Refocusing on Operational Harvest Planning Model for State-Owned Forestry in Turkey

Yıl 2020, Cilt: 6 Sayı: 2, 96 - 106, 30.12.2020

Mehmet Eker John Sessions

https://doi.org/10.33904/ejfe.829946

Cited By: 2

Öz

The growing concerns on forest ecosystem services and sustainable management of the resources with workforce, material, and products require effective planning of forestry operations in a hierarchical level. Operational planning as a component of the hierarchy generates short-term harvest planning decisions to minimize total costs by making production and distribution decisions during all seasons. Operational harvest planning of wood harvesting has been not used in Turkish conditions. Many developments and changes in managerial and operational processes in Turkish state forestry require the right product in the right place at the right time. This indicates that it is time to use operational planning to solve the wood harvesting problem with respect to specific conditions of Turkish forestry. This study introduces a model for annual planning of harvest operations/operational harvest planning (OHARP) from stand to storage for a one-year time horizon. The article presents how the operational decisions can be optimized for selection of the most appropriate harvesting blocks, time, system, landing location, and transportation mode to provide the best balance between time and cost. The mathematical model of the planning problem was formulated with linear and mixed integer programming techniques. The data for the model comes from the forest planning units and operation systems which is combined to minimize total supply costs subject to technical, environmental and socio-economic constraints. The model was tested with the real harvesting data from a forest district in the Mediterranean Region for a one year planning horizon. The test results demonstrated that when the OHARP model was implemented in the test area and compared with the actual cost of the harvest operations realized in this area, a savings of at least 4% could be achieved by better matching appropriate harvesting systems and methods to the terrain using the OHARP methodology. When operational decisions including resource constraints were optimized, up to a 30% cost reduction could be achieved in terms of average harvesting and transportation cost.

Anahtar Kelimeler

operational planning, Annual harvest planning, Wood harvesting, Optimization, AHP

