Research | AC Group | TU Wien

FWF Cluster of Excellence: Bilateral Artificial Intelligence

Mon, 01 Jan 2024 00:00:00 +0000

🌐 Visit the official FWF Project Radar Page

The project Bilateral AI aims at lifting artificial intelligence (AI) to the next level. Current AI systems are in a sense narrow. They center on a specific application or task such as object or speech recognition. Our project will combine two of the most important types of AI which have been developed separately so far: symbolic and sub-symbolic AI. While symbolic AI works with clearly defined logical rules, sub-symbolic AI (such as ChatGPT) is based on training a machine with the help of large datasets to create intelligent behavior. This integration, resulting in a Broad AI, is intended to mirror something that humans do naturally: the simultaneous use of cognition and reasoning skills. But what exactly is Broad AI? As opposed to Narrow AI, which is characterized by task specific skills, Broad AI aims at solving a wide array of problems, rather than being limited to a single task or domain. By combining sub-symbolic AI (machine learning, ML) with symbolic AI (knowledge representation and reasoning, KRR), Bilateral AI provides the means to develop the foundations of the capabilities and skill acquisition for problem solving by a Broad AI. Harnessing the full potential of both symbolic and sub-symbolic approaches can open new avenues for AI that are better at solving new problems, adapting to a wide variety of environments, having better reasoning skills, and being more efficient in terms of both computation and data use. These key features allow for a vast range of use cases for Broad AI, starting with drug development and medicine, over planning and scheduling, to autonomous traffic management and recommendation systems. With fairness, transparency, and explainability as top priorities, developing Broad AI is also essential for addressing ethical concerns and ensuring a positive impact on our society. These concerns play a central role as cross-cutting aspect in our project. The Broad AI resulting from the bilateral AI approach would use its own sensory perceptions to perform abstractions and engage in a logical thinking process. The AI could then, for example, organize a trip, minimize carbon emissions, or renovate a house as cost- effectively and ecologically as possible. In other words, AI could perform complex planning taking all aspects into account. Sepp Hochreiter, Director of Research: Broad AI could potentially improve our everyday lives as well as system-relevant aspects and processes - such as energy, transportation and healthcare - by becoming more environmentally sustainable, efficient and resource-friendly.

Parameterized Graph Drawing

Sun, 01 Jan 2023 00:00:00 +0000

Funding organization: Vienna Science and Technology Fund, WWTF
Project number: ICT22-029 (Information and Communication Technology 2022)

Project Team

Robert Ganian (Principal Investigator)

Martin Nöllenburg (Principal Investigator)

Simon Dominik Fink (Postdoctoral Researcher)

Thomas Depian (PhD Student)

Alexander Firbas (PhD Student)

Research Statement

The project is centered around two well-established fields of information and communication technology: (1) graph drawing and visualization, which deals with the construction and analysis of geometric representations of graphs and networks subject to specific layout conventions, and (2) parameterized complexity analysis, which offers the tools to design efficient algorithms as well as lower bounds custom-tailored to the specific structural properties of relevant inputs. Recent advances have highlighted the huge potential for the application of parameterized techniques on graph drawing and visualization problems. The two PIs of this proposal – Robert Ganian and Martin Nöllenburg – have already spearheaded an initial push to bring the two fields closer together, and this proposal will allow them to bring these efforts into fruition by targeting and resolving some of the most prominent questions in this intersection.

The project focuses on developing the tools and frameworks that will facilitate the parameterized analysis of central problems in graph drawing and visualization. The work is split into four fundamental themes, covering Extension Problems, Linear and Layered Layouts, Geometric Graph Representations and Bridges to Network Visualization. The output of each theme will include not only new algorithms but also tight lower bounds and, where relevant, implementations, significantly advancing the state of the art in these increasingly prominent fields of research.

ASK-SAT: Alternating Symmetry-Breaking Combinatorial Search with SAT

Mon, 01 Jan 2024 00:00:00 +0000

Project Acronym: ASK-SAT (Alternating Symmetry-Breaking Combinatorial Search with SAT)

Funding organization: Austrian Science Fund (FWF)

Project number: P 36688

Grant DOI: 10.55776/P36688

Project Team

Topic

Many unsolved problems in discrete mathematics and extremal combinatorics can be stated as whether a combinatorial object with a particular property and size exists.

The project focuses on developing novel methods for answering such questions using the innovative Satisfiability Modulo Symmetries (SMS) technique. This approach departs from traditional exhaustive search strategies by dynamically identifying and excluding redundant sub-configurations, thus streamlining the search process while utilizing the power of solvers for the propositional satisfiability problem (SAT).

The project aims to extend the capabilities of SMS to effectively tackle the existence of objects whose defining property requires alternating quantifiers, which present unique challenges beyond the scope of conventional SAT methods. This involves integrating advanced computational tools, including quantified Boolean formulas and symmetry-reasoning technologies.

Through this research, the project aspires to advance the fields of automated reasoning and discrete mathematics.

Software

The software tool developed through the project is available via GitHub, the documentation via Read the Docs.

Balancing Bicycle Sharing Systems

Mon, 01 Jan 2024 00:00:00 +0000

Christian Kloimüllner
Petrina Papazek
Bin Hu
Günther Raidl

Public bicycle sharing systems are booming worldwide in many major
cities. Such systems augment public transport very well, are green
alternatives to motorized individual traffic, may decrease traffic jams
and parking problems in cities to a certain extent, and last but not
least are an incentive for sports and a significant contribution to
public health. Modern systems have automated rental stations
distributed over the (inner) districts of the city, and users can easily
rent bicycles and bring them back at any other stations.

For user acceptance it is essential to provide enough bicycles as well
as parking slots for returning them at any station at almost all time.
As the usage pattern typically is not symmetric, e.g., people tend to
rent bikes at topographically higher stations and return them to lower
stations, an active maintenance by regularly transporting bikes from
some stations to others is crucial.

Project partners:

Problem Definition

The balancing bike sharing systems (BBSS) problem consists of finding an optimal rebalancing plan for the operator that consists of

a route for each vehicle on duty and
loading instructions for each station on the route

so that the system is in a balanced condition afterwards, i.e., no station is overly full or empty.

Two basic problem variants are considered:

Static case: The rebalancing process is done when the system is not used (e.g., overnight). Initial and target station fill levels are given as constants.
Dynamic case: The system is rebalanced while in use. Station fill levels are time-dependent and estimated by empirical data.

