Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)

Capacitated Network Design - Multi-Commodity Flow Formulations, Cutting Planes, and Demand Uncertainty

Please always quote using this URN: urn:nbn:de:kobv:83-opus-36167
  • In this thesis, we develop methods in mathematical optimization to dimension networks at minimal cost. Given hardware and cost models, the challenge is to provide network topologies and efficient capacity plans that meet the demand for network traffic (data, passengers, freight). We incorporate crucial aspects of practical interest such as the discrete structure of available capacities as well as the uncertainty of demand forecasts. The considered planning problems typically arise in the strategic design of telecommunication or public transport networks and also in logistics. One of the essential aspects studied in this work is the use of cutting planes to enhance solution approaches based on multi-commodity flow formulations. Providing theoretical and computational evidence for the efficacy of inequalities based on network cuts, we extend existing theory and algorithmic work in different directions. First, we prove that special-purpose techniques, originally designed to solve capacitated network design problems, can be successfully integrated into general-purpose mixed integer programming (MIP) solvers. Our approach relies on an automatic detection of network structure within the constraint matrix of general mixed in teger programs. More precisely, we identify multi-commodity (MCF) network sub-matrices and resolve the isomorphisms of the commodity blocks as well as the original graph structure. In the subsequent separation framework, we guide the constraint aggregation of available cutting plane procedures (e. g. based on mixed integer rounding) to produce strong cutting planes that reflect the structure of the constructed network. The new MCF-separator integrates network design specific methodology into general optimization tools which is of particular importance for practitioners that tend to use MIP solvers as black boxes. Extensive computational tests show that our network detection procedure operates accurately and reliably. Moreover, due to the generated cutting planes, we achieve an average speed-up of a factor of two for pure network design problems with general MIP solvers. Many of these instances can only be solved to optimality in reasonable time if the new MCF-separator is active. In 9 % of the instances of general MIP test sets we find consistent embedded networks and generate violated inequalities. In this case the computation time decreases by 18 % on average with almost no degradation for unaffected instances. Second, we generalize concepts, models, and cutting planes from deterministic network design to robust network design, incorporating the uncertainty of traffic demands. We enhance and compare strategies that are able to handle a polyhedral set of different traffic scenarios. In particular, we consider two correlated solution methods, based on separating extreme demand scenarios and dualizing the linear description of the demand polytope, respectively. We consider robust network design as two-stage robust optimization with recourse. First stage capacity decisions are fixed for all scenarios while the second stage flow depends on the realized demands. In order to reroute the traffic as a function of the demand dynamics, we consider three alternative recourse actions, namely, static, affine, and dynamic routing. We analyze properties of the new affine routing and show that it combines advantages of the well-known static and dynamic models. Using the concept of robust cut-set polyhedra and the corresponding lifting theorems, we develop several classes of facet-defining inequalities based on network cuts that can be used to further accelerate solution strategies for robust network design. Among them are the well-known (flow) cut-set inequalities, which we generalize to general demand polytopes, but also new classes of potential cutting planes, so-called envelope inequalities. The practical importance of the developed cutting planes is revealed by a series of computational tests. Similar to the results for the MCF-separator we achieve speed-ups of two and more using the generalized classes of strong inequalities. To evaluate the robustness of solutions that are computed with our framework we use real-life measurements of traffic dynamics from different existing telecommunication networks, among them data from the German and the European research network. Our results indicate that traffic peaks do not necessarily occur all simultaneously with respect to different source-destination pairs, which is of practical importance for the design of uncertainty sets. It is, in particular, not necessary to dimension networks for a scenario that assumes all source-destination traffic is at its peak simultaneously. With our solutions we save up to 20 % of the corresponding solution cost compared to this artificial scenario and achieve comparable levels of robustness.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics - number of accesses to the document
Metadaten
Author:Christian Raack
Document Type:Doctoral Thesis
Tag:combinatorial optimization; cutting planes; integer programming; network design; robustness
Granting Institution:Technische Universität Berlin
Advisor:Martin Grötschel
Date of final exam:2012/06/26
Publishing Institution:Technische Universität Berlin
Year of first publication:2012
URL:http://opus4.kobv.de/opus4-tuberlin/frontdoor/index/index/docId/3407
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.