供应链优化方面文献很多，但是以减少供应链波动角度的优化就是另辟蹊径了。其实在供应链建模时，就已经明确了优化的角度。Vieira指出，四阶段供应链的第一层框架就是供应端-制造端-零售端-市场端的信息流和物料流。其中信息流是从供应链的上游流向下游，即从市场端流向供应端，传递的主要是需求信息；物料流是从供应链的下游流向上游，即从供应端流向市场端，传递的主要是产品及零部件。由于信息流和物料流的联系，供应链中就有了波动的概念，包括需求的波动，生产水平和库存水平的波动。由于波动的产生，供应链优化的目标就相应产生了，可以归纳为：服务水平和平均库存水平。服务水平为供应链企业满足市场需求的能力，其中也包括响应时间；而平均库存水平就是整条供应链上供应端、制造端和零售端的所有库存总和[7-8]。Jen S. Shang[10]运用田口方法和响应曲面法对供应链仿真模型进行了优化，以确定供应链总成本最小的运作条件。与Jen S. Shang[10]不同的是，本文采用了更为复杂的四阶段供应链模型，细化了供应链的分销过程，因此在供应链仿真模型中涉及更为复杂的结构和流程。
    参考文献
[1]Wen Shi, Zhixue Liu, Jennifer Shang, Yujia Cui. Multi-criteria robust design of a JIT-based cross-docking distribution center for an auto parts supply chain[J]. European Journal of Operational Research,2013,229: 695-706
[2]Jen S. Shang, Shan ling Li, Pandu Tadikamalla. Multicriteria design and control of a cellular manufacturing system through simulation and optimization. International Journal of Production Research, 1998,36(6), 1515–1528.
[3]Taho Yang, Yiyo Kuo,Chao-Ton Su,Chia-Lin Hou. Lean production system design for fishing net manufacturing using lean principles and simulation optimization [J]. Journal of Manufacturing Systems, 2015,34:66-73
[4]Sanjay Kumar Shukla, M.K. Tiwari, Hung-Da Wan, Ravi Shankar. Optimization of the supply chain network: Simulation, Taguchi, and Psychoclonal algorithm embedded approach[J]. Computers & Industrial Engineering, 2010,58:29-39
[5]Amir Hassanzadeh, Ahmad Jafarian, Maghsoud Amiri. Modeling and analysis of the causes of bullwhip effect in centralized and decentralized supply chain using response surface method[J]. Applied Mathematical Modelling, 2014, 38: 2353-2365
[6]Guilherme Ernani Vieira. Ideas for modeling and simulation of supply chain with Arena[C]. Proceedings of the 2004 Winter Simulation Conference
[7] F.R. Jacobs, Playing the Beer Distribution Game Over the Internet[J]. Production and Operations Management 9(1) (2000),31–39.
[8]Guilherme E, Vieira,Osmar Cesar Junior. A conceptual model for the creation of supply chain simulation models[C]. Proceedings of the 2005 Winter Simulation Conference
[9]S.O. Kimbrough, D.J. Wu and F. Zhang. Computers play the beer game: can artificial agents manage supply chains[J]. Decision Support Systems 33 (2002), 323–333.
[10]Jen S. Shang, Shan ling Li, Pandu Tadikamalla. Operational design of a supply chain system using the Taguchi method, response surface methodology, simulation, and optimization[J]. International Journal of Production Research,2004,42:3823-3849
[11]Sameer Kumar, Charu Chandra and Marvin S. Seppanen. Demonstrating supply chain parameter optimization through beer game simulation[J].Information Knowledge Systems Management,2007,6:291-322
[12]M.E.Johnson, D.F. Pyke. A Framework for Teaching Supply Chain Management[J].Production and Operations Management 9(1) (2000), 2–18.
[13]Taho Yang, Yuan-Feng Wen, Fang-Fang Wang. Evaluation of robustness of supply chain information-sharing strategies using a hybrid Taguchi and multiple criteria decision-making method[J].Int. J. Production Economics,2011,134:458-466
[14]汪新. 相关多响应的稳健参数设计[D].云南财经大学,2011. 供应链仿真建模文献综述和参考文献(2):http://www.751com.cn/wenxian/lunwen_30091.html