不确定需求下考虑大数据投资决策的供应链鲁棒优化及协调模型

针对由数据公司、制造商和零售商组成的三级供应链系统，在仅知需求均值和方差信息下，建立了集中和分散两种决策情况下的供应链鲁棒优化模型。集中决策下，全体供应链成员共同决定产品零售价和产品生产数量；分散决策下，数据公司作为主方，决定需求信息零售价，制造商和零售商作为从方，分别决定产品批发价和产品订货数量。针对两种决策下的供应链鲁棒优化模型，给出了相应的最优决策，分析了大数据投资成本、消费者异质性需求满足程度等系统参数对供应链决策的影响。进一步，设计了能实现三级供应链完美协调的利润共享契约。最后，通过数值算例验证了所建模型和契约协调的有效性。研究表明，在集中和分散决策下，大数据投资成本只有在满足一定条件下，投资大数据才有利于供应链成员及系统利润的改善。特别地，与无协调时的情况相比，虽然协调机制下相关系统参数对供应链成员决策及利润绩效的作用机制存在差异，但文中提出的利润共享契约在一定条件下能够显著改进供应链成员利润且实现供应链完美协调。

Robust Optimization and Coordination Model for Supply Chain with Big Data Investment under Uncertain Demand

With the popularization of Internet technology, big data technology has penetrated into all walks of life, affecting the development of every industry. Big data analysis can help companies gain insight, and data-driven decision-making is becoming increasingly popular in the manufacturing industry. However, in reality, many companies do not have sufficient ability to analyze the complex and large data, but choose to outsource their big data project, which greatly increases the production cost. Therefore, it is of practical significance to consider integrating data companies into the supply chain system. In terms of supply chain decision-making, many researchers devoted themselves to the design of supply chain coordination mechanism, but mainly assume that the product demand is known or follows a well-known distribution, which is unrealistic.