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Wed, 23 Mar 2016 16:01:15 +0100Wed, 23 Mar 2016 16:01:15 +0100Approximation of Ellipsoids Using Bounded Uncertainty Sets
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4344
In this paper, we discuss the problem of approximating ellipsoid uncertainty sets with bounded (gamma) uncertainty sets. Robust linear programs with ellipsoid uncertainty lead to quadratically constrained programs, whereas robust linear programs with bounded uncertainty sets remain linear programs which are generally easier to solve.
We call a bounded uncertainty set an inner approximation of an ellipsoid if it is contained in it. We consider two different inner approximation problems. The first problem is to find a bounded uncertainty set which sticks close to the ellipsoid such that a shrank version of the ellipsoid is contained in it. The approximation is optimal if the required shrinking is minimal. In the second problem, we search for a bounded uncertainty set within the ellipsoid with maximum volume. We present how both problems can be solved analytically by stating explicit formulas for the optimal solutions of these problems.
Further, we present in a computational experiment how the derived approximation techniques can be used to approximate shortest path and network flow problems which are affected by ellipsoidal uncertainty.André Chasseinpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4344Wed, 23 Mar 2016 16:01:15 +0100Nonsmooth Contact Dynamics for the Large-Scale Simulation of Granular Material
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4305
For the prediction of digging forces from a granular material simulation, the
Nonsmooth Contact Dynamics Method is examined. First, the equations of motion
for nonsmooth mechanical systems are laid out. They are a differential
variational inequality that has the same structure as classical discrete algebraic equations. Using a Galerkin projection in time, it becomes possible to derive
nonsmooth versions of the classical SHAK and RATTLE integrators.
A matrix-free Interior Point Method is used for the complementarity
problems that need to be solved in every time step. It is shown that this method
outperforms the Projected Gauss-Jacobi method by several orders of magnitude
and produces the same digging force result as the Discrete Element Method in comparable computing time.Jan Kleinert; Bernd Simeon; Klaus Dresslerpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4305Wed, 24 Feb 2016 15:37:37 +0100Zone-based, Robust Flood Evacuation Planning
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4297
We consider the problem to evacuate several regions due to river flooding, where sufficient time is given to plan ahead. To ensure a smooth evacuation procedure, our model includes the decision which regions to assign to which shelter, and when evacuation orders should be issued, such that roads do not become congested.
Due to uncertainty in weather forecast, several possible scenarios are simultaneously considered in a robust optimization framework. To solve the resulting integer program, we apply a Tabu search algorithm based on decomposing the problem into better tractable subproblems. Computational experiments on random instances and an instance based on Kulmbach, Germany, data show considerable improvement compared to an MIP solver provided with a strong starting solution.Sabine Büttner; Marc Goerigkpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4297Wed, 03 Feb 2016 11:29:07 +0100Ranking Robustness and its Application to Evacuation Planning
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4296
We present a new approach to handle uncertain combinatorial optimization problems that uses solution ranking procedures to determine the degree of robustness of a solution. Unlike classic concepts for robust optimization, our approach is not purely based on absolute quantitative performance, but also includes qualitative aspects that are of major importance for the decision maker.
We discuss the two variants, solution ranking and objective ranking robustness, in more detail, presenting problem complexities and solution approaches. Using an uncertain shortest path problem as a computational example, the potential of our approach is demonstrated in the context of evacuation planning due to river flooding.Marc Goerigk; Horst Hamacher; Anika Kinscherffpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4296Wed, 03 Feb 2016 11:26:20 +0100Global existence for a go-or-grow multiscale model for tumor invasion with therapy
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4294
We investigate a PDE-ODE system describing cancer cell invasion in a tissue network. The model is an extension of the multiscale setting in [28,40], by considering two subpopulations of tumor cells interacting mutually and with the surrounding tissue. According to the go-or-grow hypothesis, these subpopulations consist of moving and proliferating cells, respectively. The mathematical setting also accommodates the effects of some therapy approaches. We prove the global existence of weak solutions to this model and perform numerical simulations to illustrate its behavior for different therapy strategies.Christian Stinner; Christina Surulescu; Aydar Uataypreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4294Mon, 01 Feb 2016 09:21:08 +0100Global existence for a degenerate haptotaxis model of cancer invasion
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4232
We propose and study a strongly coupled PDE-ODE system with tissue-dependent degenerate diffusion and haptotaxis that can serve as a model prototype for cancer cell invasion through the
extracellular matrix. We prove the global existence of weak solutions and illustrate the model behaviour by numerical simulations for a two-dimensional setting.Anna Zhigun; Christina Surulescu; Aydar Uataypreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4232Mon, 30 Nov 2015 08:35:47 +0100Performance Analysis in Robust Optimization
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4227
We discuss the problem of evaluating a robust solution.
