Decomposition and Dynamic Cut Generation in Integer Linear Programming

Decomposition algorithms such as Lagrangian relaxation and Dantzig-Wolfe decomposition are well-known methods that can be used to generate bounds for mixed-integer linear programming problems. Traditionally, these methods have been viewed as distinct from polyhedral methods, in which bounds are obtained by dynamically generating valid inequalities to strengthen the linear programming relaxation. Recently, a number of … Read more

The Quadratic Selective Travelling Saleman Problem

A well-known extension of the Travelling Salesman Problem (TSP) is the Selective TSP (STSP): Each node has an associated profit and instead of visiting all nodes, the most profitable set of nodes, taking into account the tour cost, is visited. The Quadratic STSP (QSTSP) adds the additional complication that each pair of nodes have an … Read more

Polyhedral Analysis for Concentrator Location Problems

The concentrator location problem is to choose a subset of a given terminal set to install concentrators and to assign each remaining terminal node to a concentrator to minimize the cost of installation and assignment. The concentrators may have capacity constraints. We study the polyhedral properties of concentrator location problems with different capacity structures. We … Read more

Computational study of a cutting plane algorithm for University Course Timetabling

In this paper we describe a successful case-study where a Branch-and-Cut algorithm yields the \lq\lq optimal” solution of a real-world timetabling problem of University courses \emph{(University Course Timetabling problem)}. The problem is formulated as a \emph{Set Packing problem} with side constraints. To tighten the initial formulation, we utilize well-known valid inequalities of the Set Packing … Read more

A Branch and Cut Algorithm for Hub Location Problems with Single Assignment

The hub location problem with single assignment is the problem of locating hubs and assigning the terminal nodes to hubs in order to minimize the cost of hub installation and the cost of routing the traffic in the network. There may also be capacity restrictions on the amount of traffic that can transit by hubs. … Read more

Computational study of large-scale p-Median problems

Given a directed graph G(V,A), the p-Median problem consists of determining p nodes (the median nodes) minimizing the total distance from the other nodes of the graph. We present a Branch-and-Cut algorithm yielding provably good solutions for instances up to 3795 nodes (14,402,025 variables). Key ingredients of our approach are: lagrangian relaxation, a simple procedure … Read more

Two-connected networks with rings of bounded cardinality

We study the problem of designing at minimum cost a two-connected network such that each edge belongs to a cycle using at most K edges. This problem is a particular case of the two-connected networks with bounded meshes problem studied by Fortz, Labbé and Maffioli. In this paper, we compute a lower bound on the … Read more

Safe bounds in linear and mixed-integer programming

Current mixed-integer linear programming solvers are based on linear programming routines that use floating point arithmetic. Occasionally, this leads to wrong solutions, even for problems where all coefficients and all solution components are small integers. It is shown how, using directed rounding and interval arithmetic, cheap pre- and postprocessing of the linear programs arising in … Read more

Search and Cut: New Class of Cutting Planes for 0-1 Programming

The basic principle of the cutting plane techniques is to chop away the portions of the solution space of the linear programming relaxation of an integer program that contain no integer solutions. this is true for both Gomory’s cutting planes, and other more recent cuts based on valid inequalities. Obtaining a partial or full description … Read more

Branch and cut based on the volume algorithm: Steiner trees in graphs and max-cut

We present a Branch-and-Cut algorithm where the Volume Algorithm is applied to the linear programming relaxations arising at each node of the search tree. This means we use fast approximate solutions to these linear programs instead of exact but slower solutions given by the traditionally used dual simplex method. Our claim is that such a … Read more