最大限度地减少从源节点到需求节点的费用传输时间

IF 0.9 4区 数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Mehdi Ghiyasvand, Iman Keshtkar
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引用次数: 0

摘要

一个无向图(G=(V,A))由一个由n个节点组成的集合V,一个由m条边组成的集合A,以及由源节点和需求节点组成的两个集合(S,D,subseteq V)组成。本文提出了两个新版本的定位问题,它们被称为(f(\sigma)\)定位问题和(g(\sigma)\)定位问题。我们将网络 N 的 (f(sigma)/location)定义为一个节点 (s(sigma)/in S\) ,其特性是从节点 s 到 D 的目的地的最大花费传输时间越短越好。(f(\sigma)\)定位问题将范围 \((0,\infty)\)划分为区间 \(\displaystyle\cup _{i}{(a_i. b_i)}) 和区间 \((0,\infty)\)、b_i)}\) 并为每个区间 \((a_i,b_i)\)找到一个源 \(s_i\in S\), 使得 \(s_i\) 对于每个 \(\sigma \in (a_i,b_i)\) 都是一个 \(f(\sigma)\)位置。另外,定义一个 \(g(\sigma )\)-location 为 S 的一个节点 s,其属性是从节点 s 到 D 的所有目的地的花费传输时间之和尽可能便宜。(g(\sigma)\)-定位问题将范围 \((0,\infty)\)划分为区间 \(\displaystyle\cup _{i}{(a_i. b_i)}) 和区间 \((0,\infty)\)、b_i)}\) 并为每个区间 \((a_i,b_i)\)找到一个源 \(s_i\in S\), 使得 \(s_i\) 对于每个 \(\sigma \in (a_i,b_i)\) 都是一个 \(g(\sigma)\)位置。本文提出了两种强多项式时间算法来解决 \(f(\sigma )\)-location 和 \(g(\sigma )\)-location 问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Minimizing the expense transmission time from the source node to demand nodes

Minimizing the expense transmission time from the source node to demand nodes

An undirected graph \(G=(V,A)\) by a set V of n nodes, a set A of m edges, and two sets \(S,\ D\subseteq V\) consists of source and demand nodes are given. This paper presents two new versions of location problems which are called the \(f(\sigma )\)-location and \(g(\sigma )\)-location problems. We define an \(f(\sigma )\)-location of the network N as a node \(s\in S\) with the property that the maximum expense transmission time from the node s to the destinations of D is as cheap as possible. The \(f(\sigma )\)-location problem divides the range \((0,\infty )\) into intervals \(\displaystyle \cup _{i}{(a_i,b_i)}\) and finds a source \(s_i\in S\), for each interval \((a_i,b_i)\), such that \(s_i\) is a \(f(\sigma )\)-location for each \(\sigma \in (a_i,b_i)\). Also, define a \(g(\sigma )\)-location as a node s of S with the property that the sum of expense transmission times from the node s to all destinations of D is as cheap as possible. The \(g(\sigma )\)-location problem divides the range \((0,\infty )\) into intervals \(\displaystyle \cup _{i}{(a_i,b_i)}\) and finds a source \(s_i\in S\), for each interval \((a_i,b_i)\), such that \(s_i\) is a \(g(\sigma )\)-location for each \(\sigma \in (a_i,b_i)\). This paper presents two strongly polynomial time algorithms to solve \(f(\sigma )\)-location and \(g(\sigma )\)-location problems.

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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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