To this end, we first give a short primer on how to apply robustification approaches to uncertain optimization problems using the assignment problem and the knapsack problem as illustrative examples.
As it is not immediately clear in practice which such robustness approach is suitable for the problem at hand,
we present current approaches for evaluating and comparing robustness from the literature, and introduce the new concept of a scenario curve. Using the methods presented in this paper, an easy guide is given to the decision maker to find, solve and compare the best robust optimization method for his purposes.André Chassein; Marc Goerigkpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4227Wed, 18 Nov 2015 08:25:22 +0100Minimizing the Number of Apertures in Multileaf Collimator Sequencing with Field Splitting
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4206
In this paper we consider the problem of decomposing a given integer matrix A into
a positive integer linear combination of consecutive-ones matrices with a bound on the
number of columns per matrix. This problem is of relevance in the realization stage
of intensity modulated radiation therapy (IMRT) using linear accelerators and multileaf
collimators with limited width. Constrained and unconstrained versions of the problem
with the objectives of minimizing beam-on time and decomposition cardinality are considered.
We introduce a new approach which can be used to find the minimum beam-on
time for both constrained and unconstrained versions of the problem. The decomposition
cardinality problem is shown to be NP-hard and an approach is proposed to solve the
lexicographic decomposition problem of minimizing the decomposition cardinality subject
to optimal beam-on time.Horst W. Hamacher; Ines M. Raschendorfer; Davaatseren Baatar; Matthias Ehrgottpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4206Wed, 28 Oct 2015 14:33:01 +0100Robust storage loading problems with stacking and payload constraints
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4158
We consider storage loading problems where items with uncertain weights have
to be loaded into a storage area, taking into account stacking and
payload constraints. Following the robust optimization paradigm, we propose
strict and adjustable optimization models for finite and interval-based
uncertainties. To solve these problems, exact decomposition and heuristic
solution algorithms are developed.
For strict robustness, we also present a compact formulation based
on a characterization of worst-case scenarios.
Computational results show that computation times and algorithm
gaps are reasonable for practical applications.
Furthermore, we find that the robustness concepts show different
potential depending on the type of data being used.
Marc Goerigk; Sigrid Knust; Xuan Thanh Lepreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4158Tue, 18 Aug 2015 08:23:49 +0200Discrete Parallel Machine Makespan ScheLoc Problem
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4129
Scheduling-Location (ScheLoc) Problems integrate the separate fields of
scheduling and location problems. In ScheLoc Problems the objective is to
find locations for the machines and a schedule for each machine subject to
some production and location constraints such that some scheduling object-
ive is minimized. In this paper we consider the Discrete Parallel Machine
Makespan (DPMM) ScheLoc Problem where the set of possible machine loc-
ations is discrete and a set of n jobs has to be taken to the machines and
processed such that the makespan is minimized. Since the separate location
and scheduling problem are both NP-hard, so is the corresponding ScheLoc
Problem. Therefore, we propose an integer programming formulation and
different versions of clustering heuristics, where jobs are split into clusters
and each cluster is assigned to one of the possible machine locations. Since
the IP formulation can only be solved for small scale instances we propose
several lower bounds to measure the quality of the clustering heuristics. Ex-
tensive computational tests show the efficiency of the heuristics.Corinna Heßler; Kaouthar Deghdakpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4129Tue, 28 Jul 2015 09:40:15 +0200A new solution approach for solving the 2-facility location problem in the plane with block norms
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4128
Motivated by the time-dependent location problem over T time-periods introduced in
Maier and Hamacher (2015) we consider the special case of two time-steps, which was shown
to be equivalent to the static 2-facility location problem in the plane. Geometric optimality
conditions are stated for the median objective. When using block norms, these conditions
are used to derive a polygon grid inducing a subdivision of the plane based on normal cones,
yielding a new approach to solve the 2-facility location problem in polynomial time. Combinatorial algorithms for the 2-facility location problem based on geometric properties are
deduced and their complexities are analyzed. These methods differ from others as they are
completely working on geometric objects to derive the optimal solution set.Andrea Maierpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4128Fri, 24 Jul 2015 11:31:09 +0200Competitive Algorithms for Multistage Online Scheduling
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4119
We study an online flow shop scheduling problem where each job consists of several tasks that have to be completed in t different stages and the goal is to maximize the total weight of accepted jobs.
