ForestManagementPlanningbyComputationalModellingwithHeuristics

RamazanAkbulut1,2,PeteBettinger2,KevinBoston3,Zennure Ucar21GeneralDirectorateofForestry,SouthwestAnatoliaForestResearchInstitute,Antalya,TURKEY

2SchoolofForestryandNaturalResources,UniversityofGeorgia,Athens,GA3CollegeofForestry,OregonStateUniversity,Corvallis,OR

Background&IntroductionIn most cases, linear programming forest planning models can be solved very quickly;however, these models often lack spatial (e.g., adjacency) constraints. Toaccommodate adjacency constraints, the linear problem often becomes a mixed-integer linear programming model because binary variables are needed. Dependingon the size of the problem and the type of constraints used, the time required to solvethese problems may take minutes to days. Hence, it has been suggested that heuristicmethods could be used to solve spatial forest planning problems because the timerequired to solve the problems is usually considerably less than what is required formixed-integer programming methods such as branch and bound or cutting planeprocesses. In this study, heuristic methods will be used with a high-quality initialsolution acquired from a relaxed linear programming solution. Also, a randomlygenerated initial solution will be used to test the results with independent samples.

Objectives&HypothesesTo assess whether the heuristic solutions developed with a linear programming initialsolution are better in quality than the solutions developed when initiated from arandom starting point.

§ H1:Heuristic solutions initiated using relaxed linear programming initialsolutions are no better than heuristic solutions initiated with a random startingpoint.

§ H2:The computer processing time required for a heuristic that begins with high-quality relaxed linear solution is no different than the time required forrandomly initiated heuristic solutions.

§ H3:The method that is used to convert linear solutions to an integer startingpoint does not affect the final solution quality.

Methods

Study Area• 1841 hectares located in Oregon, US• Composed of 87 stands• Douglas-fir (Pseudotsuga menziesii)

Problem Selections• 6 time periods – each 5 years• Minimum harvest age is 30• Target harvest volume is 12,555 MBF*• Objective is to maximize an even flow of harvest

volume subject to adjacency, green-up, wood-flowminimum harvest age and ending inventory constraints.

Analysis & Algorithms1. TheAreaRestrictionModel(ARM)wasusedtomodeladjacencyconstraints.A

120acreclearcut sizelimithasbeensetfortheadjacentstandstobeconsistentwiththestatelawinOregon(StateofOregon,2015).

2. Linearprogrammingwasusedtosolveplanningproblemandthesolutionwasalsousedasastartingpointforheuristics.

3. AveragerunningtimeforMixed-IntegerProgramming(MIP)was24hours.4. ThresholdAcceptingandTabuSearchwereusedasheuristicmethods.

ParameterswereselectedfromBettingeretal.,(2015).5. 18scenarioswassetuptothatusinglinearprogrammingsolutionasstarting

point.6. Kruskal-WallisandMann-WhitneyUtestwasusedtotesttherelationship

betweenthescenarios.

Figure3:Scenariostoconvertlinearprogrammingsolution

Figure2:LinearProgrammingProblemFormulation

Results

Searchprocess ScenarioReversioninterval

(iterations)

Minimum(best)

solutionvalue(MBF2)

Maximum(worst)

solutionvalue(MBF2)

Averagesolution

value(MBF2)

Standard

deviationof

solutionvalues

(MBF2)

Averagetime

required(sec)

TS12 Random 6 0.03 17.37 1.57 2.76 362.23

TS12 1 6 0.07 68.42 4.15 7.99 333.90

TS12 2 6 0.03 53.93 4.38 7.77 351.81

TS12 3 6 0.11 17.57 2.39 3.32 339.56

TS12 4 6 0.03 11.29 1.30 2.36 340.41

TS12 5 6 0.04 24.24 2.04 3.83 348.79

TS12 6 6 0.07 18.49 1.45 2.84 352.94

TS12 7 6 0.06 25.72 2.67 4.46 358.13

TS12 8 6 0.04 12.08 1.72 2.44 339.35

TS12 9 6 0.05 16.33 1.64 2.81 340.74

TS12 10 6 0.05 16.83 1.43 2.60 341.31

TS12 11 6 0.02 25.28 1.46 3.30 341.13

TS12 12 6 0.04 16.72 1.57 2.92 335.03

TS12 13 6 0.08 67.34 3.57 7.56 337.63

TS12 14 6 0.04 23.85 2.65 3.69 352.52

TS12 15 6 0.04 66.65 4.22 9.33 334.20

TS12 16 6 0.05 19.18 1.93 3.60 351.95

TS12 17 6 0.02 19.02 1.72 3.07 339.59

TS12 18 6 0.04 19.23 1.77 2.89 340.79

Searchprocess ScenarioReversioninterval

(iterations)

