91 citations · 223 across the 21 of their papers we have counts for
23 papers
On the Computational Complexity of Multi-Objective Ordinal Unconstrained Combinatorial Optimization
José Rui Figueira, Kathrin Klamroth, Michael Stiglmayr +1
Multi-objective unconstrained combinatorial optimization problems (MUCO) are in general hard to solve, i.e., the corresponding decision problem is NP-hard and the outcome set is in…
A Tight Formulation for the Dial-a-Ride Problem
Daniela Gaul, Kathrin Klamroth, Christian Pfeiffer +2
Ridepooling services play an increasingly important role in modern transportation systems. With soaring demand and growing fleet sizes, the underlying route planning problems becom…
On the online path extension problem -- Location and routing problems in board games
Konstantin Kraus, Kathrin Klamroth, Michael Stiglmayr
We consider an online version of a longest path problem in an undirected and planar graph that is motivated by a location and routing problem occurring in the board game "Turn & Ta…
Modeling Minimum Cost Network Flows With Port-Hamiltonian Systems
Onur Tanil Doganay, Kathrin Klamroth, Bruno Lang +2
We give a short overview of advantages and drawbacks of the classical formulation of minimum cost network flow problems and solution techniques, to motivate a reformulation of clas…
A new perspective on dynamic network flow problems via port-Hamiltonian systems
Onur Tanil Doganay, Kathrin Klamroth, Bruno Lang +2
We suggest a global perspective on dynamic network flow problems that takes advantage of the similarities to port-Hamiltonian dynamics. Dynamic minimum cost flow problems are formu…
Non-convex shape optimization by dissipative Hamiltonian flows
Matthias Bolten, Onur Tanil Doganay, Hanno Gottschalk +1
Shape optimization with constraints given by partial differential equations (PDE) is a highly developed field of optimization theory. The elegant adjoint formalism allows to comput…
PINN Training using Biobjective Optimization: The Trade-off between Data Loss and Residual Loss
Fabian Heldmann, Sarah Berkhahn, Matthias Ehrhardt +1
Physics informed neural networks (PINNs) have proven to be an efficient tool to represent problems for which measured data are available and for which the dynamics in the data are…
Ridepooling and public bus services: A comparative case-study
Hayk Asatryan, Daniela Gaul, Hanno Gottschalk +2
This case-study aims at a comparison of the service quality of time-tabled buses as compared to on-demand ridepooling cabs in the late evening hours in the city of Wuppertal, Germa…
Consensus-Based Optimization for Multi-Objective Problems: A Multi-Swarm Approach
Kathrin Klamroth, Michael Stiglmayr, Claudia Totzeck
We propose a multi-swarm approach to approximate the Pareto front of general multi-objective optimization problems that is based on the Consensus-based Optimization method (CBO). T…
Ordinal Optimization Through Multi-objective Reformulation
Kathrin Klamroth, Michael Stiglmayr, Julia Sudhoff
We analyze combinatorial optimization problems with ordinal, i.e., non-additive, objective functions that assign categories (like good, medium and bad) rather than cost coefficient…
Multi-objective Matroid Optimization with Ordinal Weights
Kathrin Klamroth, Michael Stiglmayr, Julia Sudhoff
Bi-objective optimization problems on matroids are in general intractable and their corresponding decision problems are in general NP-hard. However, if one of the objective functio…
Biobjective Optimization Problems on Matroids with Binary Costs
Jochen Gorski, Kathrin Klamroth, Julia Sudhoff
Like most multiobjective combinatorial optimization problems, biobjective optimization problems on matroids are in general intractable and their corresponding decision problems are…
Event-based MILP models for ride pooling applications
Daniela Gaul, Kathrin Klamroth, Michael Stiglmayr
Ridepooling services require efficient optimization algorithms to simultaneously plan routes and pool users in shared rides. We consider a static dial-a-ride problem (DARP) where a…
Efficient and Sparse Neural Networks by Pruning Weights in a Multiobjective Learning Approach
Malena Reiners, Kathrin Klamroth, Michael Stiglmayr
Overparameterization and overfitting are common concerns when designing and training deep neural networks, that are often counteracted by pruning and regularization strategies. How…
Tracing locally Pareto optimal points by numerical integration
Matthias Bolten, Onur Tanil Doganay, Hanno Gottschalk +1
We suggest a novel approach for the efficient and reliable approximation of the Pareto front of sufficiently smooth unconstrained bi-criteria optimization problems. Optimality cond…
GivEn -- Shape Optimization for Gas Turbines in Volatile Energy Networks
Jan Backhaus, Matthias Bolten, Onur Tanil Doganay +19
This paper describes the project GivEn that develops a novel multicriteria optimization process for gas turbine blades and vanes using modern "adjoint" shape optimization algorithm…
A simple and efficient dichotomic search algorithm for multi-objective mixed integer linear programs
Anthony Przybylski, Kathrin Klamroth, Renaud Lacour
We present a simple and at the same time fficient algorithm to compute all nondominated extreme points in the outcome set of multi-objective mixed integer linear programmes in any…
Gradient Based Biobjective Shape Optimization to Improve Reliability and Cost of Ceramic Components
Onur T. Doganay, Hanno Gottschalk, Camilla Hahn +3
We consider the simultaneous optimization of the reliability and the cost of a ceramic component in a biobjective PDE constrained shape optimization problem. A probabilistic Weibul…
Introducing Multiobjective Complex Systems
Tobias Dietz, Kathrin Klamroth, Konstantin Kraus +5
This article focuses on the optimization of a complex system which is composed of several subsystems. On the one hand, these subsystems are subject to multiple objectives, local co…
Adjoint Method to Calculate Shape Gradients of Failure Probabilaties for Turbomachinery Components
Hanno Gottschalk, Mohamed Saadi, Onur Tanil Doganay +2
In the optimization of turbomachinery components, shape sensitivities for fluid dynamical objective functions have been used for a long time. As peak stress is not a differential f…
A Box Decomposition Algorithm to Compute the Hypervolume Indicator
Renaud Lacour, Kathrin Klamroth, Carlos M. Fonseca
We propose a new approach to the computation of the hypervolume indicator, based on partitioning the dominated region into a set of axis-parallel hyperrectangles or boxes. We prese…
On the representation of the search region in multi-objective optimization
Kathrin Klamroth, Renaud Lacour, Daniel Vanderpooten
Given a finite set of feasible points of a multi-objective optimization (MOO) problem, the search region corresponds to the part of the objective space containing all the point…
A linear bound on the number of scalarizations needed to solve discrete tricriteria optimization problems
Kerstin Daechert, Kathrin Klamroth
General multi-objective optimization problems are often solved by a sequence of parametric single objective problems, so-called scalarizations. If the set of nondominated points is…