强制性碳减排条件下企业资源配置、生产计划与风险规避研究

项目名称： 强制性碳减排条件下企业资源配置、生产计划与风险规避研究

项目编号： No.71272089

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 管理科学

项目作者： 陈智民

作者单位： 深圳大学

项目金额： 54万元

中文摘要： 在碳排放总量世界第一的中国，以行政惩罚和市场交易为主要手段的强制性碳减排迫在眉睫。如何优化资源配置和生产计划，合理权衡绿色改进、购买排放权、承担超排惩罚等不同策略以满足减排约束并有效规避风险，是当前企业运作亟待解决的重要难题。现有研究主要集中在单周期、确定性问题上，对实际不确定及碳资产多期交易问题鲜有涉及，提出有效风险规避策略的研究则更为罕见。本项目拟采用计算机仿真构建过程模型，刻画随机特性和多源风险，求取系统动态绩效；以现代启发式算法为核心与仿真相集成，对生产计划和资源配置策略进行优化；将基于知识的风险决策模型与仿真优化模型相整合，完成对风险规避措施的评估和选择。本项目提出的理论和方法，可有效解决强制性碳减排、不确定环境以及多源风险作用下的企业资源配置、生产计划和风险规避问题，通过对理论模型从单周期静态向多周期动态的拓展和深化，推动该领域研究的发展，因此具有重要的理论和应用价值。

中文关键词： 强制性碳减排；资源配置；生产计划；风险决策；仿真优化

英文摘要： As the world's number one on carbon emission, China is agressively pushing for a compulsory carbon-emission reduction (CCER) program based primarly on administrative penalty and market trade of emission allowances. It becomes a challenging and difficult problem for Chinese enterprises to optimize production and resource planning, balance between different strategies such as trading emission-allowance, taking penalty for over-emission, or making green improvement to cut emission, and mitigating negative impact brought by the new risks associated with CCER. Current research focus mainly on static and deterministic modeling, addressing little on the problem's dynamic and uncertain characteristics or risk mitigation under CCER condition. This study propose to design and construct simulation models to represent production system flow (operations and decisions), characterize process dynamics and uncertainties, and evaluate system performance (economic and green benefits). It also develops and integrates effective algorithm that combines stochastic and heuristic search methods with the simulation model to optimize the related decisions; and develop a knowledge-based multi-risk decision analysis module interfaced with the simulation model to accomplish the evaluation and selection of risk aversion strategies. These expe

英文关键词： Compulsory carbon-emission reduction；Resource allocation；Production planning；Risk decision-making；Simulation-based optimization