Approaches

Exact approaches based on mixed integer programming:

Hop index model
Time index model

Construction heuristics:

Greedy heuristic
PILOT heuristic

Metaheuristic approaches:

Variable neighborhood search approach
GRASP approach
Hybrid of GRASP with Path Relinking

Problem instances which we are using for our work can be found here.

Complete Solution Archives for Evolutionary Combinatorial Optimization

Mon, 01 Jan 2024 00:00:00 +0000

Benjamin Biesinger
Bin Hu
Günther Raidl

A common drawback of metaheuristic approaches, especially evolutionary algorithms (EAs) is that they usually do not keep track of the search history, and already evaluated solutions are often revisited. This leads to premature convergence, wasting resources for re-evaluations, and other malicious effects.

The solution archive (SA) is a concept for dealing with this problem. By attaching it to a classical EA, it enables detecting already evaluated candidate solutions and efficiently transforming them into similar but yet unvisited solutions, i.e., performing an “intelligent mutation”. The figure above illustrates the cooperation between the EA and the archive.

Application

Since this concept causes additional overhead for inserting and transforming solutions in the SA data structure, following conditions should hold in order to maximize the efficiency:

A compact solution representation keeps memory consumption for the archive low.
Problems with expensive solution evaluations benefit most from avoiding re-evaluations.
Problems with few feasibility constraints so that the transformation procedure can be kept simple.

Following combinatorial optimization problems were particularly successful with this approach:

Royal road functions
NK landscapes
Generalized minimum spanning tree problem
(r|p)-centroid problem and other competitive facility location problem

Cooperative Optimization Approaches for Distributing Service Points

Mon, 01 Jan 2024 00:00:00 +0000

A joined research project from the Algorithms and Complexity Group, TU Wien, Austria, and Honda Research Institute, Germany

Project Team

Topic

For many business models an optimal distribution of service points in a customer community is needed. Examples are charging/battery swapping stations of electric vehicles, bicycle/car sharing stations, and repair stations. When planning such systems, estimating under which conditions which customer demand can be fulfilled is fundamental in order to design and evaluate possible solutions. To this end, demographic data is usually interlinked with geographic information, data on public transport, the street network, knowledge on manifold special locations etc. Usually, the whole process is challenging and error-prone. Customer demand information determined in such ways typically is vague, and not uncommonly a system built on such assumptions is not as effective as originally hoped for due to major deviations in reality.

To possibly improve this situation, we propose a cooperative optimization approach that incorporates potential users on a large scale and more tightly into the data acquisition as well as the optimization process. We confront the potential customers with certain location scenarios and ask them how these would suit his needs and how possibly these scenarios can be improved to fulfill more of his demand. This feedback is used to incrementally gain more knowledge about how much demand may be fulfilled under which conditions. New, more promising candidate location scenarios can then be derived and again be presented to the users. The process is iterated on a large scale with many potential users and many rounds until a satisfactory solution is reached.

The figure below depicts the components of the framework of our cooperative optimization approach and their interaction. The general framework consists of the following components: an evaluation component (EC), an optimization component (OC), a feedback component (FC), and a solution management component (SMC).

Cooperative Personnel Scheduling

Mon, 01 Jan 2024 00:00:00 +0000

a joined research project from the Algorithms and Complexity Group, TU Wien, Austria, and Honda Research Institute, Germany

Project Team

Topic

In classical personnel scheduling, the employees of a company are assigned their schedules, which determine at which times they work to fulfill some kind of demand. Our setting is slightly different. Each employee also referred to as a user, has some jobs to complete that need a specific resource. Only a limited amount of resources is available and only one job can use a resource at a time. Each job has to be assigned to a resource and a timespan in which the job is carried out. Furthermore, users will have preferences on when their jobs should be executed. These preferences have to be collected in some way. One possibility would be to let the employees specify their full preferences in the beginning. But this is tedious for the employees, error-prone and only allows for preferences that were explicitly considered when designing the algorithm.

In this project, we will develop and investigate a framework that learns these preferences over time through a limited amount of user interactions. The framework aims to construct schedules that respect employees’ preferences and are feasible with the given restrictions. While constructing the schedules it is allowed to ask the employees a limited number of questions regarding their preferences. The responses to these questions are used to model the employees’ preferences. The framework may also consider fairness, e.g. by balancing the number of user interactions among the employees. Research questions to answer include which algorithms work well for the occurring problems, how to model the employees’ preferences, and which kind of user interaction gives sufficient information on the preferences, without overwhelming the employees.

Cutting and Packing Problems

Mon, 01 Jan 2024 00:00:00 +0000

Frederico Dusberger
Günther Raidl

Cutting and packing problems occur in a multitude of industrial or other real-world applications such as

glass, paper and steel cutting,
container or pallet loading,
VLSI design,
and many other applications.

Usually items have to be cut from raw material and waste should be minimized, or the number of bins/containers needed to pack given items has to be minimized. In our research we dealt (until now) with two-dimensional bin packing problems where only guillotineable cutting/packing patterns are allowed.

Another kind of packing problems are the so called Knapsack Problems. Where items with an associated profit have to be packed into one or more knapsacks, and the profit has to be maximized. There are several variants of knapsack problems, such as the classical knapsack problem, multiconstrained knapsack problems, multiple knapsack problems, and many others. In our research we mainly deal with multiconstrained knapsack problems.

Solving cutting and packing problems can be done in various ways, we developed metaheuristics and exact algorithms for dealing with those problems. Especially the combination of evolutionary algorithms with problem specific heuristics, local and global optimization techniques are considered. Please see also our page on metaheuristics and evolutionary computation and the page on hybrid optimization techniques.

