Advancing simulation-optimization modeling (SOM) techniques for the development of robust air and water pollution load reduction-allocation (PLRA) programs

推进模拟优化建模 (SOM) 技术，以开发稳健的空气和水污染负荷减少分配 (PLRA) 计划

• 批准号: 251325-2011
• 负责人: Liu, Lei
• 金额: $ 1.38万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570956
• 关键词: Advancing; simulation; optimization; modeling; SOM

Deteriorations of regional ambient air quality and natural surface water bodies are among the major environmental concerns facing decision makers and stakeholders. Effective air and water pollution load reduction-allocation (PLRA) programs are desired for air/water quality restoration and management. Simulation-optimization modeling (SOM) techniques play a central role in developing such PLRA programs, where the simulation model serves as a numerical representation of the prototype nonlinear air/water system to predict the effects of external forces (i.e. pollutant loads) on the internal responses (i.e. air/water quality); and optimization models are coupled to iteratively drive the simulation model to find the optimal PLRA strategies. The primary objective of this proposed research aims to develop advanced SOM techniques for a PLRA simulation-optimization analysis; it entails: (a) characterize the nonlinearities and uncertainties of complex air and water systems, (b) propose nonlinearity-to-uncertainty mapping (N2UM) approaches to convert the original nonlinear SOM model to its uncertain LP equivalence and investigate its performance over existing functional approaximators, (c) develop advanced inexact optimization algorithms for solving LP-equivalent models under multiple uncertainties, and (d) develop interactive post-optimality approaches to reflect the tradeoffs between decision risk and system safety for generating operational decision schemes. The developed SOM techniques will be applied to real-world cases within a Canadian air and water context. The proposed approaches represent a unique contribution to methodologies in the PLRA field. It could provide technical supports for tackling challenges in terms of computation bottleneck, uncertainty handling, and decision-risk & system-safety tradeoff analysis. Its applications to Canadian cases will help Canadian stakeholder improve their effectiveness and efficiency in managing air/water pollution problems. Besides, a number of graduate students and PDFs will be trained through participating in the proposed research. The trained HQPs will fill a pressing need for environmental management professionals in various sectors.

区域环境空气质量和天然地表水体的恶化是决策者和利益相关者面临的主要环境问题之一。空气/水质量恢复和管理需要有效的空气和水污染负荷减少分配（PLRA）计划。仿真优化建模 (SOM) 技术在开发此类 PLRA 程序中发挥着核心作用，其中仿真模型作为原型非线性空气/水系统的数值表示，以预测外力（即污染物负荷）对内部的影响。反应（即空气/水质量）；与优化模型耦合，迭代驱动仿真模型找到最优的 PLRA 策略。这项研究的主要目标是开发用于 PLRA 模拟优化分析的先进 SOM 技术；它需要：(a) 表征复杂空气和水系统的非线性和不确定性，(b) 提出非线性到不确定性映射 (N2UM) 方法，将原始非线性 SOM 模型转换为其不确定的 LP 等效模型，并研究其相对于现有模型的性能函数逼近器，(c) 开发先进的不精确优化算法，用于在多种不确定性下求解 LP 等效模型，以及 (d) 开发交互式后优化方法，以反映决策风险与决策风险之间的权衡用于生成操作决策方案的系统安全性。开发的 SOM 技术将应用于加拿大空气和水环境中的实际案例。所提出的方法代表了对 PLRA 领域方法学的独特贡献。它可以为解决计算瓶颈、不确定性处理、决策风险与系统安全权衡分析等挑战提供技术支持。其在加拿大案例中的应用将帮助加拿大利益相关者提高管理空气/水污染问题的有效性和效率。此外，一些研究生和PDF将通过参与拟议的研究得到培训。经过培训的总部专业人士将满足各行业对环境管理专业人员的迫切需求。