Effects of site productivity on forest harvest scheduling subject to green-up and maximum area restrictions

References

• ACT 2009-06-05 no 35: Lov om naturområder i Oslo og nærliggende kommuner (markaloven). Available from http://www.lovdata.no/cgi-wift/ldles?ltdoc=/all/nl-20090605-035.html#8 (Accessed 13.03.2015)
   Google Scholar
• Barrett TM, Gilless JK. 2000. Even-aged restrictions with sub-graph adjacency. Ann Oper Res. 95:159–175. doi: 10.1023/A:1018993822494
   Web of Science ®Google Scholar
• Bergseng E, Ask JA, Framstad E, Gobakken T, Solberg B, Hoen HF. 2012. Biodiversity protection and economics in long term boreal forest management – a detailed case for the valuation of protection measures. For Policy Econ. 15:12–21. doi: 10.1016/j.forpol.2011.11.002
   Web of Science ®Google Scholar
• Bergseng E, Eid T, Løken, Ø, Astrup R. 2013. Harvest residue potential in Norway – a bio-economic model appraisal. Scand J For Res. 28:470–480. doi: 10.1080/02827581.2013.766259
   Web of Science ®Google Scholar
• Bettinger P, Zhu J. 2006. A new heuristic method for solving spatially constrained forest planning problems based on mitigation of infeasibilities radiating outward from a forced choice. Silva Fenn. 40:315–333. doi: 10.14214/sf.477
   Web of Science ®Google Scholar
• Borges JG, Hoganson HM. 2000. Structuring a landscape by forestland classification and harvest scheduling spatial constraints. For Ecol Manage. 130:269–275. doi: 10.1016/S0378-1127(99)00180-2
   Web of Science ®Google Scholar
• Borges JG, Hoganson HM, Rose DW. 1999. Combining a decomposition strategy with dynamic programming to solve spatially constrained forest management scheduling problems. For Sci. 45:201–212.
   Google Scholar
• Borges P, Bergseng E, Eid T. 2014a. Adjacency constraints in forestry – a simulated annealing approach comparing different candidate solution generators. Math Comput For Nat Sci. 6:11–25.
   Google Scholar
• Borges P, Eid T, Bergseng E. 2014b. Applying simulated annealing using different methods for the neighborhood search in forest planning problems. Eur J Oper Res. 233:700–710. doi: 10.1016/j.ejor.2013.08.039
   Web of Science ®Google Scholar
• Boston K, Bettinger P. 2001a. Development of spatially feasible forest plans: a comparison of two modeling approaches. Silva Fenn. 35:425–435. doi: 10.14214/sf.578
   Web of Science ®Google Scholar
• Boston K, Bettinger P. 2001b. The economic impact of green-up constraints in the southeastern United States. For Ecol Manage. 145:191–202. doi: 10.1016/S0378-1127(00)00417-5
   Web of Science ®Google Scholar
• Boston K, Bettinger P. 2006. An economic and landscape evaluation of the green-up rules for California, Oregon, and Washington (USA). For Policy Econ. 8:251–266. doi: 10.1016/j.forpol.2004.06.006
   Web of Science ®Google Scholar
• Boston K, Sessions J, Rose R, Hoskins W. 2009. Incorporating regeneration effort as a decision variable in tactical harvest scheduling. West J Appl For. 24:61–66.
   Web of Science ®Google Scholar
• Braastad H. 1980. Tilvekstmodellprogram for furu [Growth model computer program for Pinus sylvestris]. Norwegian Forest Research Institute. Report. 35:272–359. (In Norwegian with English summary)
   Google Scholar
• Brumelle S, Granot D, Halme M, Vertinsky I. 1998. A tabu search algorithm for finding good forest harvest schedules satisfying green-up constraints. Eur J Oper Res. 106:408–424. doi: 10.1016/S0377-2217(97)00282-8
   Web of Science ®Google Scholar
• Constantino M, Martins I, Borges JG. 2008. A new mixed-integer programming model for harvest scheduling subject to maximum area restrictions. Oper Res. 56:542–551. doi: 10.1287/opre.1070.0472
   Web of Science ®Google Scholar
• Crowe KA, Nelson JD. 2003. An indirect search algorithm for harvest-scheduling under adjacency constraints. For Sci. 49:1–11.