Cycles on Graphs and Properties of Graphs with Special Cycle Structure

Mon, 01 Jan 2024 00:00:00 +0000

Doctoral College Logical Methods in Computer Science

Mon, 01 Jan 2024 00:00:00 +0000

Doctoral College Vienna Graduate School on Computational Optimization

Mon, 01 Jan 2024 00:00:00 +0000

Engineering Linear Ordering Algorithms for Optimizing Data Visualizations

Mon, 01 Jan 2024 00:00:00 +0000

Funding Organisation: Vienna Science and Technology Fund, WWTF
Project Number: ICT19-035
Duration: 07/2020 - 06/2024

Project Team

Alexander Dobler
Markus Wallinger
Jules Wulms
Martin Nöllenburg (Professor, Principal Investigator)

Alexander Dobler, Michael Stampfer, Martin Nöllenburg at the 20th anniversary of WWTF

Topic

Optimizing linear orderings of objects is a fundamental problem for many types of data visualizations ranging from graph layouts over geospatial data to abstract sets and sequences or time-series data. Yet a systematic investigation of algorithms for solving novel constrained and application-specific ordering problems that go beyond well-studied and NP-hard classic ordering problems is missing. Practical work in visualization often resorts to heuristics without rigorous performance and quality guarantees for solving these algorithmic problems. In this project we will take an algorithmic perspective on several different ordering problems in data visualization. In the algorithm engineering sense we want to cross the gap between fundamental theory and practical applications and aim to bring the benefit of rigorous formal methods into practically relevant implementations and at the same time define new algorithmic challenges inspired by recent visualization problems. On the one hand, we will investigate the complexity of new problem settings with special input configurations, structural constraints on feasible orderings, and dependencies between multiple objects and orderings. On the other hand, we will design and implement new and sufficiently scalable algorithms with formally proven performance guarantees to compute optimal and approximate solutions. We thoroughly evaluate the improvements over existing state-of-the-art heuristics in computational experiments and user studies.

Exploiting New Types of Structure for Fixed Parameter Tractability

Mon, 01 Jan 2024 00:00:00 +0000

Funding Organisation: The Austrian Science Funds, FWF
Project Number: FWF P26696

Project Team

Eduard Eiben
Robert Ganian
Ramanujan Sridharan
Stefan Szeider (Principal Investigator)

Topic

Many important computational problems are intractable on general instances. However, realistic problem instances often contain a certain hidden structure that can be exploited to solve the problem efficiently. Such instances form a tractable class (or an island of tractability). Previous research has identified a large number of tractable classes for various NP-hard problems.

The aim of this project is to develop a rigorous framework for exploiting new kinds of structure on a wide range of problem inputs; in particular, this framework will be based on the established notion of a modulator to a tractable class of instances, which applies to problem instances that may be placed in a tractable class by a small number of local changes. The type of structure exploited by algorithms developed within the proposed research is fundamentally different from the structure exploited by known state-of-the-art approaches. This will allow us to handle natural instances where all presently known state-of-the-art approaches remain unfeasible.

Human-centered Algorithm Engineering: Graph and Map Visualization

Mon, 01 Jan 2024 00:00:00 +0000

Funding Organisation: The Austrian Science Fund, FWF
Project Number: FWF P 31119
Duration: 11/2018 - 04/2023

Project Team

Sujoy Bhore
Jules Wulms
Guangping Li
Anaïs Villedieu
Martin Nöllenburg (Principal Investigator)

Topic

Human-centered algorithm engineering is an unconventional new paradigm in algorithmics that puts, for the first time, human factors into the focus of algorithm engineering, a methodology that is based on a cycle of design, analysis, implementation, and experimental evaluation of algorithms. Unlike other fields in computer science that investigate the interplay of humans and computers, our focus is on the symbiosis of human expert users and computers on the fundamental level of algorithms. We combine the mathematical accuracy and computational power of formal algorithmics with the creative power of the human mind in order to provide more effective and efficient algorithms for currently insufficiently solved and ill-defined algorithmic problems. Human-centered algorithms aim to produce solutions of better quality with faster overall performance, and higher user satisfaction. The project has two main goals: (i) establishing theoretical foundations, models of computation, and suitable evaluation methods and (ii) showing the practicality and benefits of the new paradigm for several prime examples of human-centered algorithmic problems that lack satisfying traditional algorithmic solutions in graph drawing and computational cartography.

Large-Scale Radio Therapy Scheduling

Mon, 01 Jan 2024 00:00:00 +0000

One of the most advanced medical centers for Ion Beam Therapy and Research in Europe is currently being built in Wiener Neustadt: MedAustron. The radiation therapies applied will use protons and carbon ions for cancer treatment. The first patient treatments are planned in 2016. In the full operational phase up to 1,200 people per year are expected to benefit from the international first class medicine offered by MedAustron.

Scheduling patient treatments with their large number of individual tasks and specific requirements in order to best utilize the available resources and treat patients as soon and smoothly as possible is a huge challenge. The Algorithms and Complexity Group collaborates with MedAustron in the development of advanced scheduling algorithms and their software implementation to fulfill these needs and to give as many cancer patients as possible a new opportunity.

Within the Algorithms and Complexity Group, Günther Raidl is leading this research effort.

Learning to Solve Dynamic Vehicle Routing Problems

Mon, 01 Jan 2024 00:00:00 +0000

a joined research project from the Algorithms and Complexity Group, TU Wien, Austria, and Honda Research Institute, Germany

Project Team

Topic

Vehicle routing - i.e., planning an optimal set of routes for a fleet of vehicles - is an intensely studied research area with enormous practical and raising relevance due to increasing mobility and transportation demand and new challenges coming, e.g., from increasing interest in shared mobility services and electric vehicles. The dial-a-ride problem (DARP), for example, is the problem of finding optimal tours of vehicles through different pickup and drop-off locations in order to serve a number of transportation requests, allowing different customers to share a vehicle. The electric autonomous dial-a-ride problem (E-ADARP) represents a challenging and practically relevant extension to the DARP, where electric autonomous vehicles are employed and their charging requirements have to be taken into consideration. Furthermore, not only classical objectives like total travel time have to be optimized, but user inconvenience also plays an important role. Thus, factors such as user excess ride time, which is due to detours because of the ride-sharing, have to be taken into account.

For such problems, heuristic optimization approaches are considered to be the means of choice due to a better scalability compared to exact approaches. In this project, we propose a heuristic framework based on large neighborhood search (LNS) to solve the E-ADARP, and we plan to tackle the issue of scalability by automatically designing, i.e., learning, efficient heuristics that either guide or possibly replace classical optimization techniques. We will investigate the usage of reinforcement learning with different machine learning models to dynamically select operators for the LNS from a set of possible operators during the optimization process. We intend to experimentally compare this approach to other learning techniques such as classical supervised learning, imitation learning, and Q learning.

