@article{BaermannLiersMartinetal.2015, author = {B{\"a}rmann, Andreas and Liers, Frauke and Martin, Alexander and Merkert, Maximilian and Thurner, Christoph and Weninger, Dieter}, title = {Solving network design problems via iterative aggregation}, series = {Mathematical Programming Computation}, volume = {7}, journal = {Mathematical Programming Computation}, number = {2}, doi = {10.1007/s12532-015-0079-1}, pages = {189 -- 217}, year = {2015}, abstract = {In this work, we present an exact approach for solving network design problems that is based on an iterative graph aggregation procedure. The scheme allows existing preinstalled capacities. Starting with an initial aggregation, we solve a sequence of network design master problems over increasingly fine-grained representations of the original network. In each step, a subproblem is solved that either proves optimality of the solution or gives a directive where to refine the representation of the network in the subsequent iteration. The algorithm terminates with a globally optimal solution to the original problem. Our implementation uses a standard integer programming solver for solving the master problems as well as the subproblems. The computational results on random and realistic instances confirm the profitable use of the iterative aggregation technique. The computing time often reduces drastically when our method is compared to solving the original problem from scratch.}, language = {en} } @article{LiersMerkert2015, author = {Liers, Frauke and Merkert, Maximilian}, title = {Structural Investigation of Piecewise Linearized Network Flow Problems}, volume = {26}, doi = {10.1137/15M1006751}, pages = {2863 -- 2886}, year = {2015}, abstract = {In this work we study polyhedra in the context of network flow problems, where the flow value on each arc lies in one of several predefined intervals. This is motivated by nonlinear problems on transportation networks, where nonlinearities are handled by piecewise linear approximation or relaxation - a common and established approach in many applications. Several methods for modeling piecewise linear functions are known which provide a complete description for a single network arc. However, in general this property is lost when considering multiple arcs. We show how to strengthen the formulation for specific substructures consisting of multiple arcs by linear inequalities. For the case of paths of degree-two-nodes we give a complete description of the polyhedron projected to the integer variables. Our model is based on - but not limited to - the multiple choice method; we also show how to transfer our results to a formulation based on the incremental method. Computational results show that a state-of-the-art MIP-solver greatly benefits from using our cutting planes for random and realistic network topologies.}, language = {en} } @unpublished{BaermannGellermannMerkertetal.2016, author = {B{\"a}rmann, Andreas and Gellermann, Thorsten and Merkert, Maximilian and Schneider, Oskar}, title = {Staircase Compatibility and its Applications in Scheduling and Piecewise Linearization}, pages = {24}, year = {2016}, abstract = {We consider the clique problem with multiple-choice constraints (CPMC) and characterize a case where it is possible to give an efficient description of the convex hull of its feasible solutions. This case, which we call staircase compatibility, generalizes common properties in applications and allows for a linear description of the integer feasible solutions to (CPMC) with a totally unimodular constraint matrix of polynomial size. We derive two such totally unimodular reformulations for the problem: one that is obtained by a strengthening of the compatibility constraints and one that is based on a representation as a dual network flow problem. Furthermore, we show a natural way to derive integral solutions from fractional solutions to the problem by determining integral extreme points generating this fractional solution. We also evaluate our reformulations from a computational point of view by applying them to two different real-world applications. The first one is a problem in railway timetabling where we try to adapt a given timetable slightly such that energy costs from operating the trains are reduced. The second one is the piecewise linearization of non-linear flow problems on a gas network. In both cases, we are able to reduce the solution times significantly by passing to the theoretically stronger formulations of the problem.}, language = {en} } @unpublished{LiersMartinMerkertetal.2020, author = {Liers, Frauke and Martin, Alexander and Merkert, Maximilian and Mertens, Nick and Michaels, Dennis}, title = {Solving Mixed-Integer Nonlinear Optimization Problems using Simultaneous Convexification - a Case Study for Gas Networks}, pages = {36}, year = {2020}, abstract = {Solving mixed-integer nonlinear optimization problems (MINLPs) to global optimality is extremely challenging. An important step for enabling their solution consists in the design of convex relaxations of the feasible set. Known solution approaches based on spatial branch-and-bound become more effective the tighter the used relaxations are. Relaxations are commonly established by convex underestimators, where each constraint function is considered separately. Instead, a considerably tighter relaxation can be found via so-called simultaneous convexification, where convex underestimators are derived for more than one constraint function at a time. In this work, we present a global solution approach for solving mixed-integer nonlinear problems that uses simultaneous convexification. We introduce a separation method that relies on determining the convex envelope of linear combinations of the constraint functions and on solving a nonsmooth convex problem. In particular, we apply the method to quadratic absolute value functions and derive their convex envelopes. The practicality of the proposed solution approach is demonstrated on several test instances from gas network optimization, where the method outperforms standard approaches that use separate convex relaxations.}, language = {en} }