Kaynakça

• Acar, H.H., 1994. Developing Forest Transportation Plans in Mountainous Terrain, Karadeniz Technical University, PhD Thesis, 150 p., Trabzon.
• Akay, A.E., Özkan, D., Bilici, E., 2016. Assessing productivity and work safety of a mechanized logging operation. The International Forestry Symposium (IFS 2016). 7-11 December, Kastamonu, Turkey.
• Bayoğlu, S., 1972. Forest Transportation and Developing Possibilities in Turkey, İ.Ü. Publication of Forestry Faculty No: 1747/185, İstanbul, 73 p.
• Beaudoin, D., Frayret, J.M., LeBel, L., 2008. Hierachical forest management with anticipation: an application to tactical-operational planning integration. Canadian Journal of Forest Research 38 (8):2198-2211.
• Borges, P., Eid, T., Bergseng, E., 2014. Applying simulated annealing using different methods for the neighborhood search in forest planning problems. Eur. J. Oper. Res. 233: 700–710.
• Boyland, M., 2003. Hierarchical planning in forestry. ATLAS SIMFOR Project Technical Report. Department of Forest Science, The University of British Columbia, Vancouver, BC.
• Chung, W., Sessions, J., 2000. NETWORK 2000: A Program for Optimizing Large Fixed and Variable Cost Transportation Systems, Proc. of the Eighth Symposium on Systems Analysis in Forest Resources, Arthaud, G.J. (ed.). Sept 28-30, Aspen, Colorado.
• Çoban, H.O., 2011. Calculation of average skidding distance on sloping terrain with GIS tools. Turkish Journal of Forestry 12(2):98-103.
• Dong, L., Bettinger, P., Qin, H., Liu, Z., 2018. Reflections on the number of independent solutions for forest spatial harvest scheduling problems: A case of simulated annealing. Silva Fenn. 52: 7803.
• Dykstra, D.P, 1976. Timber Harvest Layout by Mathematical and Heuristic Programming, PhD Thesis, Oregon State University, 299 p.
• Dykstra, D.P., Heinrich, R., 1996. FAO Model Code of Forest Harvesting Practice, FAO Publications, Rome, 85 s.
• Eker, M., 2004. Development of Annual Operational Planning Model for Timber Harvesting. PhD Thesis, Karadeniz Technical University, Trabzon, Turkey. 239 p.
• Eker, M.; Acar, H.H., 2006. Developing an annual harvest operation planning model for Turkish state forestry, Council on Forest Engineering Proceedings The 29th Annual Meeting, July 30-August 2, 363-374 pp., Coeur d'Alene-Idaho, USA.
• Eker, M., 2020. A Review on Decision Processes for Wood Harvesting in Turkish Forestry. Eur J Forest Eng, 6(1): 41-51
• Engür, O.M., 1996. Technology Selection on Forest harvesting and mechanization Possibilities, İstanbul University, PhD Thesis, İstanbul, 216 p.
• Epstein, R., Morales, P., Seron, J., Weintraub, A., 1999. Use of OR Systems in the Chilean Forest Industries, Interfaces 29(1): 7-28.
• Epstein, R., Weintraub, A., Sapunar, P., Nieto, E., Sessions, J.B., Sessions, J., Bustamante, F., Musante, H., 2006. A Combinatorial Heuristic Approach for Solving Real-Size Machinery Location and Road Design Problems in Forestry Planning. Operations Research 54(6):1017-1027.
• Erdaş, O., 1997. Forest Roads –I, Publication of KTÜ Forestry Faculty, No:187/25, Trabzon, 390 p.
• FS,2020. Forestry Statistics, Official Statistics, General Directorate of Forestry. https://www.ogm.gov.tr/ekut uphane/Sayfalar/Istatistikler.Accessed: 25 September 2020.
• GDF, 2020. GDF, 2020. Official Communique about Harvesting of Wood-based Forest Products – No:310. General Directorate of Forestry, Ankara, Turkey. https://www.ogm.gov.tr/ekutuphane/Tebligler Accessed: 13 February 2020.
• Gunn, A.E., 1991. Some Aspects of Hierarchical Production Planning in Forest Management, Proceedings of the 1991 Symposium on Systems Analysis in Forest Resources, Ed. By Buford, M.A., March 3-6 Charleston, p.53-62
• Jin, X., Pukkala, T., Li, F., 2016. Fine-tuning heuristic methods for combinatorial optimization in forest planning. Eur. J. For. Res. 135: 765–779.
• Karlsson, J., Rönnqvist, M. ve Bergström, J., 2002. Annual Harvest Planning, Department of Mathematics, Linköping Inst. of Technology, LiTH-MAT-R–2002-15, Sweden.
• Karlsson, J., Rönnqvist, M., Bergström, J., 2004. An optimization model for annual harvest planning. Canadian Journal of Forest Research, 8: 1747–1754.
• Lan, Z., 2001. A Cost Model for Machine Operation in Wood Cutting and Extraction, ECOWOOD-Activities at the University of Helsiki Partner Number 4, 2001-03-22, Finland
• Laroze A., Greber B., 1991. Multi-Level Harvest Plannıng and Log Merchandising Using Goal-Programming, Proceedings of the 1991 Symposium on Systems Analysis in Forest, Resources, Ed. By Buford, M.A., March 3-6, 199, Charleston, South Carolina p.24-30.
• Martell, D.E., Gunn, E., Weintraub, A. 1998. Forest Management Challenges for Operational Researchers, European Operational Research, 104(1): 1-17
• Martin, A.J., Sendak, P.E., 1973. Operations Research in Forestry: A Bibliography, USDA Forest Service General Technical Report NE-8. N. Forest Experiment Station Forest Service, 94 p.
• Nieuwenhuis, M., 1989. Operations Research in Forestry. Irish Forestry, 46(1): 51-58
• Oborn, R.M.R.,1996. A Mixed-Integer Programming Model for Tactical Forest Operations Planning, Proceedings of the Meeting on Planning and Implementing Forest Operations to Achieve Sustainable Forests, Ed. by C.R. Blinn, M.A. Thompson, COFE - 19th Annual Meeting and IUFRO, July 29 – August 1., USA, pp.201-212.
• Ünver Okan,S., Acar, H. H., 2009. A Damage Prediction Model for Quantity Loss on Skidded Spruce Logs during Ground Base Skidding in North Eastern Turkey. Croatian Journal of Forest Engineering, 30: 59-65.
• Reimer, D.R., 1979. An Operational Computer Assisted Forest Engineering System, Proceedings of Symposium on Mountain Logging, Ed. by W. Carson, J. Jorgensen, H. Lysons, IUFRO, 10-14 September, University of Washington.
• Robak, E.W., 1984. Toward a Microcomputer-Based DSS for Planning Forest Operations, Interfaces, 14(5): 105-111.
• Rönnqvist; M., Westerlund, A., Carlsson, D., 1999. Extraction of Logs in Forestry Using Operations Research and Geographical Information Systems, Proceedings of the 32. Hawaii International Conference on System Sciences, Hawaii.
• Rönnqvist, M., D’Amours, S., Weintraub, A., Jofre, A., Gunn, E., Haight, R.G., Martell, D., Murray, A.T., Romero, C., 2015. Operations research challenges in forestry: 33 open problems. Ann. Oper. Res. 232: 11-40.
• Saaty, T., 1989. Multicriteria Decision Making: The Analytic Hierarchy Process, RWS Publication, Pittsburg, 219 s.
• Samset, I., 1979. The Accessibility of Forest Resources, Proceedings of Symposium on Mountain Logging, Ed. by W. Carson, J. Jorgensen, H. Lysons, IUFRO, 10-14 September, University of Washington.
• Schuster, E.G., Leafers, L.A., Thompson, J.E., 1993. A Guide to Computer Based Analytical Tolls for Implementing National Forest Plans, USDA Forest Service, Intermountain Research Station General