Learning to Solve Quantified Boolean Formulas

Mon, 01 Jan 2024 00:00:00 +0000

Funding Organisation: Vienna Science and Technology Fund, WWTF
Project Number: ICT19-060
Duration: 05/2020 - 04/2023

Project Team

Franz Xaver Reichl
Leroy Chew
Friedrich Slivovsky
Stefan Szeider

Topic

Quantified Boolean Formulas (QBFs) can succinctly encode hard problems arising in planning, verification,
and synthesis, and the development of efficient procedures for evaluating QBFs (so-called QBF solvers)
will drive advances in all of these areas.

These applications require that QBF solvers not only decide whether a formula is true or false but also
output a witnessing strategy. For instance, when using QBF to solve a program synthesis problem, we
expect solvers to return the synthesized program. Our work on solver design and proof theory has
convinced us that strategies are at the heart of QBF research. However, the prevailing theoretical models of
QBF solvers as decision procedures or proof systems do not capture the fact that they need to compute
strategies. This project aims to develop a new, richer model of QBF solvers as strategy learning algorithms.

Software

Unique: A preprocessor for (D)QBF that computes unique Skolem and Herbrand functions (GitHub).
Pedant: A certifying DQBF solver based on definition extraction (GitHub).

Matheuristics: Hybrid Optimization Algorithms for Transportation Problems with Mutltiple Visits

Mon, 01 Jan 2024 00:00:00 +0000

MSCA COFUND Doctoral Programme LogiCS@TUWien

Mon, 01 Jan 2024 00:00:00 +0000

Multi-Criteria Optimization of FTTx Networks

Mon, 01 Jan 2024 00:00:00 +0000

New Frontiers for Parameterized Complexity

Mon, 01 Jan 2024 00:00:00 +0000

Funding Organisation: The Austrian Science Fund, FWF
Duration: 1 September 2018 – 31 August 2022
Project Number: FWF P 31336

Project Team

Robert Ganian (Principal Investigator)
Thekla Hamm
Kirill Simonov
Viktoriia Korchemna

Research Statement

Many important computational problems are intractable on general instances. However, realistic problem instances often contain a certain hidden structure that can be exploited to solve the problem efficiently. The parameterized complexity paradigm provides the perfect tools and techniques to formalize, quantify and exploit various forms of structure in order to efficiently solve a plethora of NP-hard problems. This paradigm has been applied in many fundamental areas of computer science with great success, including graph algorithms, computational logic, boolean satisfiability, constraint satisfaction and others. However, there are prominent areas of computer science where we still lack a thorough understanding of the parameterized complexity of key computational problems.

This project aims at investigating the parameterized complexity of fundamental problems in two such high-impact areas: integer linear programming and machine learning. The obtained results will allow the design of efficient algorithms capable of exploiting various natural forms of structure in order to find exact solutions for NP-hard problems in these important areas of computer science. A secondary aim of the project is to apply newly obtained insights and advancements in other, including more traditional, frontiers of parameterized complexity research.

Optimization Challenges in the Future Federated Internet

Mon, 01 Jan 2024 00:00:00 +0000

Overcoming Intractability in the Knowledge Compilation Map

Mon, 01 Jan 2024 00:00:00 +0000

Parameterized Analysis in Artificial Intelligence

Mon, 01 Jan 2024 00:00:00 +0000

Funding organization: The Austrian Science Funds, FWF
Project number: Y 1329 START-Programm (ParAI)
Grant DOI: 10.55776/Y1329

Project Team

Picture credits: Soeren Nickel, 2020

Research Statement

Parameterized complexity theory is a well-established paradigm used for the fine-grained analysis of computational problems. Such analysis can provide efficient algorithms for these problems by exploiting subtle structural properties of relevant inputs, as well as powerful lower bounds that rule out efficient algorithms even for severely restricted instances. Parameterized complexity analysis has found great success across numerous fields of computer science, with notable examples including graph algorithms, computational geometry, database theory, computational logic and constraint satisfaction. In the highly prominent fields of artificial intelligence (AI) and machine learning (ML) – areas which have become an ubiquitous part of today’s society – we see a distinct lack of foundational research targeting the fine-grained, parameterized complexity of fundamental problems. The goal of this six-year project is to change this.

A Parameterized Toolbox for Problems in AI and ML

One main objective of this project is the development of new innovative tools and machinery that allows us to apply the parameterized complexity framework in this setting. Indeed, most of the existing tools developed in parameterized complexity theory are designed to work in the setting of discrete problems on graphs. On the other hand, many problems of interest in AI and ML do not admit straightforward graph representations and/or contain non-discrete components. The development of the required tools will then go hand in hand with obtaining new algorithms and matching lower bounds for the studied problems.

Parameterized Compilation

Mon, 01 Jan 2024 00:00:00 +0000

Funding Organisation: The Austrian Science Funds, FWF
Project Number: FWF P26200

Project Team

Simone Bova
Ronald de Haan
Neha Lodha
Stefan Szeider (Principal Investigator)

Topic

Knowledge compilation offers a compelling approach to coping with computationally hard problems. This line of research was initiated in the early 1990s, and since then has become a very active and progressing field. Knowledge compilation proceeds in two phases: In the first phase the input data is compiled into a new representation, which is then used in a second phase to execute a number of individual tasks efficiently. Ideally, one aims at a compilation that causes only a polynomial increase in space, and the classical theory of compilation offers theoretical tools to decide whether such a compilation is possible or not. However, systematic research shows that most relevant problems are not compilable with a polynomial increase in space. Hence, the classical theory cannot provide reasonable guarantees for these problems.

The goal of this project is to overcome this limit of classical knowledge compilation by utilizing structural aspects of problem inputs (such as as tree-likeness, degree of cyclicity, or backdoor size). As the key to this goal we propose to study knowledge compilation within the framework of parameterized complexity, which has become an important and successful research direction in algorithms and complexity. Parameterized complexity provides powerful methods and tools for exploiting structural aspects of problems and is therefore ideally suited for this purpose. Using parameters we can exploit structural aspects of the input in order to support the compilation. Hence we aim at positive results in terms of upper bounds for compilation space and compilation time for problems that are not compilable in the classical sense.

Events

Symposium on New Frontiers in Knowledge Compilation

This symposium was organised by Pierre Marquis and Stefan Szeider and held in Vienna, Austria, June 4-6, 2015. The aim of this symposium was to bring together researchers who work on knowledge compilation from various angles, including knowledge representation, constraints, theory of algorithms, complexity, machine learning, and databases, as well as researchers from related areas. Lectures and discussions have put all these different approaches into context and stimulated a fruitful exchange of ideas between researchers from different fields. The symposium had over 30 participants and featured several invited and contributed talks.

