Faster algorithms for evacuation problems in networks with a single sink of small degree and bounded capacitated edges

IF 0.9 4区 数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Yuya Higashikawa, Naoki Katoh, Junichi Teruyama, Yuki Tokuni
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引用次数: 0

Abstract

In this paper, we propose new algorithms for evacuation problems defined on dynamic flow networks. A dynamic flow network is a directed graph in which source nodes are given supplies and a single sink node is given a demand. The evacuation problem seeks a dynamic flow that sends all supplies from sources to the sink such that its demand is satisfied in the minimum feasible time horizon. For this problem, the current best algorithms are developed by Schlöter (2018) and Kamiyama (2019), which run in strongly polynomial time but with high-order polynomial time complexity because they use submodular function minimization as a subroutine. In this paper, we propose new algorithms that do not explicitly execute submodular function minimization, and we prove that they are faster than the current best algorithms when an input network is restricted such that the sink has a small in-degree and every edge has the same capacity.

Abstract Image

具有小度单汇和有界容纳边的网络中疏散问题的更快算法
在本文中,我们针对动态流动网络上定义的疏散问题提出了新的算法。动态流动网络是一个有向图,其中源节点给出供应,单个汇节点给出需求。疏散问题寻求的是一种动态流,它能将所有供应从源节点发送到汇节点,从而在最小可行时间范围内满足其需求。对于这个问题,目前最好的算法是由 Schlöter (2018) 和 Kamiyama (2019) 开发的,其运行时间为强多项式时间,但具有高阶多项式时间复杂度,因为它们使用了子模函数最小化作为子程序。在本文中,我们提出了不显式执行子模函数最小化的新算法,并证明当输入网络受到限制,使得汇的内度很小且每条边的容量相同时,这些算法比目前最好的算法更快。
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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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