   Google Scholar
• Crowe KA, Nelson JD. 2005. An evaluation of the simulated annealing algorithm for solving the area-restricted harvest-scheduling model against optimal benchmarks. Can J For Res. 35:2500–2509. doi: 10.1139/x05-139
   Web of Science ®Google Scholar
• Crowe KA, Nelson JD, Boyland M. 2003. Solving the area-restricted harvest-scheduling model using the branch and bound algorithm. Can J For Res. 33:1804–1814. doi: 10.1139/x03-101
   Web of Science ®Google Scholar
• Dahlin B, Sallnäs O. 1993. Harvest scheduling under adjacency constraints: a case study from the Swedish sub-alpine region. Scand J For Res. 8:281–290. doi: 10.1080/02827589309382777
   Web of Science ®Google Scholar
• Daust D, Nelson JD. 1993. Spatial reduction factors for strata-based harvest schedules. For Sci. 39:152–165.
   Google Scholar
• Eid T, Hobbelstad K. 2000. AVVIRK-2000 – a large scale forestry scenario model for long-term investment, income and harvest analyses. Scand J For Res. 15:472–482. doi: 10.1080/028275800750172736
   Web of Science ®Google Scholar
• Eid T, Hoen HF, Økseter P. 2001. Economic consequences of sustainable forest management regimes at non-industrial forest owner level in Norway. For Policy Econ. 2:213–228. doi: 10.1016/S1389-9341(00)00038-1
   Web of Science ®Google Scholar
• Eid T, Hoen HF, Økseter P. 2002. Timber production possibilities of the Norwegian forest area and measures for a sustainable forestry. For Policy Econ. 4:187–200. doi: 10.1016/S1389-9341(01)00069-7
   Web of Science ®Google Scholar
• Falcão AO, Borges JG. 2002. Combining random and systematic search heuristic procedures for solving spatially constrained forest management scheduling models. For Sci. 48:608–621.
   Google Scholar
• Gobakken T. 2003. Brukerveiledning til SGIS – et skoglig geografisk informasjonssystem. [User Guide to SGIS – a forest geographic information]. Version 2.1. 26 p. Unpublished user manual. (In Norwegian.).
   Google Scholar
• Gobakken T, Lexerød N, Eid T. 2008. T – a forest simulator for bioeconomic analyses based on models for individual trees. Scand J For Res. 23:250–265. doi: 10.1080/02827580802050722
   Web of Science ®Google Scholar
• Goycoolea M, Murray AT, Barahona F, Epstein R, Weintraub A. 2005. Harvest scheduling subject to maximum area restrictions: exploring exact approaches. Oper Res. 53:490–500. doi: 10.1287/opre.1040.0169
   Web of Science ®Google Scholar
• Goycoolea M, Murray AT, Vielma JP, Weintraub A. 2009. Evaluating approaches for solving the area restriction model in harvest scheduling. For Sci. 55:149–165.
