that a broad area of the solution space might be explored simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the neighborhood, hybrid and reversion search techniques of a simulated annealing algorithm in solving forest spatial harvest scheduling problems

Dong¹,

Bettinger²,

Liu³

et al. 2016

Silva Fenn.

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Highlights• The performances of neighborhood, hybrid and reversion search strategies of simulated annealing were evaluated when implemented with a forest spatial harvest scheduling problem.• The performances of alternative search strategies of simulated annealing were all systematic and clear better than the conventional strategy.• The reversion techniques were significant superior to the other search strategies in solving forest spatial harvest scheduling problems. AbstractHeuristic techniques have been increasingly used to address the complex forest planning problems over the last few decades. However, heuristics only can provide acceptable solutions to difficult problems, rather than guarantee that the optimal solution will be located. The strategies of neighborhood, hybrid and reversion search processes have been proved to be effective in improving the quality of heuristic results, as suggested recently in the literature. The overall aims of this paper were therefore to systematically evaluate the performances of these enhanced techniques when implemented with a simulated annealing algorithm. Five enhanced techniques were developed using different strategies for generating candidate solutions. These were then compared to the conventional search strategy that employed 1-opt moves (Strategy 1) alone. The five search strategies are classified into three categories: i) neighborhood search techniques that only used the change version of 2-opt moves (Strategy 2); ii) self-hybrid search techniques that oscillate between 1-opt moves and the change version of 2-opt moves (Strategy 3), or the exchange version of 2-opt moves (Strategy 4); iii) reversion search techniques that utilize 1-opt moves and the change version of 2-opt moves (Strategy 5) or the exchange version of 2-opt moves (Strategy 6). We found that the performances of all the enhanced search techniques of simulated annealing were systematic and often clear better than conventional search strategy, however the required computational time was significantly increased. For a minimization planning problem, Strategy 6 produced the lowest mean objective function values, which were less than 1% of the means developed using conventional search strategy. Although Strategy 6 performed very well, the other search strategies should not be neglected because they also have the potential to produce high-quality solutions.

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“…Earlier reviews by Baskent and Keles [18 •] and Shan et al [22] highlighted adjacency and green-up relationships in forest management planning, discussing limitations of MIP and heuristic parameter selection. However, the conceptual framework defined in these reviews has become outdated, motivating a reassessment to incorporate new approaches supporting spatial planning for multiple ecosystem services.…”

Section: Introductionmentioning

confidence: 99%

An Updated Review of Spatial Forest Planning: Approaches, Techniques, Challenges, and Future Directions

Baskent,

Borges,

Kašpar

2024

Curr. For. Rep.

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Purpose of Review The spatial forest planning concept has evolved as an essential component of the forest management planning process. The development of both exact and heuristic modeling techniques as analytical solution techniques have seen significant progress in application to spatial forest planning over the last two decades. This paper aims at providing a comprehensive review of the current state of spatial forest planning in both scope and depth, focusing on different approaches and techniques used, the challenges faced, and the potential future developments. For that purpose, we conduct a world-wide literature review and an extensive analysis of the status and trends over the past two decades in spatial forest planning. Recent Findings The literature review indicates that recent advancements have led to the development of new algorithms/formulations for addressing spatial constraints in forest planning with exact solution techniques. Nevertheless, it highlights further that heuristic techniques are still widely used, especially in large real-world problems that encompass multiple ecosystem services and constraints. Besides the provisioning services, there has been a noticeable increase in the proportion of regulating, supporting and cultural services addressed in objective functions of forest management planning models. Adjacency/green-up relationships, opening size, core area, wildlife habitat and the spatial arrangement of fuel treatments have been considered as indicators to address the provision of these services and spatial forest problem. Summary We pinpoint persistent challenges to using exact modeling techniques to address large real problems with multiple ecosystems services. We highlight further that determining the optimal combination and values of heuristic parameters and assessing the quality of heuristic solutions remains a central challenge. Finally, we highlight the potential of artificial intelligence to overcome computational obstacles to the application of both exact and heuristic techniques to spatially explicit forest management planning.

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Pitfalls and potential of particle swarm optimization for contemporary spatial forest planning

Cited by 6 publications

References 30 publications

Planejamento Espacial Da Colheita Considerando Áreas Máximas Operacionais

Planejamento Espacial Da Colheita Considerando Áreas Máximas Operacionais

Evaluating the neighborhood, hybrid and reversion search techniques of a simulated annealing algorithm in solving forest spatial harvest scheduling problems

An Updated Review of Spatial Forest Planning: Approaches, Techniques, Challenges, and Future Directions

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