不确定条件下退役机电产品回收与再制造的绿色绩效动态演化机制研究

Recycling and remanufacturing of retired mechanical and electrical products have a significant impact on green performance such as national resources and environment, however, the mechanism underlying it has not been fully understood due to the lack of systematic studies on one hand, and especially due to the presence of uncertainties in the system on the other hand, which result in many difficulties in structure modeling , quantitative description of the coupling among green factors, as well as in the prediction of green performance. This poses consequently a big challenge to the government in making scientific decisions. This project is aimed to solve those problems by identifying and defining the green impact factors and their couplings based on ISM and Petri net, after which a static model for the system structure and a model for the system dynamics are proposed, in which multi- dimensional factors involving resources, environment, economy and human factor are considered. By means of numerical simulation of the models, the multi-level hierarchical mechanism and laws of dynamic evolution of green performance in recycling and remanufacturing of retired mechanical and electrical products are studied, and the green performance are predicted and evaluated. In order to treat with the uncertainties in the system, Monte Carlo simulation and Sample Average Approximation method are applied for the analysis of variance in the green performance caused by the presence of uncertainty. Based on it, optimal decisions under uncertainty are studied using stochastic multi-objective optimization approach, so that predictability of green policies made by the government can be improved on theoretical basis. Based on results of case studies, policy suggestions are to be made for the green sustainable development in the recycling and remanufacturing of retired mechanical and electronic products.

退役机电产品的回收与再制造对国家资源与环境等绿色绩效指标产生重大影响，但其影响机制尚未明了，缺乏系统性理论研究，尤其是其中诸多不确定性因素的存在，造成系统结构建模困难、绿色因子间量化耦合关系不明确、绿色绩效难于预测等难题，给政府科学决策带来挑战。针对以上问题，本项目以退役机电产品回收与再制造系统为对象，采用ISM和Petri网方法对系统的绿色因子及其耦合关系进行识别与定义，提出系统静态结构与动态过程模型，进而构建环境、资源、经济、人因等多维度系统动力学模型。通过对该模型的数值模拟，揭示绿色绩效的多级递阶动态演化机制及规律，对绿色绩效进行预测与评价。在此基础上，采用蒙特卡罗仿真和样本平均逼近方法对不确定性所引起的绿色绩效变化进行分析，并基于多目标随机优化方法寻求不确定条件下的最优决策，以从理论上提高政策制定的前瞻性。通过实证研究，提出支持退役机电产品回收与再制造绿色可持续发展的政策建议。

退役机电产品回收与再制造对国家资源与环境等绿色绩效指标具有重大影响，但其影响机制尚未明了，尤其是其中诸多不确定性因素的存在，造成系统建模困难、绿色绩效难于预测等难题。针对以上问题，本项目对不确定条件下退役机电产品回收与再制造绿色绩效的动态演化机制进行理论研究。内容包括：.（1）退役机电产品回收与再制造的绿色绩效系统动力学建模方法研究。提炼了回收与再制造系统的50个绿色影响因素，构建了一种多层次多维度回收与再制造绿色系统的静态结构模型，描述了系统构成因子及其与绿色绩效之间的关联。提出了报废机电产品回收与再制造过程信息综合追溯模型，阐述了消费者行为对回收率的影响关系。以报废汽车为例，构建了其回收与再制造绿色系统动力学模型，用于分析回收与再制造对资源、环境、人因等绿色绩效的影响。.（2）不确定条件下退役机电产品回收与再制造的绿色绩效模型研究。建立了质量不确定性下的碳排放模型，提出独立再制造系统的最优生产决策模型；针对多制造商博弈，提出了4种竞争性闭环供应链分散式决策模型；构建了市场需求不确定条件下的EOQ模型，从环境与经济两个层面推导出需求不确定下的再制造系统成本与利润最优策略；针对混合再制造系统，提出了回收数量与市场需求双重不确定条件下的EES-EOQ模型，推导出三类再制造最优策略。 .（3）退役机电产品回收与再制造的绿色绩效评价模型研究。提出了一种基于字典评估来测量DMU效率的新DEA（数据包络分析）评价方法，提出了再制造生态效率的指标因素，构建了工程机械产品回收及再制造过程的碳排放评估模型，及质量不确定情况下的回收与再制造环境效益和成本评价模型。.基于以上研究成果，发表学术论文18篇（其中SCI收录14篇，SCI一区、TOP期刊论文7篇，同时被SSCI收录8篇）；授权软件著作权3项；获中国循环经济协会科学技术奖三等奖1项；在该研究方向已毕业2名博士研究生，9名硕士研究生。