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Shortest Fuzzy Hamiltonian Cycle on Transportation Network Using Minimum Vertex Degree and Time-dependent Dijkstra’s Algorithm

机译：使用最小顶点度和时间依赖Dijkstra算法运输网络上最短的模糊汉密尔顿周期

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摘要

Determining the shortest travel time on transportation is affected by many logistics parameters such as cost, quality, speed and satisfaction. This study proposes a new hybrid time-dependent Dijkstra’s and minimum vertex degree algorithm on a spherical bipolar fuzzy weighted graph to find shortest travel time of Hamiltonian cycles on transportation problems. To weight the transportation points in uncertain environment, decision makers can express their views with spherical bipolar fuzzy information based on the criteria. In the proposed methodology, for a given digraph, the nodes are weighted by the decision makers’ spherical bipolar fuzzy evaluations, and fuzzy Hamiltonian paths are determined using the minimum vertex degree method. The fuzzy Hamiltonian cycles constitute the routes for transportation network, and the starting point, which gives the shortest travel time, is investigated by time-dependent Dijkstra’s algorithm. This approach is an alternative way to solve the traveling salesman problem (TSP) in time-dependent graphs withΘ(VN2)time complexity. The proposed methodology is illustrated on a logistic network. It is intended to guide future graph-transportation research.
机译：确定运输最短的旅行时间受到许多物流参数的影响，例如成本，质量，速度和满意度。本研究提出了一种新的混合时间依赖的Dijkstra和最小的顶点度数算法在球面双极模糊加权图上，找到了汉密尔顿循环对运输问题的最短旅行时间。为了重量不确定环境中的运输点，决策者可以根据标准用球形双极模糊信息表达他们的观点。在所提出的方法中，对于给定的数字，节点由决策者的球形双极模糊评估加权，并且使用最小顶点度法测定模糊哈密顿路径。模糊汉密尔顿循环构成运输网络的路线，并且通过时间依赖的Dijkstra的算法研究了提供最短旅行时间的起点。这种方法是在具有θ（VN2）时间复杂度的时间依赖性图形中解决旅行推销员问题（TSP）的替代方法。所提出的方法在物流网络上示出。它旨在引导未来的图形运输研究。

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