Optimisation algorithms for spatially constrained forest planning

Liu, Guoliang; Han, Shijie; Zhao, Xiuhai; Nelson, John D.; Wang, Hongshu; Wang, Weiying

doi:10.1016/j.ecolmodel.2005.10.028

Cited by 38 publications

(36 citation statements)

References 13 publications

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“…Within the field of forestry, s-metaheuristic techniques have been widely used in forest planning problems, perhaps because they involve more intuitive processes than p-metaheuristics techniques. More importantly, some studies have suggested that p-metaheuristics techniques may be less effective in solving complex forest spatial planning problems when the harvest adjacency restrictions are integrated Liu et al 2006), but this conclusion highly depends on the actual implementation of the heuristic methods, and how solutions are modified to allow the population to evolve. For example, Bettinger et al (2002) and Liu et al (2006) reported that genetic algorithms were not appropriate when applied to a strict harvest adjacency problem due to the large number of constraint violations incurred, but when limited genetic information is passed from parents to child solutions, few constraint violations can occur.…”

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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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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“…The SA technique has been previously applied to forest harvest planning (Pukkala & Kurttila 2005, Liu et al 2006, Quintero et al 2010, and has proven to achieve solutions very close to a mathematical optimum. The SA algorithm uses random initial solutions and a geometric cooling schedule.…”

Section: Optimization Techniques and Implementationmentioning

confidence: 99%

“…The large number of variables and constraints involved, the existence of non-linear relationships between variables, and the need to simultaneously optimize for different objectives often make the optimization of models an arduous task. In this case, heuristic techniques can be applied as they can handle the model complexity more efficiently (Bettinger & Chung 2004, Pukkala & Kurttila 2005, Liu et al 2006. Among these techniques, Simulated Annealing, Genetic Algorithms, Tabu Search, Threshold Accepting, and Ant Colony Algorithms were successfully applied in forest planning (Pukkala & Kurttila 2005, Liu et al 2006, Zeng et al 2007, Zhu & Bettinger 2008, Quintero et al 2011.…”

Section: Introductionmentioning

confidence: 99%

Heuristic forest planning model for optimizing timber production and carbon sequestration in teak plantations

Quintero-Méndez¹,

Jerez-Rico²

2017

iForest

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We developed a forest planning model integrating two operational scales (single-stand and forest levels) for the optimization of timber production and carbon sequestration in forest teak (Tectona grandis L. f.) plantations. At the stand level, growth and yield simulations using a heuristic thinning optimizer provided a set of near-optimal thinning regimes for individual stands differing on initial spacing and site quality, given biological, silvicultural, and financial constraints. The set of near-optimal thinning regimes obtained were then used as input of the forest-level model, which generated optimal harvest plans for the whole plantation by simultaneously maximizing the net present value of merchantable wood and carbon sequestration. The net amount of carbon captured by the biomass and the emissions produced by decomposition of woody debris and timber products after harvest were estimated. The growth and yield model was based on a system of differential equations incorporating heuristics (genetic algorithms) to optimize age and intensity of thinnings. The full model can handle the optimization of harvest schedules for projects up to 10,000 ha and 200 stands and was tested on a validation dataset including teak plantations from Venezuela and other Latin American countries. Results indicated that regimes favoring carbon sequestration reduce the benefits of timber production, and equal profitability of carbon sequestration and timber production was obtained for carbon prices over 40 $US Gg -1 . Sensitivity analysis showed that the proposed model is sensible to variation in growth rates, carbon and timber prices, and production quotas, and barely sensible to harvest and transport costs. The developed model has a modular structure that allows its calibration to incorporate data from a wide range of management regimes for teak and other forest species.

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“…Harvesting planning aims to long-term tasks scheduling in order to maximize the cut volume and the profit respecting the imposed restrictions (Liu et al, 2006). One of the restrictions of this problem is that some jurisdictions prohibit clearcuts, i.e., large areas cut (Brumelle et al, 1998;Gunn & Richards, 2005).…”

Section: Forest-based Supply Chainmentioning

confidence: 99%

Overview of MPC applications in supply chains: Potential use and benefits in the management of forest-based supply chains

et al. 2015

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Aim of study: This work aims to provide an overview of Model Predictive Controllers (MPC) applications in supply chains, to describe the forest-based supply chain and to analyse the potential use and benefits of MPC in a case study concerning a biomass supply chain.Area of study: The proposed methods are being applied to a company located in Finland.Material and methods: Supply chains are complex systems where actions and partners’ coordination influence the whole system performance. The increase of competitiveness and need of quick responses to the costumers implies the use of efficient management techniques. The control theory, particularly MPC, has been successfully used as a supply chain management tool. MPC is able to deal with dynamic interactions between the partners and to globally optimize the supply chain performance in the presence of disturbances. However, as far as is authors’ knowledge, there are no applications of this methodology in the forest-based supply chains. This work proposes a control architecture to improve the performance of the forest supply chain. The controller is based on prediction models which are able to simulate the system and deal with disturbances.Main results: The preliminary results enable to evaluate the impacts of disturbances in the supply chain. Thus, it is possible to react beforehand, controlling the schedules and tasks’ allocation, or alert the planning level in order to generate a new plan.Research highlights: Overview of MPC applications in supply chains; forest-based supply chain description; case study presentation: wood biomass supply chain for energy production; MPC architecture proposal to decrease the operation times.Keywords: biomass; forest; Model Predictive Control; planning; supply chain.

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Optimisation algorithms for spatially constrained forest planning

Cited by 38 publications

References 13 publications

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

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

Heuristic forest planning model for optimizing timber production and carbon sequestration in teak plantations

Overview of MPC applications in supply chains: Potential use and benefits in the management of forest-based supply chains

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