Graph Generation: A New Approach to Solving Expanded Linear Programming Relaxations

TL;DR

This paper introduces Graph Generation, a novel enhancement to Column Generation that accelerates solving expanded LP relaxations in mixed integer programs by efficiently representing columns as directed acyclic graphs, demonstrated on vehicle routing.

Contribution

The paper presents a new Graph Generation technique that improves convergence speed of Column Generation without weakening LP relaxations, applicable to resource constrained shortest path problems.

Findings

Faster convergence of Column Generation in vehicle routing.

Graph Generation maintains LP relaxation strength.

Effective application to classical vehicle routing problem.

Abstract

In this article we introduce Graph Generation, an enhanced Column Generation (CG) algorithm for solving expanded linear programming relaxations of mixed integer linear programs. To apply Graph Generation, we must be able to map any given column to a small directed acyclic graph for which any path from source to sink describes a feasible column. This structure is easily satisfied for vehicle routing and crew scheduling problems; and other such problems where pricing is a resource constrained shortest path problem. Such graphs are then added to the restricted master problem (RMP) when the corresponding column is generated during pricing. The use of Graph Generation does not weaken the linear programming relaxation being solved. At any given iteration of CG enhanced by Graph Generation; the technique permits the RMP to express a much wider set of columns than those generated during pricing, leading to faster convergence of CG. Graph Generation does not change the structure of the CG pricing problem. We show how the method can be applied in a general way, and then demonstrate the effectiveness of our approach on the classical Capacitated Vehicle Routing Problem.