39 research outputs found
Exact solution approaches for the discrete -neighbor -center problem
The discrete -neighbor -center problem (d--CP) is an
emerging variant of the classical -center problem which recently got
attention in literature. In this problem, we are given a discrete set of points
and we need to locate facilities on these points in such a way that the
maximum distance between each point where no facility is located and its
-closest facility is minimized. The only existing algorithms in
literature for solving the d--CP are approximation algorithms and
two recently proposed heuristics.
In this work, we present two integer programming formulations for the
d--CP, together with lifting of inequalities, valid inequalities,
inequalities that do not change the optimal objective function value and
variable fixing procedures. We provide theoretical results on the strength of
the formulations and convergence results for the lower bounds obtained after
applying the lifting procedures or the variable fixing procedures in an
iterative fashion. Based on our formulations and theoretical results, we
develop branch-and-cut (B&C) algorithms, which are further enhanced with a
starting heuristic and a primal heuristic.
We evaluate the effectiveness of our B&C algorithms using instances from
literature. Our algorithms are able to solve 116 out of 194 instances from
literature to proven optimality, with a runtime of under a minute for most of
them. By doing so, we also provide improved solution values for 116 instances
A New General-Purpose Algorithm for Mixed-Integer Bilevel Linear Programs
International audienceBilevel optimization problems are very challenging optimization models arising in many important practical contexts, including pricing mechanisms in the energy sector, airline and telecommunication industry, transportation networks, critical infrastructure defense, and machine learning. In this paper, we consider bilevel programs with continuous and discrete variables at both levels, with linear objectives and constraints (continuous upper level variables, if any, must not appear in the lower level problem). We propose a general-purpose branch-and-cut exact solution method based on several new classes of valid inequalities, which also exploits a very effective bilevel-specific preprocessing procedure. An extensive computational study is presented to evaluate the performance of various solution methods on a common testbed of more than 800 instances from the literature and 60 randomly generated instances. Our new algorithm consistently outperforms (often by a large margin) alternative state-of-the-art methods from the literature, including methods exploiting problem-specific information for special instance classes. In particular, it solves to optimality more than 300 previously unsolved instances from the literature. To foster research on this challenging topic, our solver is made publicly available online
An outer approximation algorithm for multi-objective mixed-integer linear and non-linear programming
In this paper, we present the first outer approximation algorithm for
multi-objective mixed-integer linear programming problems with any number of
objectives. The algorithm also works for certain classes of non-linear
programming problems. It produces the non-dominated extreme points as well as
the facets of the convex hull of these points. The algorithm relies on an
oracle which solves single-objective weighted-sum problems and we show that the
required number of oracle calls is polynomial in the number of facets of the
convex hull of the non-dominated extreme points in the case of multiobjective
mixed-integer programming (MOMILP). Thus, for MOMILP problems for which the
weighted-sum problem is solvable in polynomial time, the facets can be computed
with incremental-polynomial delay. From a practical perspective, the algorithm
starts from a valid lower bound set for the non-dominated extreme points and
iteratively improves it. Therefore it can be used in multi-objective
branch-and-bound algorithms and still provide a valid bound set at any stage,
even if interrupted before converging. Moreover, the oracle produces Pareto
optimal solutions, which makes the algorithm also attractive from the primal
side in a multi-objective branch-and-bound context. Finally, the oracle can
also be called with any relaxation of the primal problem, and the obtained
points and facets still provide a valid lower bound set. A computational study
on a set of benchmark instances from the literature and new non-linear
multi-objective instances is provided.Comment: 21 page
On SOCP-based disjunctive cuts for solving a class of integer bilevel nonlinear programs
We study a class of integer bilevel programs with second-order cone
constraints at the upper-level and a convex-quadratic objective function and
linear constraints at the lower-level. We develop disjunctive cuts (DCs) to
separate bilevel-infeasible solutions using a second-order-cone-based
cut-generating procedure. We propose DC separation strategies and consider
several approaches for removing redundant disjunctions and normalization. Using
these DCs, we propose a branch-and-cut algorithm for the problem class we
study, and a cutting-plane method for the problem variant with only binary
variables.
We present an extensive computational study on a diverse set of instances,
including instances with binary and with integer variables, and instances with
a single and with multiple linking constraints. Our computational study
demonstrates that the proposed enhancements of our solution approaches are
effective for improving the performance. Moreover, both of our approaches
outperform a state-of-the-art generic solver for mixed-integer bilevel linear
programs that is able to solve a linearized version of our binary instances.Comment: arXiv admin note: substantial text overlap with arXiv:2111.0682
The connected facility location polytope
We analyze the polytope associated with a combinatorial problem that combines the Steiner tree problem and the uncapacitated facility location problem. The problem, called connected facility location problem, is motivated by a real-world application in the design of a telecommunication network, and concerns with deciding the facilities to open, the assignment of customers to open facilities, and the connection of the open facilities through a Steiner tree. Several solution approaches are proposed in the literature, and the contribution of our work is a polyhedral analysis for the problem. We compute the dimension of the polytope, present valid inequalities, and analyze conditions for these inequalities to be facet defining. Some inequalities are taken from the Steiner tree polytope and the uncapacitated facility location polytope. Other inequalities are new
An algorithmic framework for the exact solution of tree-star problems
Many problems arising in the area of telecommunication ask for solutions with a tree-star topology. This paper proposes a general procedure for finding optimal solutions to a family of these problems. The family includes problems in the literature named as connected facility location, rent-or-buy and generalized Steiner tree-star. We propose a solution framework based on a branch-and-cut algorithm which also relies on sophisticated reduction and heuristic techniques. An important ingredient of this framework is a dual ascent procedure for asymmetric connected facility location. This paper shows how this procedure can be exploited in combination with various mixed integer programming formulations. Using the new framework, many benchmark instances in the literature for which only heuristic results were available so far, can be solved to provable optimality within seconds. To better assess the computational performance of the new approach, we additionally consider larger instances and provide optimal solutions for most of them too