The set of tasks of a job contains one task for each stage and each stage has a dedicated set of identical parallel machines corresponding to it that can only process tasks of this stage. In order to gain the weight (profit) associated with a job j, each of its tasks has to be executed between a task-specific release date and deadline subject to the constraint that all tasks of job j from stages 1, …, i-1 have to be completed before the task of the ith stage can be started. In the online version, jobs arrive over time and all information about the tasks of a job becomes available at the release date of its first task. This model can be used to describe production processes in supply chains when customer orders arrive online.
We show that even the basic version of the offline problem with a single machine in each stage, unit weights, unit processing times, and fixed execution times for all tasks (i.e., deadline minus release date equals processing time) is APX-hard. Moreover, we show that the approximation ratio of any polynomial-time approximation algorithm for this basic version of the problem must depend on the number t of stages.
For the online version of the basic problem, we provide a (2t-1)-competitive deterministic online algorithm and a matching lower bound. Moreover, we provide several (sometimes tight) upper and lower bounds on the competitive ratio of online algorithms for several generalizations of the basic problem involving different weights, arbitrary release dates and deadlines, different processing times of tasks, and several identical machines per stage.
Michael Hopf; Clemens Thielen; Oliver Wendtpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4119Thu, 09 Jul 2015 14:06:19 +0200On the History of Differential-Algebraic Equations
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4106
To write about the history of a subject is a challenge that grows with the number of pages as the original goal of completeness is turning more and more into an impossibility. With this in mind, the present article takes a very narrow approach and uses personal side trips and memories on conferences,
workshops, and summer schools as the stage for some of the most important protagonists and their contributions to the field of Differential-Algebraic Equations (DAEs).Bernd Simeonpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4106Tue, 23 Jun 2015 14:32:01 +0200A nonlocal sample dependence SDE-PDE system modeling proton dynamics in a tumor
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4104
A nonlocal stochastic model for intra- and extracellular proton dynamics in a tumor is proposed.
The intracellular dynamics is governed by an SDE coupled to a reaction-diffusion
equation for the extracellular proton concentration on the macroscale. In a more general context
the existence and uniqueness of solutions for local and nonlocal
SDE-PDE systems are established allowing, in particular, to analyze the proton dynamics model both,
in its local version and the case with nonlocal path dependence.
Numerical simulations are performed
to illustrate the behavior of solutions, providing some insights into the effects of randomness on tumor acidity. Peter E. Kloeden; Stefanie Sonner; Christina Surulescupreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4104Mon, 22 Jun 2015 15:00:13 +0200A stochastic model featuring acid induced gaps during tumor progression.
