Decision support models for forest management planning

森林管理规划的决策支持模型

• 批准号: 314003-2006
• 负责人: Crowe, Kevin
• 金额: $ 1.31万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=366014
• 关键词: Decision; support; models; forest; management

This proposal describes a research program with long-term objectives of developing the necessary decision support models in forest management planning, using them to solve management problems, and providing specialized education and training to graduate students and practitioners.      The research program's short-term objectives are to explore two problem areas.  The first problem area requires the formulation of a stochastic optimization model, and I propose using the integration of simulation and optimization, increasingly referred to as "simulation optimization".   I am interested in using this model to advance our knowledge in two project areas.  First, the algorithmic implications of using this method deserve inquiry.  Second, I am also interested in using the model to gain insights into the nature of the problem per se; i.e., of planning timber harvests in an uncertain environment.  The novelty of my proposed research is that, although stochastic planning models have been developed, none have been spatially explicit; and spatially explicit planning models are increasingly required to plan for biodiversity objectives.      In the second problem area, the deep uncertainty associated with estimates of future climate change requires that planned adaptation employ robust strategies; i.e., strategies that perform well when compared with the alternatives across a wide range of plausible futures.  The deployment of genetic material in an environment of uncertain change is analogous to the investment in assets in an uncertain market. For this reason, my approach will be to use a seed portfolio based on Markowitz' portfolio optimization model in selecting an optimal portfolio of seed sources to deploy in an environment of uncertain climate change.  The seed portfolio model will form the basis of my approach to delineating the deployment boundaries of genetically improved stock. Solutions to these problems and those that continue to evolve are desperately needed by planners across Canada.

该提案描述了一项具有长期目标的研究计划，目的是开发森林管理规划中必要的决策支持模式，利用这些模式解决管理问题，并为研究生和实践者提供专门的教育和培训。他说，研究计划的短期目标是探索两个问题领域。第一个问题领域需要建立一个随机优化模型，我建议使用模拟和优化的结合，越来越多地被称为“模拟优化”。我有兴趣利用这个模型来推进我们在两个项目领域的知识。第一，使用这种方法的算法含义值得探讨。第二，我也有兴趣使用该模型来洞察问题本身的本质；我提出的研究的新奇之处在于，尽管已经开发了随机规划模型，但没有一个在空间上是明确的；而且越来越多地需要空间上明确的规划模型来规划生物多样性目标。他说，在第二个问题领域，与未来气候变化估计相关的深度不确定性要求计划的适应采用稳健的战略；即，与各种看似合理的未来的替代方案相比，表现良好的战略。在不确定变化的环境中部署遗传物质，类似于在不确定的市场中投资资产。为此，我的方法将是使用基于Markowitz的投资组合优化模型的种子投资组合来选择种子来源的最优投资组合，以在不确定的气候变化环境中进行部署。种子投资组合模型将构成我划定遗传改良股票部署边界的方法的基础。加拿大各地的规划者迫切需要解决这些问题和那些不断演变的问题的方案。