Minimum(best)

solutionvalue

(MBF2)

Maximum(worst)

solutionvalue

(MBF2)

Averagesolution

value(MBF2)

Standard

deviationof

solutionvalues

(MBF2)

Averagetime

required(sec)

TA12 Random 3 22.19 2,997.67 608.21 547.80 612.30

TA12 1 3 6.87 7,070.66 671.68 842.39 688.21

TA12 2 3 21.53 4,664.50 508.13 611.78 699.81

TA12 3 3 7.84 3,012.48 571.72 576.24 662.86

TA12 4 3 13.38 1,551.75 406.80 331.25 611.54

TA12 5 3 16.60 3,493.69 581.16 716.88 674.82

TA12 6 3 19.09 3,901.22 444.41 522.41 600.97

TA12 7 3 21.15 3,345.78 663.60 580.64 552.00

TA12 8 3 29.90 3,159.32 624.80 689.39 632.12

TA12 9 3 15.90 6,374.19 681.22 823.85 651.95

TA12 10 3 31.91 4,118.63 474.80 528.29 603.94

TA12 11 3 24.84 2,730.28 420.70 475.17 650.93

TA12 12 3 9.01 4,887.68 577.37 787.05 664.14

TA12 13 3 58.22 2,816.72 549.75 540.47 631.68

TA12 14 3 20.87 2,266.43 554.69 472.37 640.51

TA12 15 3 13.40 1,855.45 502.05 430.19 687.76

TA12 16 3 15.42 2,047.25 455.83 452.07 568.35

TA12 17 3 19.29 4,692.67 481.04 610.73 646.85

TA12 18 3 26.81 5,605.16 526.64 689.36 641.58

Searchprocess&scenario Minimum(best)solutionvalue(MBF) Maximum(worst)solutionvalue(MBF) Averagesolutionvalue(MBF)

TSScenario4 75,329.92 75,325.43 75,328.49

TAScenario4 75,323.29 75,352.47 75,316.63

Figure4:Resultsof TabuSearchScenarios

Figure5:ResultsofThresholdAcceptingScenarios

Figure6:ComparisonofBestScenariosofEachAlgorithm

SearchAlgorithm Totalvolumescheduled(MBF)Requiredtime

(seconds)Bestsolutionvalue(MBF2)

Tabusearchmethod 75,328.49a 340.41a 1.42

Thresholdacceptingmethod 75,316.63a 611.54a 416.74

Mixedintegerprogramming 75,326.79b 86,400.00b 2.30

Figure7:ComparisonofHeuristicandMathematicalProgramming

ConclusionInitiating heuristics with high quality linear programming solution worked to improvethe solution quality of heuristics. However, there are some scenarios produced worsesolutions than the random heuristics even though they started with a initialparameters. Although the results indicate that tabu search can more reliably provide asolution that has the most even scheduled harvest volumes, on a practical level thedifference between average solutions generated by both methods amounts to aboutthree truckloads of wood, or about $6,000 US dollars (12 MBF x $500 per MBF).

AcknowledgementThis work was supported by the Turkish Ministry of National Education and theGeneral Directorate of Forestry under the direction of the Ministry of Forestry andWater Affairs. This work was also supported by the US Department of Agriculture,Natural Institute of Food and Agriculture, McIntire-Stennis Project 1012166,administrated by the University of Georgia.

ReferencesBettinger,P.,Demirci,M.,&Boston,K.(2015).Searchreversionwithins-metaheuristics:Impactsillustratedwithaforestplanningproblem. SilvaFennica,49,1-20.StateofOregon(2015).Whenisclearcuttingtherightchoice?http://oregonforests.org/content/clearcutting.RetrievedOctober25,2015.Akbulut,R.(2016).SpatialForestPlanDevelopmentUsingHeuristicProcessesthatareInitiatedwithaRelaxedLinearProgrammingSolution.MasterofSciencethesis.UniversityofGeorgia,Athens,GA.71p.