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

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攸关方面临的主要环境问题之一。有效的空气和水污染负荷削减分配(PLRA)计划是空气/水质量恢复和管理所必需的。仿真优化建模(SOM)技术在开发这类PLRA程序中起着核心作用，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)对内部响应(即空气/水质量)的影响；优化模型被耦合以迭代地驱动仿真模型以寻找最优PLRA策略。这项研究的主要目标是开发先进的SOM技术用于PLRA模拟优化分析；它包括：(A)表征复杂空气和水系统的非线性和不确定性；(B)提出将原始的非线性SOM模型转换为其不确定的LP等价模型的方法，并研究其相对于现有函数逼近器的性能；(C)开发高级不精确优化算法来求解多重不确定性下的LP等价模型；以及(D)开发交互式后优化方法来反映决策风险和系统安全性之间的权衡以生成运行决策方案。开发的SOM技术将在加拿大的空气和水环境中应用于真实世界的案例。拟议的方法是对《公共部门会计准则》领域方法论的独特贡献。它可以为解决计算瓶颈、不确定性处理、决策风险和系统安全权衡分析等方面的挑战提供技术支持。它在加拿大案例中的应用将有助于加拿大利益攸关方提高其在管理空气/水污染问题方面的效力和效率。此外，一些研究生和PDF将通过参与拟议的研究进行培训。训练有素的总部基地将满足不同行业对环境管理专业人员的迫切需求。

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.