Capacitated Domination: Problem Complexity and Approximation Algorithms

Mong Jen Kao*, Han Lin Chen, D. T. Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


We consider a local service-requirement assignment problem named capacitated domination from an algorithmic point of view. In this problem, we are given a graph with three parameters defined on each vertex, which are the cost, the capacity, and the demand, of a vertex, respectively. A vertex can be chosen multiple times in order to generate sufficient capacity for the demands of the vertices in its closed neighborhood. The objective of this problem is to compute a demand assignment of minimum cost such that the demand of each vertex is fully-served by some of its closed neighbors without exceeding the amount of capacity they provide.In this paper, we provide complexity results as well as several approximation algorithms to compose a comprehensive study for this problem. First, we provide logarithmic approximations for general graphs which are asymptotically optimal. From the perspective of parameterized complexity, we show that this problem is W[1]-hard with respect to treewidth and solution size. Moreover, we show that this problem is fixed-parameter tractable with respect to treewidth and the maximum capacity of the vertices. The latter result implies a pseudo-polynomial time approximation scheme for planar graphs under a standard framework.In order to drop the pseudo-polynomial factor, we develop a constant-factor approximation for planar graphs, based on a new perspective which we call general ladders on the hierarchical structure of outer-planar graphs. We believe that the approach we use can be applicable to other capacitated covering problems.

Original languageEnglish
Issue number1
StatePublished - 1 May 2015


  • Approximation algorithms
  • Capacitated domination
  • Dominating set
  • Graph theory


Dive into the research topics of 'Capacitated Domination: Problem Complexity and Approximation Algorithms'. Together they form a unique fingerprint.

Cite this