More information can be found on the symposium homepage.

Dagstuhl Seminar on Recent Trends in Knowledge Compilation

Stimulated by the success of the Symposium in Vienna, another larger meeting on this topic was organized by Adnan Darwiche (UCLA, US), Pierre Marquis (Artois University – Lens, FR), Dan Suciu (University of Washington – Seattle, US, and Stefan Szeider (TU Wien, AT). The seminar was held at Schloss Dagstuhl, Wadern, Germany, September 17-22, 2017,

The seminar had over 40 participants and featured several invited and contributed talks as well as a demo session and and open problems session.

Among others, the seminar was an excellent occasion for disseminating results of the project, in terms of several talks.

A report on the seminar has been published as Dagstuhl Report (Volume 7, Issue 9, 2018).

More information can be found on the seminar homepage.

QBF Proofs and Certificates

Mon, 01 Jan 2024 00:00:00 +0000

Funding organization: Austrian Science Fund (FWF)

Project number: (ESP 197 ESPRIT-Programm)

Grant DOI: 10.55776/ESP197

Project Team

Leroy Chew
Stefan Szeider
Friedrich Slivovsky

Topic

Quantified Boolean Formulas (QBF) extends propositional logic. Solving a QBF is theoretically more difficult than solving a propositional SAT (satisfiability) problem. QBFs are canonically PSPACE complete, meaning solving them could be used to solve any PSPACE problem by proxy. In the last decade progress in QBF solving is being undertaken. Proof complexity is the main theoretical framework used to understand SAT and QBF solving. Proof systems use the same logical rules as sound solvers, the proof size can often be a lower bound of running time between proof systems and related solvers. Proof systems can also be used to certify the correctness of solvers. However certification of QBF solvers is not yet a common occurrence.

We wish to further study the landscape of QBF proof complexity, studying the existing QBF proof systems that have emerged to capturing the solvers over the last decade. We also wish to use knowledge of soundness and complexity to make minor and major modifications to QBF proof systems, that could be beneficial to theory and practice. Our hope is that such knowledge will be of use to the QBF solving community.

QBF proof systems arranged via p-simulation

REVEAL-AI: Revealing and Utilizing the Hidden Structure for Solving Hard Problems in AI

Mon, 01 Jan 2024 00:00:00 +0000

Funding organization: Vienna Science and Technology Fund (WWTF)
Call: Information and Communication Technologies (ICT- 19)
Project number: ICT19-065 (Reveal-AI)

Project Team

Wolfgang Dvorak
Markus Hecher
Matthias König
Vaidyanathan P. R.
Anna Rapberger
André Schidler
Stefan Szeider (PI)
Stefan Woltran (co-PI)

Topic

At the core of several critical areas of AI and reasoning are hard-to-solve computational problems, such as the processing of constraints, carrying out sound reasoning tasks, and the verification of the correctness of procedures and protocols. All these problems are in general intractable and pose a challenge to algorithm design, even more as today’s applications demand more extensive problem inputs be solved. This requires new and more robust algorithms to facilitate further progress in technological innovation. Fortunately, typical problem inputs tend to contain some form of hidden structure, as the problem data is usually the product of a process. This research project is about revealing this hidden structure and utilizing it for an efficient solution.

SLIM: SAT Based Local Improvement

Mon, 01 Jan 2024 00:00:00 +0000

Funding organization: Austrian Science Fund (FWF)

Project number: (FWF P 32441)

Project Team

Stefan Szeider (PI)
André Schidler
P.R. Vaidyanathan

Topic

Many problems can be efficiently translated into the propositional satisfiability problem (SAT) and then solved with a powerful SAT solver or variants such as MaxSAT or QBF-SAT. In some cases, the translation causes a significant blow-up in size, so this one-shot translation is not feasible. In this project, we overcome this limitation by repeatedly translating small local parts of the problem instance to SAT, which allows us to scale the SAT solver’s power to large instances. We could successfully implement this approach for various critical computational problems, including learning the structure of a Bayesian network, inducing an interpretable decision tree, or minimizing a Boolean circuit.

STRIDES: Structure Identification with SAT

Mon, 01 Jan 2024 00:00:00 +0000

Project Acronym: STRIDES (Structure Identification with SAT)

Funding organization: Austrian Science Fund (FWF)

Project number: P 36420

Grant DOI: 10.55776/P36420

Project Team

Stefan Szeider (PI)
André Schidler
P.R. Vaidyanathan
Florentina Voboril

Topic

SAT is the famous propositional satisfiability problem. It asks to assign the variables of a propositional formula with truth values 0 and 1 such that the entire formula becomes true. SAT is generally considered intractable, but over the last twenty years, computer programs (SAT solvers) have been engineered that can solve the problem surprisingly fast. Numerous other hard problems can be translated to SAT and solved via SAT solvers. However, the translation to SAT often causes a significant increase in size, which limits the application of SAT solvers to small problem inputs.

The project aims at scaling the use of SAT solvers to large problem inputs by utilizing the recently introduced SAT-based Local Improvement Method (SLIM). It starts with an initial heuristic solution and repeatedly applies a SAT solver to small local parts of the input, overcoming the size limitation. The project will investigate using SLIM for problems that ask to find a specific hard-to-find structure in given data. Such structure identification problems arise in text data, large graphs and networks, and logical circuits. It will focus on methods for making the SLIM approach more efficient and finding general insights into its workings. The research is expected to lead to new theoretical and practical results.

The Parameterized Complexity of Reasoning Problems

Mon, 01 Jan 2024 00:00:00 +0000

Reasoning, to derive conclusions from facts, is a fundamental task in Artificial Intelligence that arises in a wide range of applications from Robotics to Expert Systems. Different applications require different forms of reasoning such as Nonmonotonic Reasoning (e.g., reasoning under the presence of default as- sumptions), Constraint-Based Reasoning (reasoning with forbidden configurations), and Bayesian Reasoning (reasoning with uncertain data). All these forms of reasoning give rise to computational problems that can be solved algorithmically. The efficiency of algorithms has a direct impact on applications; for example, improved algorithms for Bayesian inference yield more accurate computer assisted medical diagnosis.

