Additional insight into the performance of a new heuristic for solving spatially constrained forest planning problems

被引:9
|
作者
Zhu, Jianping [1 ]
Beffinger, Pete [1 ]
Li, Rongxia [1 ]
机构
[1] Univ Georgia, Warnell Sch Forestry & Nat Resources, Athens, GA 30602 USA
关键词
forest planning; harvest scheduling; heuristics; raindrop method;
D O I
10.14214/sf.276
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
The raindrop method of searching a solution space for feasible and efficient forest management plans has been demonstrated as being useful under a limited set of circumstances, mainly where adjacency restrictions are accommodated using the unit restriction model. We expanded on this work and applied the model (in a modified form) to a problem that had both wood flow and area restriction adjacency constraints, then tested the problem formulation on six hypothetical forests of different sizes and age class distributions. Threshold accepting and tabu search were both applied to the problems as well. The modified raindrop method's performance was best when applied to forests with normal age class distributions. I-opt tabu search worked best on forests with young age class distributions. Threshold accepting and the raindrop method both performed well on forests with older age class distributions. On average, the raindrop method produced higher quality solutions for most of the problems, and in all but one case where it did not, the solutions generated were not significantly different than the heuristic that located a better solution. The advantage of the raindrop method is that it uses only two parameters and does not require extensive parameterization. The disadvantage is the amount of time it needs to solve problems with area restriction adjacency constraints. We suggest that it may be advantageous to use this heuristic on problems with relatively simple spatial forest planning constraints, and problems that do not involve young initial age class distributions. However, generalization of the performance of the raindrop method to other forest planning problems is problematic, and will require examination by those interested in pursuing this planning methodology. Given that our tests of the raindrop method are limited to a small set of URM and ARM formulations, one should view the combined set of work as additional insight into the potential performance of the method on problems of current interest to the forest planning community.
引用
收藏
页码:687 / 698
页数:12
相关论文
共 50 条
  • [31] A new filled function method for solving constrained global optimization problems
    Gao, Yuelin
    Lin, Hongwei
    Li, Minmin
    Yang, Lili
    OPTIMIZATION, 2024,
  • [32] A new projection algorithm for solving constrained equilibrium problems in Hilbert spaces
    Nguyen The Vinh
    OPTIMIZATION, 2019, 68 (08) : 1447 - 1470
  • [33] A new optimization algorithm for solving complex constrained design optimization problems
    Rao, R. Venkata
    Waghmare, G. G.
    ENGINEERING OPTIMIZATION, 2017, 49 (01) : 60 - 83
  • [34] A new neural network for solving a class of constrained least square problems
    Ye, DZ
    Xia, YS
    Wu, XY
    CHINESE JOURNAL OF ELECTRONICS, 2001, 10 (04): : 493 - 496
  • [35] A new optimization algorithm for solving complex constrained design optimization problems
    Department of Mechanical Engineering, S.V. National Institute of Technology, Surat, India
    Eng Optim, 1 (60-83):
  • [36] A New Objective Penalty Function Approach for Solving Constrained Minimax Problems
    Li J.
    Wu Z.
    Long Q.
    Wu, Z. (zywu@cqnu.edu.cn), 1600, Springer Science and Business Media Deutschland GmbH (02): : 93 - 108
  • [37] A New GT Heuristic for Solving Multi Objective Job Shop Scheduling Problems
    Lakshmipathy, D.
    Chandrasekaran, M.
    Balamurugan, T.
    Sriramya, P.
    ADVANCED MANUFACTURING RESEARCH AND INTELLIGENT APPLICATIONS, 2014, 591 : 184 - +
  • [38] New procedures with new activity assumptions for solving resource constrained project scheduling problems
    Ben Issa, Samer
    Tu, Yiliu
    JOURNAL OF PROJECT MANAGEMENT, 2020, 5 (01) : 41 - 58
  • [39] A high performance neural network model for solving chance constrained optimization problems
    Nazemi, Alireza
    Tahmasbi, Narges
    NEUROCOMPUTING, 2013, 121 : 540 - 550
  • [40] Fixed Charge Transportation Problems: a new heuristic approach based on Lagrangean relaxation and the solving of core problems
    Saez Aguado, Jesus
    ANNALS OF OPERATIONS RESEARCH, 2009, 172 (01) : 45 - 69