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4062
In this paper we propose a phenomenological model for the formation of an interstitial gap between the tumor and the stroma. The gap
is mainly filled with acid produced by the progressing edge of the tumor front. Our setting extends existing models for acid-induced tumor invasion models to incorporate
several features of local invasion like formation of gaps, spikes, buds, islands, and cavities. These behaviors are obtained mainly due to the random dynamics at the intracellular
level, the go-or-grow-or-recede dynamics on the population scale, together with the nonlinear coupling between the microscopic (intracellular) and macroscopic (population)
levels. The wellposedness of the model is proved using the semigroup technique and 1D and 2D numerical simulations are performed to illustrate model predictions and draw
conclusions based on the observed behavior.Sandesh Athni Hiremath; Christina Surulescupreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4062Wed, 29 Apr 2015 12:21:48 +0200A multiscale modeling approach to glioma invasion with therapy
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4048
We consider the multiscale model for glioma growth introduced in a previous work and extend it to account
for therapy effects. Thereby, three treatment strategies involving surgical resection, radio-, and
chemotherapy are compared for their efficiency. The chemotherapy relies on inhibiting the binding
of cell surface receptors to the surrounding tissue, which impairs both migration and proliferation.
Alexander Hunt; Christina Surulescupreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4048Wed, 08 Apr 2015 14:26:45 +0200A Finite Dominating Set Algorithm for a Dynamic Location Problem in the Plane
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4017
A single facility problem in the plane is considered, where an optimal location has to be
identified for each of finitely many time-steps with respect to time-dependent weights and
demand points. It is shown that the median objective can be reduced to a special case of the
static multifacility median problem such that results from the latter can be used to tackle the
dynamic location problem. When using block norms as distance measure between facilities,
a Finite Dominating Set (FDS) is derived. For the special case with only two time-steps, the
resulting algorithm is analyzed with respect to its worst-case complexity. Due to the relation
between dynamic location problems for T time periods and T-facility problems, this algorithm
can also be applied to the static 2-facility location problem.Andrea Maier; Horst W. Hamacherpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/4017Thu, 05 Mar 2015 14:56:26 +0100Bicriteria approach to the optimal location of surveillance cameras
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3997
We consider the problem of finding efficient locations of surveillance cameras, where we distinguish
between two different problems. In the first, the whole area must be monitored and the number of cameras
should be as small as possible. In the second, the goal is to maximize the monitored area for a fixed number of
cameras. In both of these problems, restrictions on the ability of the cameras, like limited depth of view or range
of vision are taken into account. We present solution approaches for these problems and report on results of
their implementations applied to an authentic problem. We also consider a bicriteria problem with two objectives:
maximizing the monitored area and minimizing the number of cameras, and solve it for our study case.Horst W. Hamacher; Gross Aleksandrapreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3997Fri, 20 Feb 2015 13:54:12 +0100Bicriteria approach to the optimal location of surveillance cameras
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3979
We consider the problem of finding efficient locations of surveillance cameras, where we distinguish
between two different problems. In the first, the whole area must be monitored and the number of cameras
should be as small as possible. In the second, the goal is to maximize the monitored area for a fixed number of
cameras. In both of these problems, restrictions on the ability of the cameras, like limited depth of view or range
of vision are taken into account. We present solution approaches for these problems and report on results of
their implementations applied to an authentic problem. We also consider a bicriteria problem with two objectives:
maximizing the monitored area and minimizing the number of cameras, and solve it for our study case.Aleksandra Gross; Horst W. Hamacherpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3979Thu, 29 Jan 2015 08:18:53 +0100Robust Flows with Losses and Improvability in Evacuation Planning
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3947
We consider a network flow problem, where the outgoing flow is reduced by a certain percentage in each node. Given a maximum amount of flow that can leave the source node, the aim is to find a solution that maximizes the amount of flow which arrives at the sink.
Starting from this basic model, we include two new, additional aspects: On the one hand, we are able to reduce the loss at some of the nodes; on the other hand, the exact loss values are not known, but may come from a discrete uncertainty set of exponential size.
Applications for problems of this type can be found in evacuation planning, where one would like to improve the safety of nodes such that the number of evacuees reaching safety is maximized.
We formulate the resulting robust flow problem with losses and improvability as a mixed-integer program for finitely many scenarios, and present an iterative scenario-generation procedure that avoids the inclusion of all scenarios from the beginning. In a computational study using both randomly generated instance and realistic data based on the city of Nice, France, we compare our solution algorithms.Marc Goerigk; Ismaila Abderhamanepreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3947Wed, 10 Dec 2014 15:24:43 +0100A Bicriteria Approach to Robust Optimization
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3940
The classic approach in robust optimization is to optimize the solution with respect to the worst case scenario. This pessimistic approach yields solutions that perform best if the worst scenario happens, but also usually perform bad on average. A solution that optimizes the average performance on the other hand lacks in worst-case performance guarantee.
