Path finding models attempt to provide efficient approaches for finding shortest paths in networks. A well-known shortest path algorithm is the Dijkstra algorithm. This paper redesigns it in order to tackle situations in which the parameters of the networks may be uncertain. To be precise, we allow that the parameters take the form of special picture fuzzy numbers. We use this concept so that it can flexibly fit the vague character of subjective decisions. The main contributions of this article are fourfold: \(\mathrm{(i)}\) The trapezoidal picture fuzzy number along with its graphical representation and operational laws is defined. \(\mathrm{(ii)}\) The comparison of trapezoidal picture fuzzy numbers on the basis of their expected values is proposed in terms of their score and accuracy functions. \(\mathrm{(iii)}\) Based on these elements, we put forward an adapted form of the Dijkstra algorithm that works out a picture fuzzy shortest path problem, where the costs associated with the arcs are captured by trapezoidal picture fuzzy numbers. Also, a pseudocode for the application of our solution is provided. \(\mathrm{(iv)}\) The proposed algorithm is numerically evaluated on a transmission network to prove its practicality and efficiency. Finally, a comparative analysis of our proposed method with the fuzzy Dijkstra algorithm is presented to support its cogency.

路径查找模型试图提供有效的方法来查找网络中的最短路径。众所周知的最短路径算法是Dijkstra算法。本文对其进行了重新设计，以解决网络参数可能不确定的情况。确切地说，我们允许参数采用特殊图片模糊数字的形式。我们使用此概念，以便它可以灵活地适应主观决策的模糊特征。本文的主要贡献有四个方面：\（\ mathrm {{i}} \）定义了梯形图片模糊数及其图形表示和运算规律。\（\ mathrm {（ii）} \）根据分数和精度函数，提出了基于期望值的梯形图片模糊数的比较。\（\ mathrm {（iii）} \）在这些元素的基础上，我们提出了Dijkstra算法的一种改编形式，该算法解决了图片模糊最短路径问题，其中与弧相关的成本由梯形图片模糊数捕获。此外，还提供了适用于我们解决方案的伪代码。\（\ mathrm {（iv）} \）在传输网络上对提出的算法进行了数值评估，以证明其实用性和效率。最后，对我们提出的方法与模糊Dijkstra算法进行了比较分析，以支持其能力。