Pete Bettinger, Mehmet Demirci, Kevin Boston. (2015). Search reversion within s-metaheuristics: impacts illustrated with a forest planning problem. Silva Fennica vol. 49 no. 2 article id 1232. https://doi.org/10.14214/sf.1232

Keywords: forest planning; heuristics; threshold accepting; tabu search; spatial harvest scheduling; adjacency constraints; mixed integer goal programming

Highlights: The interruption of the sequence of events used to explore a solution space and develop a forest plan, and the re-initiation of the search process from a high-quality, known starting point (reversion) seems necessary for some s-metaheuristics; When using a s-metaheuristic, higher quality forest plans may be developed when the reversion interval is around six iterations of the model.

Abstract | Full text in HTML | Full text in PDF | Author Info

The use of a reversion technique during the search process of s-metaheuristics has received little attention with respect to forest management and planning problems. Reversion involves the interruption of the sequence of events that are used to explore the solution space and the re-initiation of the search process from a high-quality, known starting point. We explored four reversion rates when applied to three different types of s-metaheuristics that have previously shown promise for the forest planning problem explored, threshold accepting, tabu search, and the raindrop method. For two of the s-metaheuristics, we also explored three types of decision choices, a change to the harvest timing of a single management unit (1-opt move), the swapping of two management unit’s harvest timing (2-opt moves), and the swapping of three management unit’s harvest timing (3-opt moves). One hundred independent forest plans were developed for each of the metaheuristic / reversion rate combinations, all beginning with randomly-generated feasible starting solutions. We found that (a) reversion does improve the quality of the solutions generated, and (b) the rate of reversion is an important factor that can affect solution quality.

Bettinger, School of Forestry and Natural Resources, 180 E. Green Street, University of Georgia, Athens, Georgia, USA 30602 E-mail: pbettinger@warnell.uga.edu
Demirci, General Directorate of Forestry, Ministry of Forest and Water Affairs, Republic of Turkey E-mail: mehmetdemirci@yahoo.com
Boston, Department of Forest Engineering, Resources and Management, College of Forestry, Oregon State University, USA E-mail: Kevin.Boston@oregonstate.edu

article id 474, category Research article

Tero Heinonen, Mikko Kurttila, Timo Pukkala. (2007). Possibilities to aggregate raster cells through spatial optimization in forest planning. Silva Fennica vol. 41 no. 1 article id 474. https://doi.org/10.14214/sf.474

Keywords: threshold accepting; spatial optimisation; landscape metrics; old forest

Abstract | View details | Full text in PDF | Author Info

Heinonen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tero.heinonen@joensuu.fi
Kurttila, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: mk@nn.fi
Pukkala, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tp@nn.fi

article id 357, category Research article

Elizabeth Dodson Coulter, John Sessions, Michael G. Wing. (2006). Scheduling forest road maintenance using the analytic hierarchy process and heuristics. Silva Fennica vol. 40 no. 1 article id 357. https://doi.org/10.14214/sf.357

Keywords: decision support; simulated annealing; threshold accepting; road environmental impacts; AHP

Abstract | View details | Full text in PDF | Author Info

The management of low-volume roads has transitioned from focusing on maintenance designed to protect a capital investment in road infrastructure to also include environmental effects. In this study, two models using mathematical programming are applied to schedule forest road maintenance and upgrade activities involving non-monetary benefits. Model I uses a linear objective function formulation that maximizes benefit subject to budgetary constraints. Model II uses a non-linear objective function to maximize the sum of benefits divided by the sum of all costs in a period. Because of the non-linearity of the constraints and the requirements that the decision variables be binary, the solutions to both problem formulations are found using two heuristics, simulated annealing and threshold accepting. Simulated annealing was found to produce superior solutions as compared to threshold accepting. The potential benefit for completing a given road maintenance or upgrade project is determined using the Analytic Hierarchy Process (AHP), a multi-criterion decision analysis technique. This measure of benefit is combined with the economic cost of completing a given project to schedule maintenance and upgrade activities for 225 km (140 miles) of road in forested road systems within western Oregon. This combination of heuristics, cost-benefit analysis, environmental impacts, and expert judgment produces a road management schedule that better fits the current road management paradigm.

Coulter, College of Forestry and Conservation, University of Montana, 32 Campus Drive, Missoula, MT 59812, USA E-mail: elizabeth.coulter@cfc.umt.edu
Sessions, Department of Forest Engineering, College of Forestry, Oregon State University, 204 Peavy Hall, Corvallis, OR 97331-5706, USA E-mail: js@nn.us
Wing, Department of Forest Engineering, College of Forestry, Oregon State University, 204 Peavy Hall, Corvallis, OR 97331-5706, USA E-mail: mgw@nn.us

Click this link to register to Silva Fennica.

If you are a registered user, log in to save your selected articles for later access.

Contents alert

Your selected articles

Follow @SilvaFennica