In practice it is important to find a good compromise between these two solutions. We propose to deal with this problem by considering it from a bicriteria perspective. The Pareto curve of the bicriteria problem visualizes exactly how costly it is to ensure robustness and helps to choose the solution with the best balance between expected and guaranteed performance.
Building upon a theoretical observation on the structure of Pareto solutions for problems with polyhedral feasible sets, we present a column generation approach that requires no direct solution of the computationally expensive worst-case problem. In computational experiments we demonstrate the effectivity of both the proposed algorithm, and the bicriteria perspective in general.André Chassein; Marc Goerigkpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3940Mon, 08 Dec 2014 16:08:38 +0100Robust Geometric Programming is co-NP hard
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3938
Geometric Programming is a useful tool with a wide range of applications in engineering. As in real-world problems input data is likely to be affected by uncertainty, Hsiung, Kim, and Boyd introduced robust geometric programming to include the uncertainty in the optimization process. They also developed a tractable approximation method to tackle this problem. Further, they pose the question whether there exists a tractable reformulation of their robust geometric programming model instead of only an approximation method. We give a negative answer to this question by showing that robust geometric programming is co-NP hard in its natural posynomial form.André Chassein; Marc Goerigkpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3938Fri, 05 Dec 2014 14:17:25 +0100Sink Location to Find Optimal Shelters in Evacuation Planning
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3934
The sink location problem is a combination of network flow and location problems: From a given set of nodes in a flow network a minimum cost subset \(W\) has to be selected such that given supplies can be transported to the nodes in \(W\). In contrast to its counterpart, the source location problem which has already been studied in the literature, sinks have, in general, a limited capacity. Sink location has a decisive application in evacuation planning, where the supplies correspond to the number of evacuees and the sinks to emergency shelters.
We classify sink location problems according to capacities on shelter nodes, simultaneous or non-simultaneous flows, and single or multiple assignments of evacuee groups to shelters. Resulting combinations are interpreted in the evacuation context and analyzed with respect to their worst-case complexity status.
There are several approaches to tackle these problems: Generic solution methods for uncapacitated problems are based on source location and modifications of the network. In the capacitated case, for which source location cannot be applied, we suggest alternative approaches which work in the original network. It turns out that latter class algorithms are superior to the former ones. This is established in numerical tests including random data as well as real world data from the city of Kaiserslautern, Germany.Philipp Heßler; Horst W. Hamacherpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3934Mon, 24 Nov 2014 11:46:38 +0100Empirical Evaluation for the Conceptual Interoperability Analysis Approach: A Controlled Experiment Design
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3915
Building interoperation among separately developed software units requires checking their conceptual assumptions and constraints. However, eliciting such assumptions and constraints is time consuming and is a challenging task as it requires analyzing each of the interoperating software units. To address this issue we proposed a new conceptual interoperability analysis approach which aims at decreasing the analysis cost and the conceptual mismatches between the interoperating software units. In this report we present the design of a planned controlled experiment for evaluating the effectiveness, efficiency, and acceptance of our proposed conceptual interoperability analysis approach. The design includes the study objectives, research questions, statistical hypotheses, and experimental design. It also provides the materials that will be used in the execution phase of the planned experiment.Hadil Abukwaikpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3915Tue, 11 Nov 2014 11:13:20 +0100A coverage-based Box-Algorithm to compute a representation for optimization problems with three objective functions
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3911
A new algorithm for optimization problems with three objective functions is presented which computes a representation for the set of nondominated points. This representation is guaranteed to have a desired coverage error and a bound on the number of iterations needed by the algorithm to meet this coverage error is derived. Since the representation does not necessarily contain nondominated points only, ideas to calculate bounds for the representation error are given. Moreover, the incorporation of domination during the algorithm and other quality measures are discussed.Tobias Kuhn; Stefan Ruzikapreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3911Fri, 07 Nov 2014 10:55:37 +0100