The aim of this project is to devise new efficient algorithms for reasoning problems and to gain new theoretical insights into the question of what makes a reasoning problem hard, and what makes it easy. We study reasoning problems within the framework of Parameterized Complexity, a new and rapidly emerging field of Algorithms and Complexity. Parameterized Complexity takes structural aspects of problem instances into account which are most significant for empirically observed problem-hardness. Most of the considered reasoning problems are intractable in general, but the real-world context of their origin provides structural information that can be made accessible to algorithms in form of parameters. This makes Parameterized Complexity an ideal setting for the analysis and efficient solution of these problems that we want to explore.

Two inputs for a reasoning problem: random (left) and realistic (right).

An Illustration: the picture above shows the visualizations of two inputs for a reasoning problem. The input to the left is a random input. The input to the right is a real-world input (from multiplier verification). Both inputs have approximately the same size in bits, however, evidently the real-world instance is somehow “structured”, whereas the random instance is not. The parameterized complexity approach allows to utilize this kind of structure to solve the problem efficiently.

Project Record

Project Title: The Parameterized Complexity of Reasoning Problems
Principle Investigator: Stefan Szeider
Funding Organization: European Research Council (ERC)
Project Type: ERC Starting Independent Researcher Grant
Project Volume: EUR 1.4 Mio
Project Duration: Jan 2010 - Dec 2014
Host Institution: Vienna University of Technology

Project Team

Simone Bova
Johannes Fichte
Robert Ganian
Ronald de Haan
Eva Nedoma
Friedrich Slivovsky
Stefan Szeider, project leader
Yue Chen
Serge Gaspers (University of NSW and NICTA, Australia)
Sebastian Ordyniak

de Haan, Fichte, Slivovsky, Gaspers, Ordyniak, Szeider, Bova, Ganian (from left to right)

Variable Dependencies of Quantified Boolean Formulas

Mon, 01 Jan 2024 00:00:00 +0000

Funding Organisation: The Austrian Science Fund, FWF
Project Number: FWF P 27721

Project Team

Friedrich Slivovsky
Tomas Peitl
Stefan Szeider (Professor, Principal Investigator)

Topic

The satisfiability problem of Quantified Boolean Formulas (QBF) offers succinct encodings for hard problems arising in areas such as formal verification and planning. This research project explores new ways to leverage independence of variables for QBF. The nesting of existential and universal quantifiers in Quantified Boolean Formulas can generate variable dependencies that are a serious obstacle to lifting successful techniques from propositional satisfiability to QBF. In some cases one can identify a variable dependency as spurious and conclude that the corresponding variables are in fact independent. This information can be used to significantly improve the performance of decision procedures, but deciding whether variables are independent is a highly intractable problem in itself.

The aim of this project is three-fold: (A) to advance the state of the art in detecting variable independence by developing new theory and improved algorithms for currently used methods; (B) to find new approaches to detecting and harnessing variable independence; (C) to extend the scope of successful techniques from QBF to more general problems.

Software

Research under Theme (B) has lead to the development of Qute (GitHub), a dependency learning QBF solver. Qute placed 3rd in the PCNF track and 4th in the Prenex non-CNF track of QBFEVAL'17.

Problem Instances

Tue, 24 Feb 2015 00:00:00 +0000

Electric Autonomous Dial-a-Ride Problem

Contact: Maria Bresich and/or Günther Raidl

The E-ADARP benchmark instances including a description of their format as originally introduced and provided in Bongiovanni et al. (2019) can be found here.

Graph Burning Problem

Contact: Enrico Iurlano and/or Günther Raidl

Computational results referred to in the preprint [Iurlano, Raidl, and Djukanovic 2025] can be found here.
A description of the instance format can be found here here.

Interactive Job Scheduling Problem

Contact: Johannes Varga and/or Günther Raidl

The IJSP instance set can be found here.
The instance set with real-world availabilities can be found here.

A description of the instance format can be found here.

The samples for the Bayesian Learning can be found here.
Their format is described here.

Roman Domination Problem

Instance sets for different graph classes can be found here.
Experimental results for the instance sets can be found here.
A description of the instance format can be found here.

1 result


2024
[1]	A Simulated Annealing Based Approach for the Roman Domination Problem Jakob Greilhuber, Sophia Schober, Enrico Iurlano, Günther R. Raidl Metaheuristics and Nature Inspired Computing (Bernabé Dorronsoro, Rachid Ellaia, El-Ghazali Talbi, eds.), volume 2016 of CCIS, pages 28–43, 2024, Springer. [bibtex] [pdf] [doi]

Contact: Enrico Iurlano and/or Günther Raidl

EV Fleet Charging and Allocation Problem

Contact: Johannes Varga and/or Günther Raidl

The EVFCAP instance set can be found here.
The reduced EVFCAP instance set can be found here.

A description of the instance format can be found here.

EV charging scheduling problem with SOC-dependent maximum charging power

Contact: Thomas Jatschka and/or Günther Raidl

Individual EVS-SOC instances can be found here.
Rolling horizon EVS-SOC instances can be found here.

A description of the instance format can be found here.

Searching for Stable Fixed-Edge Graphs - Instances and Source Code

Contact: Benedikt Klocker and/or Günther Raidl
Instance Set 1 (3-connected 3-regular planar graphs with minimum degree three) can be found here.
Instance Set 2 (after inserting some random degree two vertices into 3-connected 3-regular planar graphs with minimum degree three) can be found here.
Instance Set 3 (random potential candidate graphs for minimal counter example, grouped by number of faces) can be found here.

All instance files are in g6-format see here for a description of the format.

The source code for the program to check if a graph contains a SFE-cycle can be found here.

Snarks which do not contain perfect pseudo-matching whose contraction leads to planar/K5-minor-free/SUD-K5-minor-free graphs

In the following we will provide a collection of all snarks with up to 32 vertices that do not contain a planarizing perfect pseudo-matching. For the definition of this property and also the terminology which will be used in the following see [1]. The algorithm used to check the properties is described in [2]. Furthermore, a collection of all snarks with up to 32 vertices provided by the House of Graphs was used.

