The shortest path problem (SPP) is a special network structured linear programming problem that appears in a wide range of applications. Classical SPPs consider only one objective in the networks while some or all of the multiple, conflicting and incommensurate objectives such as optimization of cost, profit, time, distance, risk, and quality of service may arise together in real-world applications. These types of SPPs are known as the multi-objective shortest path problem (MOSPP) and can be solved with the existing various approaches. This paper develops a Data Envelopment Analysis (DEA)-based approach to solve the MOSPP with fuzzy parameters (FMOSPP) to account for real situations where input–output data include uncertainty of triangular membership form. This approach to make a connection between the MOSPP and DEA is more flexible to deal with real practical applications. To this end, each arc in a FMOSPP is considered as a decision-making unit with multiple fuzzy inputs and outputs. Then two fuzzy efficiency scores are obtained corresponding to each arc. These fuzzy efficiency scores are combined to define a unique fuzzy relative efficiency. Hence, the FMOSPP is converted into a single objective Fuzzy Shortest Path Problem (FSPP) that can be solved using existing FSPP algorithms.

最短路径问题（SPP）是一种特殊的网络结构化线性规划问题，它出现在广泛的应用中。传统的SPP仅考虑网络中的一个目标，而在现实应用中可能会同时出现多个或所有多个，相互冲突且不相称的目标，例如成本，利润，时间，距离，风险和服务质量的优化。这些类型的SPP被称为多目标最短路径问题（MOSPP），可以使用现有的各种方法来解决。本文开发了一种基于数据包络分析（DEA）的方法来解决带有模糊参数（FMOSPP）的MOSPP，以解决输入输出数据包括三角形成员形式不确定性的实际情况。这种在MOSPP和DEA之间建立连接的方法更加灵活，可以处理实际的实际应用。为此，将FMOSPP中的每个弧视为具有多个模糊输入和输出的决策单元。然后获得与每个弧相对应的两个模糊效率得分。这些模糊效率得分被组合以定义唯一的模糊相对效率。因此，FMOSPP被转换为可以使用现有FSPP算法解决的单个目标模糊最短路径问题（FSPP）。