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Robust shortest path problem based on a confidence interval in fuzzy bicriteria decision making

机译:模糊双准则决策中基于置信区间的鲁棒最短路径问题

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This paper proposes a model for a robust shortest path problem with random edge costs. The proposed model is formulated as a bicriteria optimization problem to minimize the total cost of the path from a start node to an end node as well as to maximize the range of the confidence interval satisfying the condition that the total cost of any path in the region is less than a target value in terms of robustness. It is not always the case that a solution that simultaneously optimizes all of the objectives exists when the objectives conflict with each other. In this paper, fuzzy goals for both objectives and the aggregation function are introduced in a solution approach that considers the decision maker's preference and the subjectivity of her or his judgment in multicriteria decision making. The fuzzy bicriteria problem is transformed into a constrained shortest path problem. Furthermore, an exact algorithm based on Dijkstra's algorithm and a heuristic algorithm based on the Lagrange relaxation-based algorithm are developed for the constrained shortest path problem.
机译:本文提出了一种具有随机边缘成本的鲁棒最短路径问题的模型。提出的模型被公式化为双标准优化问题,以最小化从起始节点到结束节点的路径的总成本,并最大化置信区间的范围,以满足该区域中任何路径的总成本的条件就鲁棒性而言,小于目标值。当目标彼此冲突时,并不总是存在同时优化所有目标的解决方案。在本文中,在解决方案方法中引入了针对目标和聚合函数的模糊目标,该方法考虑了决策者在多准则决策中的偏好以及其判断的主观性。模糊双准则问题转化为约束最短路径问题。此外,针对约束最短路径问题,开发了一种基于Dijkstra算法的精确算法和一种基于基于Lagrange松弛算法的启发式算法。

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