Comment on "A simple way to incorporate uncertainty and risk into forest harvest scheduling"

被引:4
|
作者
Eyvindson, Kyle [1 ]
Kangas, Annika [2 ]
机构
[1] Univ Jyvaskyla, Dept Biol & Environm Sci, POB 35, Jyvaskyla 40014, Finland
[2] Nat Resources Inst Finland Luke, Econ & Soc, POB 68, Joensuu 80101, Finland
关键词
Stochastic programming; Risk; Uncertainty; Conditional Value at Risk;
D O I
10.1016/j.foreco.2016.03.038
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
In a recent research article, Robinson et al. (2016) described a method of estimating uncertainty of harvesting outcomes by analyzing the historical yield to the associated prediction for a large number of harvest operations. We agree with this analysis, and consider it a useful tool to integrate estimates of uncertainty into the optimization process. The authors attempt to manage the risk using two different methods, based on deterministic integer linear programming. The first method focused on maximizing the 10th quantile of the distribution of predicted volume subject to area constraint, while the second method focused on minimizing the variation of total quantity of volume harvested subject to a harvest constraint. The authors suggest that minimizing the total variation of the harvest could be a useful tool to manage risk. Managing risks requires trade-offs, however, typically less risk involves higher costs. The authors only superficially stated the costs and did not consider if these costs are reasonable for the management of risk. In this comment, we specifically develop the models used in their article, and demonstrate a method of managing the downside risk by utilizing the Conditional Value at Risk. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:86 / 91
页数:6
相关论文
共 50 条
  • [21] Forest harvest scheduling with clearcut and core area constraints
    Neto, Teresa
    Constantino, Miguel
    Martins, Isabel
    Pedroso, Joao Pedro
    ANNALS OF OPERATIONS RESEARCH, 2017, 258 (02) : 453 - 478
  • [22] Spatially explicit forest harvest scheduling with difference equations
    Rachel St. John
    Sándor F. Tóth
    Annals of Operations Research, 2015, 232 : 235 - 257
  • [23] Associations between forest harvest scheduling and artificial intelligence
    Bettinger, P.
    Rasheed, K.
    Maier, F.
    Merry, K.
    INTERNATIONAL FORESTRY REVIEW, 2024, 26 (04) : 387 - 397
  • [24] FOREST HARVEST SCHEDULING PLAN INTEGRATED TO THE ROAD NETWORK
    Belavenutti, Pedro Henrique
    da Silva, Martins
    Arce, Julio Eduardo
    Loch, Gustavo Valentim
    David, Hassan Camil
    Fiorentin, Luan Demarco
    CERNE, 2016, 22 (01) : 69 - 75
  • [25] Spatially explicit forest harvest scheduling with difference equations
    St John, Rachel
    Toth, Sandor F.
    ANNALS OF OPERATIONS RESEARCH, 2015, 232 (01) : 235 - 257
  • [26] Efficiency in forest management: A multiobjective harvest scheduling model
    Hernandez, M.
    Gomez, T.
    Molina, J.
    Leon, M. A.
    Caballero, R.
    JOURNAL OF FOREST ECONOMICS, 2014, 20 (03) : 236 - 251
  • [27] GIS tool for optimization of forest harvest-scheduling
    Vopenka, Petr
    Kaspar, Jan
    Marusak, Robert
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2015, 113 : 254 - 259
  • [28] Forest harvest scheduling with clearcut and core area constraints
    Teresa Neto
    Miguel Constantino
    Isabel Martins
    João Pedro Pedroso
    Annals of Operations Research, 2017, 258 : 453 - 478
  • [29] A Simple Way to Incorporate Target Structural Information in Molecular Generative Models
    Zhang, Wenyi
    Zhang, Kaiyue
    Huang, Jing
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2023, 63 (12) : 3719 - 3730
  • [30] A mixed integer programming model for forest harvest scheduling problem
    Damanik, Sarintan Efratani
    Purwoko, Agus
    Hidayat, Rahmat
    1ST INTERNATIONAL CONFERENCE ON ADVANCE AND SCIENTIFIC INNOVATION, 2019, 1175