【摘要】 匹配问题是第四方物流(fourth party logistics, 4PL)协同运作模式下的关键问题，然而，在此过程中会面临不完全评价值信息给决策带来的挑战。针对这一挑战，采用了一种基于芬克奇异值分解(Funk singular value decomposition, FunkSVD)预测不完全评价值信息的方法，建立以第三方物流与企业客户双方满意度最大为目标、以一对一匹配为约束的4PL双边匹配模型，并提出一种集成匈牙利法粒子修复机制的粒子群优化(Hungary-particle swarm optimization, Hungary-PSO)算法。算例分析表明，FunkSVD对不完全评价值信息的预测更接近于真实值，Hungary-PSO算法比CPLEX更适用于解决大规模的4PL双边匹配问题。最后，通过参数分析，对4PL平台的营销力度提出管理建议。更多还原

【Abstract】 Matching problem is a key problem in the collaborative operation mode of fourth party logistics(4 PL). However, in this process, it will face the challenge brought by incomplete evaluation value information to decision-making. To solve this challenge, a method of predicting incomplete evaluation value information based on the funk singular value decomposition(FunkSVD) technique is used. A 4 PL bilateral matching model with the objective of maximizing the satisfaction of both the third-party logistics and enterprise customers and the constraints of one-to-one matching is established. Subsequently, a particle swarm optimization algorithm integrating hungarian method-based particle repairing mechanism(Hungary-PSO) is proposed. The example analysis shows that FunkSVD predicts the incomplete evaluation value information more closely to the real value, and Hungary-PSO is more suitable for solving large-scale 4 PL bilateral matching problems than CPLEX. Finally, through parameter analysis, management suggestions on the marketing strength of 4 PL platform are proposed.更多还原