Optimization in the distribution and sequencing of machines in wood harvesting in planted forests
Abstract
Aim of study: The objective was to evaluate an optimized model for the distribution and sequencing of machines in tree felling and processing and extraction operations, aiming to reduce unproductive times and gains in production capacity.
Area of study: The study was conducted in a cellulose and paper company in the state of Parana, Brazil, in operational areas of the wood harvesting.
Material and methods: The times of tree felling, processing and extraction were obtained from a harvester (hv) and a forwarder (fw) to estimate the operational efficiency and productivity of the system. At the same time, vector features equivalent to the set of plots in the area, roads and a matrix feature of the digital terrain model were obtained from a geographic database for estimation of a matrix of distances between the plots. The Ward method of hierarchical grouping was used based on the matrix of distances between the fields to form two work groups (GI and GII). For each working group, the shortest walking sequence was estimated using the Kruskal algorithm.
Main results: An optimized distribution of 3 hw and 2 fw was necessary in GI to achieve the production target of 200 m³ PMH-1, with the machines covering a distance of 2,335 m. The proportion of machines 3 hw and 3 fw was needed in GII with a distance covered of 2,878 m. The proposed model optimized the number of machines and provided a 26.7% reduction in the total displacement of machines.
Research highlights: The high cost of wood harvesting operations requires the use optimized of the forestry machines. The proposed model made it determine with efficiency the machines distribution and sequencing for a wood harvesting optimized. The model can be implemented in the operational planning in the forestry companies in more complex wood harvesting scenarios.
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References
Alvares CA, Stape JL, Sentelhas PC, Gonçalves JDM, Sparovek G, 2013. Köppen’s climate classification map for Brazil. Meteorol Z 22(6): 711–728. https://doi.org/10.1127/0941-2948/2013/0507
Augustynczik ALD, Arce JE, Yousefpour R, Da Silva ACL, 2016. Promoting harvesting stands connectivity and its economic implications in Brazilian forest plantations applying integer linear programming and simulated annealing. For Policy Econ 73: 120–129. https://doi.org/10.1016/j.forpol.2016.09.007
Demirci M, Bettinger P, 2015. Using mixed integer multi-objective goal programming for stand tending block designation: A case study from Turkey. For Policy Econ 55: 28–36. https://doi.org/10.1016/j.forpol.2015.03.007
Esri, 2018. Feature to Point. Environmental Systems Research Institute. https://doc.arcgis.com/en/archive/ [10 November 2024].
Ferrari LS, Arce JE, Pelissari AL, 2019. Optimal harvest scheduling in Pinus spp. plantations for tactical planning. Sci For 47(121): 167–176. https://dx.doi.org/10.18671/scifor.v47n121.17
Ferrari LS, Arce JE, Pelissari AL, Da Silva JP, Figueiredo Filho A, Oliveira EB, 2020. Tactical planning of forest harvesting under different scheduling restrictions. Sci For 48(127): e3334. https://doi.org/10.18671/scifor.v48n127.12
França LCJ, Júnior FWA, Silva CSJ, Monti CAU, Ferreira TC, Santana CJO, Gomide LR, 2022. Forest landscape planning and management: A State-of-the-Art Review. Trees For People 8: e100275. https://doi.org/10.1016/j.tfp.2022.100275
Frisk M, Flisberg P, Rönnqvist M, Andersson G, 2016. Detailed scheduling of harvest teams and robust use of harvest and transportation resources. Scand J For Res 31(7): 681–690. https://doi.org/10.1080/02827581.2016.1206144
Gomide LR, Arce JE, Da Silva AL, 2010. Efeito das restrições espaciais de adjacência no planejamento florestal otimizado. Floresta 40(3): 573–584. https://doi.org/10.5380/rf.v40i3.18919
Leal RO, López CA, 2011. Metodología para la planeación logística de vías forestales para la cosecha de plantaciones de Eucalyptus globulus Labill. utilizando herramientas de optimización. Colomb For 14(1): 51–67. https://doi.org/10.14483/udistrital.jour.colomb.for.2011.1.a05
Likaj R, Shala A, Mehmetaj M, Hyseni P, Bajrami X, 2013. Application of graph theory to find optimal paths for the transportation problem. IFAC Proc Vol 46(8): 235–240. https://doi.org/10.3182/20130606-3-XK-4037.00031
Machado CC, Lopes ES, 2014. Planejamento. In: Colheita Florestal, 3rd ed; Machado CC (ed). pp: 543. UFV, Viçosa, Brazil.
McEwan A, Marchi E, Spinelli R, Brink M, 2020. Past, present and future of industrial plantation forestry and implication on future timber harvesting technology. J For Res 31(2): 339–351. https://doi.org/10.1007/s11676-019-01019-3
R Core Team, 2019. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org
Santos HG, Jacomine PKT, Anjos LHC, Oliveira VA, Lumbreras JF, Coelho MR, Almeida JA, Araujo Filho JC, Oliveira JB, Cunha TJF, 2018. Sistema Brasileiro de Classificação de Solos, 5th ed. Embrapa, Brasília, Brazil.
Sharma S, 1996. Applied multivariate techniques. John Wiley & Sons, New York, USA.
Simões D, Fenner PT, Esperancini MST, 2009. Avaliação técnica e econômica da colheita de florestas de eucalipto com harvester. Sci For 38(88): 89–97.
Väätäinen K, Asikainen A, Sikanen L, Ala-Fossi A, 2006. The cost effect of forest machine relocations on logging costs in Finland. For Stud 45: 135–141.
Ward JH, 1963. Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301): 236–244.
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