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Silva Fennica 1926-1997
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Articles by Lingbo Dong

Category : Research article

article id 7803, category Research article
Lingbo Dong, Pete Bettinger, Huiyan Qin, Zhaogang Liu. (2018). Reflections on the number of independent solutions for forest spatial harvest scheduling problems: a case of simulated annealing. Silva Fennica vol. 52 no. 1 article id 7803. https://doi.org/10.14214/sf.7803
Keywords: simulated annealing; forest management planning; combinatorial optimization; neighborhood search; adjacency constraint
Highlights: No one particular neighborhood search technique of simulated annealing was found to be universally acceptable; The optimal number of independent solutions necessary for addressing the area restriction harvest scheduling model was described with a negative logarithmic function that was related with the problem size. However, optimal number of independent solutions necessary was not sensitive to the problem size for non-spatial and unit restriction harvest scheduling model problems, which should be somewhat above 250 independent runs; The types of adjacency constraints have moderate effects on the number of independent solutions, but these effects are not significant.
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article id 1622, category Research article
Lingbo Dong, Pete Bettinger, Zhaogang Liu, Huiyan Qin, Yinghui Zhao. (2016). Evaluating the neighborhood, hybrid and reversion search techniques of a simulated annealing algorithm in solving forest spatial harvest scheduling problems. Silva Fennica vol. 50 no. 4 article id 1622. https://doi.org/10.14214/sf.1622
Keywords: spatial harvest scheduling; metaheuristics; combinatorial optimization; neighborhood search; reversion search
Highlights: The performances of neighborhood, hybrid and reversion search strategies of simulated annealing were evaluated when implemented with a forest spatial harvest scheduling problem; The performances of alternative search strategies of simulated annealing were all systematic and clear better than the conventional strategy; The reversion techniques were significant superior to the other search strategies in solving forest spatial harvest scheduling problems.
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