The following table lists for each snark size (number of vertices) from 26 to 32 the total number of such snarks, the number of snarks which do not have a planarizing perfect pseudo-matching but do have a perfect pseudo-matching whose contraction leads to a K5-minor-free graph [no pppm], the number of snarks which do not have a perfect pseudo matching whose contraction leads to a K5-minor-free graph but have one whose contraction leads to a SUD-K5-minor-free graph [no k5mfppm], and the number of snarks which do not have a perfect pseudo matching whose contraction leads to a SUD-K5-minor-free graph but have one whose contraction leads to a graph containing a compatible circuit decomposition [no sk5mfppm].

You can download the list of all such snarks by pressing on the link of the appropriate cell. The graphs are listed one per line in the graph6 format, see Brendan McKay’s website for a formal definition.

Every snark with up to 24 vertices contains a planarizing perfect pseudo-matching. Furthermore, all snarks with up to 32 vertices contain a perfect pseudo-matching whose contraction leads to a graph containinga compatible circuit decomposition.

size	total number of snarks	no pppm	no k5mfppm	no sk5mfppm
26	1297	2	0	0
28	12517	45	15	0
30	139854	933	578	33
32	1764950	24268	18537	1062

3 results


2019
[3]	A Lower Bound for the Smallest Uniquely Hamiltonian Planar Graph with Minimum Degree Three Benedikt Klocker, Herbert Fleischner, Günther R. Raidl 2019, Technical report AC-TR-19-007, Algorithms and Complexity Group, TU Wien. [bibtex] [pdf]
2018
[2]	A SAT Approach for Finding Sup-Transition-Minors Benedikt Klocker, Herbert Fleischner, Günther Raidl 2018, Technical report AC-TR-18-010, Algorithms and Complexity Group, TU Wien. [bibtex] [pdf]
[1]	A Model for Finding Transition-Minors Benedikt Klocker, Herbert Fleischner, Günther Raidl 2018, Technical report AC-TR-18-009, Algorithms and Complexity Group, TU Wien. [bibtex] [pdf]

Anytime Algorithms For Solving the Longest Common Palindromic Subsequence Problem (LCPS):

Contact: Marko Djukanovic and/or Günther Raidl
Instances for the 2-LCPS problem can be found here.
The supplementary file of this research can be found here.

Battery Swapping Station Location Problem (BSSLP)

Contact: Thomas Jatschka, Günther Raidl
Instances for the EVTeC 2023 can be found here.

Service Point Distribution Problem (SPDP)

Contact: Thomas Jatschka, Günther Raidl
Instances for the EvoCOP 2019 can be found here.
Instances for the LOD 2019 are available here.
GPSPDP instances are available here.

Prize-Collecting Job Sequencing with One Common and Multiple Secondary Resources (PC-JSOCMSR)

Contact: Matthias Horn, Johannes Maschler, Günther Raidl
Instances for the PC-JSOCMSR problem can be found here. For details on the instance format see the included description. The following publications consider these benchmark instances: 6 results


2021
[6]	Multivalued Decision Diagrams for Prize-Collecting Job Sequencing with One Common and Multiple Secondary Resources Johannes Maschler, Günther R. Raidl Annals of Operations Research, volume 302, pages 407–531, 2021. [bibtex] [pdf]
[5]	A-based Construction of Decision Diagrams for a Prize-Collecting Scheduling Problem Matthias Horn, Johannes Maschler, Günther R. Raidl, Elina Rönnberg Computers & Operations Research*, volume 126, number 105125, 2021. Note: previous technical report version at https://www.ac.tuwien.ac.at/files/tr/ac-tr-18-011a.pdf [bibtex] [doi]
[4]	A* Search for Prize-Collecting Job Sequencing with One Common and Multiple Secondary Resources Matthias Horn, Günther R. Raidl, Elina Rönnberg Annals of Operations Research, volume 302, pages 477–501, 2021. Note: previous technical report version at https://www.ac.tuwien.ac.at/files/tr/ac-tr-19-002.pdf [bibtex] [pdf] [doi]
2019
[3]	Multivalued Decision Diagrams for Prize-Collecting Job Sequencing with One Common and Multiple Secondary Resources Johannes Maschler, Günther Raidl 2019, Technical report AC-TR-19-003, Algorithms and Complexity Group, TU Wien. [bibtex] [pdf]
2018
[2]	Multivalued Decision Diagrams for a Prize-Collecting Sequencing Problem Johannes Maschler, Günther R. Raidl PATAT 2018: Proceedings of the 12th International Conference of the Practice and Theory of Automated Timetabling, pages 375–397, 2018. [bibtex] [pdf]
[1]	An A* Algorithm for Solving a Prize-Collecting Sequencing Problem with One Common and Multiple Secondary Resources and Time Windows Matthias Horn, Günther R. Raidl, Elina Rönnberg PATAT 2018: Proceedings of the 12th International Conference of the Practice and Theory of Automated Timetabling, pages 235–256, 2018. [bibtex] [pdf]

String Problems

The Longest Common Subsequence Problem (LCSP)

Contact: Marc Huber and/or Günther Raidl
Instances for LCSP (LOD 2021) can be found here.

The Longest Common Palindromic Subsequence Problem (LCPSP)

Contact: Marko Djukanovic and/or Günther Raidl
Instances for LCPSP (provided by Christian Blum ) can be found here.

The Constraint Longest Common Subsequence Problem (CLCSP)

Contact: Marc Huber and/or Günther Raidl
Instances for CLCSP (LOD 2021) can be found here.

The Shortest Common Supersequence Problem (SCSP)

Contact: Marc Huber and/or Günther Raidl
Instances and results for SCSP (EvoCOP 2022) can be found here.

