Multi-Commodity Multi-Facility Network Design

TL;DR

This paper reviews advances in polyhedral techniques for multi-commodity network design models, focusing on inequalities and relaxations that improve solution methods for large-scale problems.

Contribution

It provides a comprehensive review of fundamental polyhedral techniques and inequalities used in solving multi-commodity network design problems.

Findings

Polyhedral inequalities improve branch-and-bound algorithms.

Techniques like metric inequalities and mixed-integer rounding are effective.

Network shrinking simplifies complex models for better solvability.

Abstract

We consider multi-commodity network design models, where capacity can be added to the arcs of the network using multiples of facilities that may have different capacities. This class of mixed-integer optimization models appears frequently in telecommunication network capacity expansion problems, train scheduling with multiple locomotive options, supply chain, and service network design problems. Valid inequalities used as cutting planes in branch-and-bound algorithms have been instrumental in solving large-scale instances. We review the progress that has been done in polyhedral investigations in this area by emphasizing three fundamental techniques. These are the metric inequalities for projecting out continuous flow variables, mixed-integer rounding from appropriate base relaxations and shrinking the network to a small $k$-node graph. The basic inequalities derived from arc-set, cut-set and partition relaxations of the network are also extensively utilized with certain modifications in robust and survivable network design problems.