Tuning of the metaheuristic variable neighborhood search for a forest planning problem

• Autor(a) principal: Carlos Alberto Araújo Júnior
• Data de Publicação: 2018
• Outros Autores: João Batista Mendes, Adriana Leandra de Assis, Christian Dias Cabacinha, Jonathan James Stocks, Liniker Fernandes da Silva, Helio Garcia Leite
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFMG
• Texto Completo: https://doi.org/10.1590/01047760201824032538; http://hdl.handle.net/1843/43264; https://orcid.org/0000-0003-0909-8633; https://orcid.org/0000-0002-8148-083X
• topic: Pesquisa operacional; Inteligência artificial; Florestas – Administração

In forest science it is important evaluate new technologies from computational science. This work aimed to test a different kind of metaheuristic called Variable Neighborhood Search in a forest planning problem. The management total area has 4.210 ha distributed in 120 stands in ages between 1 and 6 years old and site index since 22 m to 31 m. The problem was modelled considering the maximization of the net present value subject to the restrictions: annual cut volume between 140.000 m³ and 160.000 m³, harvester ages equal to 5, 6 or 7 years, and the impossibility of division of the management unity at harvester time. It was evaluated different settings for the Variable Neighborhood Search, varying the quantity of neighbours, the neighbourhood structure and number or generations. 30 repetitions were performed for each setting. The results were compared to the one obtained from integer linear programming and linear programming. The integer linear programming considered the best solution obtained after 1 hour of processing. The best setting to the Variable Neighborhood Search was 100 neighbours, a neighbourhood structure with changes in 1%, 2%, 3% and 4% of prescriptions and 500 iterations. The results shown by the Variable Neighborhood Search was 2,77% worse than one obtained by the integer linear programming with 1 hours of processing, and 2,84% worse than the linear programming. It is possible to conclude that the presented metaheuristic can be used satisfactorily in a resolution of forest scheduling problem when the best parameters are chosen.