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: 2006
• 资助国家: 加拿大
• 起止时间: 2006-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=335468
• 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.

该提案描述了一项研究计划，其长期目标是开发森林管理规划中必要的决策支持模型，利用它们来解决管理问题，并为研究生和从业人员提供专业教育和培训。 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制定一个随机优化模型，我建议使用模拟和优化的集成，越来越多地被称为“模拟优化”。 我有兴趣使用这个模型来推进我们在两个项目领域的知识。首先，使用这个方法的算法含义值得探究。其次，我也有兴趣使用这个模型来深入了解问题本身的本质;即，规划木材收获在一个不确定的环境中。我提出的研究的新奇是，虽然随机规划模型已经开发出来，没有空间明确的，和空间明确的规划模型越来越需要规划生物多样性的目标。 在第二个问题领域，与对未来气候变化的估计相关的严重不确定性要求有计划的适应采用强有力的战略;即，在一个不确定变化的环境中部署遗传物质类似于在一个不确定的市场中投资资产。因此，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在不确定的气候变化环境中部署的种子来源的最佳投资组合。种子投资组合模型将构成我的方法的基础，以划定遗传改良股票的部署边界。加拿大各地的规划者迫切需要解决这些问题和那些继续演变的问题。