Abstract: To describe and optimize the product supply chain’ carbon footprint under uncertainity accurately,based on the distributed decision making and stochastic chance constrained programming methods,many uncertainties of environment such as market demand and resource constraints were considered for constructing the optimization model of multi-parameter and multi-product supply chain carbon footprint.According to the constrained nonlinear mix planning features of model,an improved simulated annealing genetic hybrid intelligent algorithm was designed.Proved by the compared tests with benchmark functions,the algorithm performance was significantly improved in all aspects.A model example was designed to test effectiveness of model and algorithm,and the control basis of parameters with the sensitivity analysis was found.It was also provided a theoretical and practical guidance for the optimization problem of product supply chain carbon footprint.

Key words: product supply chain, carbon footprint, simulated annedling genetic algorithm, hybrid algorithm, uncertain environment

摘要： 为了更精确地描述并优化不确定环境下的产品供应链碳足迹,利用分布决策和随机机会约束规划法,综合考虑产品市场需求、资源约束等诸多不确定性环境因素,构建了多参数多产品供应链碳足迹优化模型。针对模型多约束非线性混合规划特性,设计了一种改良模拟退火遗传混合智能算法。基准测试函数运行比较显示,该算法各方面性能均有明显改进。通过对构造算例进行仿真,验证了模型及算法的有效性,并通过敏感性分析解析了模型参数控制依据,以求为产品供应链碳足迹优化问题提供一定的理论和实践指导。

关键词: 产品供应链, 碳足迹, 模拟退火遗传算法, 混合算法, 不确定环境