   Google Scholar
• Gunn EA, Richards EW. 2005. Solving the adjacency problem with stand-centred constraints. Can J For Res. 35:832–842. doi: 10.1139/x05-013
   Web of Science ®Google Scholar
• Hasle G, Haavardtun J, Kloster O, Løkketangen A. 2000. Interactive planning for sustainable forest management. Ann Oper Res. 95:19–40. doi: 10.1023/A:1018997923403
   Web of Science ®Google Scholar
• Hoen HF, Eid T. 1990. A model for analysis of treatment strategies for a forest applying standvice simulations and linear programming. Research paper of Norwegian Forest Research Institute. Ås, Akershus. 9/90:1–35. (In Norwegian with English summary)
   Google Scholar
• Hoen HF, Eid T, Veisten K, Økseter P. 1998. Økonomiske konsekvenser av tiltak for et bærekraftig skogbruk. Forutsetninger og metodebeskrivelse. [Economical consequences of measures for a sustainable forestry. Assumptions and methods.] Research paper of Skogforsk, Ås, Supplement, 6/98:1–48. (In Norwegian)
   Google Scholar
• ILOG. 2013. ILOG CPLEX 12.5 – user's Manual [Internet] [cited 2015 July 20]. Available from: http://www-01.ibm.com/support/knowledgecenter/SSSA5P_12.5.1/maps/ic-homepage.html?lang=en
   Google Scholar
• Malchow-Møller N, Strange N, Thorsen BJ. 2004. Real-options aspects of adjacency constraints. For Policy Econ. 6:261–270. doi: 10.1016/j.forpol.2004.03.002
   Web of Science ®Google Scholar
• Martins I, Alvelos F, Constantino M. 2012. A branch-and-price approach for harvest scheduling subject to maximum area restrictions. Comput Optim Appl. 51:363–385. doi: 10.1007/s10589-010-9347-1
   Web of Science ®Google Scholar
• Martins I, Constantino M, Borges JG. 2005. A column generation approach for solving a non-temporal forest harvest model with spatial structure constraints. Eur J Oper Res. 161:478–498. doi: 10.1016/j.ejor.2003.07.021
   Web of Science ®Google Scholar
• Martins I, Ye M, Constantino M, Fonseca MC, Cadima J. 2014. Modeling target volume flow in forest harvest scheduling subject to maximum area restrictions. TOP. 22:343–362. doi: 10.1007/s11750-012-0260-x
   Web of Science ®Google Scholar
• McDill ME, Braze J. 2000. Comparing adjacency constraint formulations for randomly generated forest planning problems with four age-class distributions. For Sci. 46:423–436.
   Google Scholar
• McDill ME, Rebain SA, Braze J. 2002. Harvest scheduling with area-based adjacency constraints. For Sci. 48:631–642.
   Google Scholar
• McIntosh B. 1999. The UK experience in forest design planning. In: McLaren J, editor. Issues in global timber supplies. San Francisco, CA: Miller Freeman. p. 221–225.
   Google Scholar
• Murray AT. 1999. Spatial restrictions in harvest scheduling. For Sci. 45:45–52.
   Google Scholar
• Murray AT, Goycoolea M, Weintraub A. 2004. Incorporating average and maximum area restrictions in harvest scheduling models. Can J For Res. 34:456–464. doi: 10.1139/x03-217
   Web of Science ®Google Scholar
• Neto T, Constantino M, Martins I, Pedroso JP. 2013. A branch-and-bound procedure for forest harvest scheduling problems addressing aspects of habitat availability. Int T Oper Res. 20:689–709. doi: 10.1111/itor.12003
   Web of Science ®Google Scholar
• Öhman K, Eriksson LO. 1998. The core area concept in forming contiguous areas for long-term forest planning. Can J For Res. 28:1032–1039. doi: 10.1139/x98-076
   Web of Science ®Google Scholar
• Öhman K. 2000. Creating continuous areas of old forest in long-term forest planning. Can J For Res. 30:1817–1823. doi: 10.1139/x00-103
   Web of Science ®Google Scholar
• Öhman K, Eriksson LO. 2010. Aggregating harvest activities in long term forest planning by minimizing harvest area perimeters. Silva Fenn. 44:77–89. doi: 10.14214/sf.457
   Web of Science ®Google Scholar
• Rebain SA, McDill ME. 2003. Can mature patch constraints mitigate the fragmenting effects of harvest opening size restrictions? Int Trans Oper Res. 10:499–513. doi: 10.1111/1475-3995.00424
   Google Scholar
• Richards EW, Gunn EA. 2000. A model and tabu search method to optimize stand harvest and road construction schedules. For Sci. 46:188–203.
