A composite very large-scale neighborhood structure for the capacitated minimum spanning tree problem

  • Authors:

  • Ravindra K. Ahuja;James B. Orlin;Dushyant Sharma

  • Affiliations:

  • Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA;Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA

  • Venue:
  • Operations Research Letters
  • Year:
  • 2003

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Abstract

The capacitated minimum spanning tree (CMST) problem is to find a minimum cost spanning tree in a network where nodes have specified demands, with an additional capacity constraints on the subtrees incident to a given source node s. The capacitated minimum spanning tree problem arises as an important subproblem in many telecommunication network design problems. In a recent paper, Ahuja et al. (Math. Program. 91 (2001) 71) proposed two very large-scale neighborhood search algorithms for the capacitated minimum spanning tree problem. Their first node-based neighborhood structure is obtained by performing multi-exchanges involving several trees where each tree contributes a single node. Their second tree-based neighborhood structure is obtained by performing multi-exchanges where each tree contributes a subtree. The computational investigations found that node-based multi-exchange neighborhood gives the best performance for the homogenous demand case (when all nodes have the same demand), and the tree-based multi-exchange neighborhood gives the best performance for the heterogeneous demand case (when nodes may have different demands). In this paper, we propose a composite neighborhood structure that subsumes both the node-based and tree-based neighborhoods, and outperforms both the previous neighborhood search algorithms for solving the capacitated minimum spanning tree problem on standard benchmark instances. We also develop improved dynamic programming based exact algorithms for searching the composite neighborhood.