A multi-objective perspective on the cable-trench problem

IF 1.1 4区 数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Combinatorial Optimization Pub Date : 2025-04-26 DOI:10.1007/s10878-025-01289-0
Lara Löhken, Michael Stiglmayr
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Abstract

The cable-trench problem is defined as a linear combination of the shortest path and the minimum spanning tree problem. In particular, the goal is to find a spanning tree that simultaneously minimizes its total length and the total path length from a pre-defined root to all other vertices. Both, the minimum spanning tree and the shortest path problem are known to be efficiently solvable. However, a linear combination of these two objectives results in a highly complex problem. In this article, we introduce the bi-objective cable-trench problem which separates the two cost functions. We show that in general, the bi-objective formulation has additional compromise solutions compared to the cable-trench problem in its original formulation. To determine the set of non-dominated points and efficient solutions, we use \(\varepsilon \)-constraint scalarizations in combination with a problem-specific cutting plane. Moreover, we present numerical results on different types of graphs analyzing the impact of density and cost structure on the cardinality of the non-dominated set and the solution time.

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电缆沟问题的多目标视角
电缆沟问题被定义为最短路径和最小生成树问题的线性组合。具体来说,其目标是找到一棵生成树,同时使其总长度和从预定根到所有其他顶点的总路径长度最小。众所周知,最小生成树问题和最短路径问题都是可以有效求解的。然而,这两个目标的线性组合会产生一个非常复杂的问题。在本文中,我们引入了双目标电缆沟问题,它将两个成本函数分开。我们的研究表明,一般来说,与电缆沟问题的原始表述相比,双目标表述有更多的折中方案。为了确定非支配点集和有效解,我们将 \(\varepsilon \)-约束标量化与特定问题的切割平面相结合。此外,我们还给出了不同类型图形的数值结果,分析了密度和成本结构对非支配集的卡片数和求解时间的影响。
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来源期刊
Journal of Combinatorial Optimization
Journal of Combinatorial Optimization 数学-计算机:跨学科应用
CiteScore
2.00
自引率
10.00%
发文量
83
审稿时长
6 months
期刊介绍: The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering. The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.
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