在总量控制与交易机制下，企业除面对传统供需风险外，还包括碳配额分配不足、碳市场价格波动、自净减排成本和收益变化等新的风险。如何考虑各种不确定因素影响，整合并优化生产和碳资源配置决策，有效规避多源风险，是当今理论研究热点，也是实践亟待解决的重要难题。现有研究过度简化模型结构、忽略自净项目特征、将自净与碳交易决策在同一规划尺度上简单互补，严重脱离实际，存在较大缺陷。针对现有研究不足，本项目充分考虑自净减排效益延迟、资本投资回收期长、项目间可能互斥、收益成分结构复杂不确定、减排成本不确定间断递增以及其他风险因素，采用计算机仿真构建风险决策过程模型，结合企业资源规划和能源管理体系，将生产、自净和碳交易决策与风险分析有效整合；在此基础上以现代启发式算法为核心与仿真相集成，实现生产和碳资源配置策略的优化，并对不确定条件下最优策略组合的边界条件与分布特征展开研究，提升理论研究水平和实践应用价值.

Under a Cap and Trade (C&T) mechanism, industrial enterprises must face a number of new risks, such as insufficient allocation of emission quotes, market fluctuation of carbon credit price, and unpredictable cost and benefit of self-purification (SP, i.e making green improvement to reduce emission), in addition to traditional supply/demand risk. How to integrate different strategies or options, optimize production and carbon-resource planning decisions, and mitigate various risks effectively under C&T conditions has been a critical difficult problem facing the Chinese enterprises and of primary interest for academic studies. Current research over-simplified the problem formulation or modeling (e.g. ignoring the important characteristics of self-purification projects and complement the SP with trading decisions on the same time-scale) in order to obtain closed analytic solutions or carry out intended theoretical deductions. This has led to defective and inconsistent and unrealistic solutions. Targeting on these issues, this research proposes to integrate the production, self-purification and emission trading decisions and risk analyses within the constraints characterised by SP projects (e.g. decision and implementation time scale, longer return/payback period, possible conflicts among the projects, uncertain abatement cost and green yield), combining other related risk factors. Designing and constructing simulation models to represent production system flow and related decisions to characterize process dynamics and uncertainties, and evaluate system output/performance (economic and green benefits); developing effective algorithm that combines the power of stochastic and heuristic searches to optimize the related decisions based on the evaluation of simulation; finally developing a knowledge-based risk analysis system interfaced with the simulation model to accomplish the evaluation and selection of risk mitigation strategies. These expected results will contribute significantly to both theoretical exploration and practical application.