   Google Scholar
• Shan Y, Bettinger P, Cieszewski CJ, Li RT. 2009. Trends in spatial forest planning. Math Comput For Nat Sci. 1:86–112.
   Google Scholar
• Sharma R, Brunner A, Eid T, Øyen B-H. 2011. Modelling dominant height growth from national forest inventory individual tree data with short time series and large age errors. For Ecol Manage. 262:2162–2175. doi: 10.1016/j.foreco.2011.07.037
   Web of Science ®Google Scholar
• Stølevik M, Hasle G, Kloster O. 2007. Solving the long-term forest treatment scheduling problem. In: Hasle G, Lie KA, Quak E, editors. Geometric modelling, numerical simulation, and optimization. Berlin: Springer; p. 437–473.
   Google Scholar
• Strand L. 1967. Height curves for birch. p. 291–296 in Braastad, H. (ed.) Yield tables for birch. Norwegian Forest Research Institute, Report 22:256–365. (In Norwegian with English summary)
   Google Scholar
• Thompson EF, Halterman BG, Lyon TJ, Miller RL. 1973. Integrating timber and wildlife management planning. For. Chron. 49:247–250. doi: 10.5558/tfc49247-6
   Google Scholar
• Tomter SM, Hylen G, Nilsen J-E. 2010. Development of Norway's national forest inventory. In: Tomppo E, Gschwantner T, Lawrence M, McRoberts RE, editors. National forest inventories – pathways for common reporting. London: Springer, 411–424.
   Google Scholar
• Tóth SF, McDill ME. 2007. Promoting large, compact mature forest patches in harvest scheduling models. Environ Model Assess. 13:1–15. doi: 10.1007/s10666-006-9080-4
   Web of Science ®Google Scholar
• Tóth SF, McDill ME. 2009. Finding efficient harvest schedules under three conflicting objectives. For Sci. 55:117–131.
   Google Scholar
• Tóth SF, McDill ME, Könnyü N, George S. 2013. Testing the use of lazy constraints in solving area-based adjacency formulations of harvest scheduling models. For Sci. 59:157–176.
   Google Scholar
• Tveite B. 1977. Site index curves for Norway spruce (Picea Abies (L.) Karst). Norwegian forest research institute. Report. 33:1–84. (In Norwegian with English summary) doi: 10.1016/0370-1573(77)90060-6
   Web of Science ®Google Scholar
• Vielma JP, Murray AT, Ryan DM, Weintraub A. 2007. Improving computational capabilities for addressing volume constraints in forest harvest scheduling problems. Eur J Oper Res. 176:1246–1264. doi: 10.1016/j.ejor.2005.09.016
   Web of Science ®Google Scholar
• Wei Y, Hoganson HM. 2006. Spatial information for scheduling core area production in forest planning. Can J For Res. 36:23–33. doi: 10.1139/x05-233
   Web of Science ®Google Scholar
• Wei Y, Hoganson HM. 2008. Tests of a dynamic programming-based heuristic for scheduling forest core area production over large landscapes. For Sci. 54:367–380.
   Google Scholar
• Wikström P, Edenius L, Elfving B, Eriksson LO, Lämås T, Sonesson J, Öhman K, Wallerman J, Waller C, Klintebäck F. 2011. The Heureka forestry decision support system: An overview. Math Comput For Nat Sci. 3:87–94.
   Google Scholar
• Zhu J, Bettinger P. 2008. Estimating the effects of adjacency and green-up constraints on landowners of different sizes and spatial arrangements located in the southeastern U.S. For Policy Econ. 10:295–302. doi: 10.1016/j.forpol.2007.11.006
   Web of Science ®Google Scholar