Particle Therapy Patient Scheduling

Contact: Johannes Maschler, Martin Riedler, Günther Raidl

Particle Therapy Patient Scheduling Problem (PTPSP)

Instances for PTPSP can be found here. For details on the instance format and the applied preprocessing see the included description. The following publications consider these benchmark instances: 3 results


2018
[3]	Particle Therapy Patient Scheduling with Limited Starting Time Variations of Daily Treatments Johannes Maschler, Günther R. Raidl International Transactions in Operational Research, 2018. [bibtex] [pdf] [doi]
[2]	Particle Therapy Patient Scheduling: Time Estimation for Scheduling Sets of Treatments Johannes Maschler, Martin Riedler, Günther R. Raidl Computer Aided Systems Theory – EUROCAST 2017, Part I, pages 364–372, 2018, Springer. [bibtex] [pdf] [doi]
2017
[1]	An Enhanced Iterated Greedy Metaheuristic for the Particle Therapy Patient Scheduling Problem Johannes Maschler, Thomas Hackl, Martin Riedler, Günther R. Raidl Proceedings of the 12th Metaheuristics International Conference, pages 465–474, 2017. [bibtex] [pdf]

First instances for PTPSP including a description of the used file format can be found here. Updated instances and results can be found here. These benchmark instances have been considered by 1 result


2016
[1]	Particle Therapy Patient Scheduling: First Heuristic Approaches Johannes Maschler, Martin Riedler, Markus Stock, Günther R. Raidl PATAT 2016: Proceedings of the 11th International Conference of the Practice and Theory of Automated Timetabling, pages 223–244, 2016. [bibtex] [pdf]

Simplified Intraday Particle Therapy Patient Scheduling Problem (SI-PTPSP)

The instances can be found here. For details on the instance format see the included README file.
The following publication considers these test instances:

1 result


2020
[1]	An Iterative Time-Bucket Refinement Algorithm for a High-Resolution Resource-Constrained Project Scheduling Problem Martin Riedler, Thomas Jatschka, Johannes Maschler, Günther R. Raidl International Transactions in Operational Research, jan 2020. [bibtex] [pdf] [doi]

Job Sequencing with One Common and Multiple Secondary Resources (JSOCMSR)

Instances for the JSOCMSR can be found here

Districting and Routing Problem for Security Control

Contact: Benjamin Biesinger, Christian Kloimüllner

Instances for the published results can be found here.

Dial-a-Ride Problem

Contact: Martin Riedler, Günther Raidl

Instances including a description of the used file format can be found here.

Network Design Problem with Relays

Contact: Markus Leitner, Ivana Ljubic, Martin Riedler, Mario Ruthmair

Instances including a description of the used file format can be found here.
Further instances from the literature can be found at https://sites.psu.edu/auk3/research/test-problems/.

Directed Network Design Problem with Relays

Contact: Markus Leitner, Ivana Ljubic, Martin Riedler, Mario Ruthmair

The modified instances by Cabral et al. are available here.
Our newly generated Euclidean instances can be found here.
The used instance format is described in format-description.txt.
The instances by Konak can be retrieved at https://sites.psu.edu/auk3/research/test-problems/.

Rooted Delay-Constrained Minimum Spanning Tree Problems

Contact: Mario Ruthmair

C++-Code for reading instance format
complete instance graphs with 100, 200, 500, 1000 nodes and random integer edge costs and delays in [1,99]

Consensus Tree Problem

Contact: Sandro Pirkwieser

instances based on real data
new artificially generated instances
new artificially generated small instances

Periodic Vehicle Routing Problem with Time Windows

Contact: Sandro Pirkwieser

We created instances based on Solomon’s VRPTW instances with 100 customers:

planning period of four days
planning period of six days
planning period of eight days

Of the newly created instances with a planning horizon of four days we generated smaller ones for the exact approaches:

having 36 customers
having 50 customers

Periodic Vehicle Routing Problem and Periodic Traveling Salesman Problem

Contact: Sandro Pirkwieser

We created larger instances having 336 to 576 customers and a planning horizon of four or six days

instances for the pvrp
instances for the ptsp

Vehicle Routing Problem with Compartments

Contact: Sandro Pirkwieser

benchmark instances of Derigs et al. available here
modified petrol benchmark instances of Derigs et al. exhibiting a harder packing subproblem

Bi-level Vehicle Routing Problem

Contact: Günther R. Raidl, Bin Hu

Full result table of “Boosting an Exact Logic-Based Benders Decomposition Approach by Variable Neighborhood Search”

Contact: Christian Kloimüllner, Petrina Papazek

The homepage of the Balancing Bicylce Sharing System (BBSS) project can be found here.

Our instances are based on real data from Citybike Wien. We got information about 92 stations which contain fill levels at different time in the system, capacity of stations, geographic coordinates, and traveling times between stations.

We constructed instances which contain:

capacity,
initial fill level,
target fill level of stations,
traveling times, and
(user) demand values for dynamic rebalancing.

Furthermore, our instances have different size of stations, namely, 10, 20, 30, 60, 90, 120, 180, 240, 300, 400, 500, 600 and 700. Thus, we got a set of artificial stations from our partner, the AIT (Austrian Institute of Technology), which extend the real data we got from Citybike Wien. In particular, we choose the stations for our instances randomly from a set of 92 real stations plus 664 artificial stations.

For the following set of instances we took a snapshot from the system of Citybike Wien to derive initial fill levels for the stations. Furthermore, we set the target value of stations to be 50% of their capacities. We used exactly one depot and also one travel time matrix and the demands for the stations are derived randomly for eight different time points.

bench2: Benchmark instances based on data from Citybike Wien (used in [1-3, 6])

In EVOCOP-2013 results we list complete computational results based on bench 2 for comparing our auxilary algroithms for deriving loading operations described in [2013,4].

The next benchmark instances contain (user) demand values for the real stations which we got from our partner, the AIT. For the artificial stations we calculated random demand values according to a Beta distribution.

Moreover, we adopted the initial fill levels and target values. The initial fill levels for the artificial stations have been calculated the same way like we did it for the demands, i.e., such that the distribution of the fill levels for artificial stations is similar to that one of the real stations. The target values were set according to their demand. For stations with a high bike demand we set the target value to be 75% of the stations capacity, for stations with a high slot demand to 25% and if both, the bike- as well as the slot demand, are similar we set their target value to 50% of the capacity.

Additionally, we provided four different travel time matrices which show the traveling time at 4:30 am, 8:00 am, 12:00 pm, as well as 6:15 pm.

bench3: Benchmark instances with advanced initial and target fill levels and time-varying travel times (used in [4,5])

Publications using these benchmark instances

7 results


2019
[7]	Algorithmic Approaches for Optimization Problems in Bike Sharing and Security Control Christian Kloimüllner mar 2019, PhD thesis, Institute of Logic and Computation, TU Wien. Note: supervised by Günther R. Raidl [bibtex] [pdf]
2015
[6]	PILOT, GRASP, and VNS Approaches for the Static Balancing of Bicycle Sharing Systems Marian Rainer-Harbach, Petrina Papazek, Günther R. Raidl, Bin Hu, Christian Kloimüllner Journal of Global Optimization, volume 63, number 3, pages 597-629, 2015, Springer. [bibtex] [pdf] [doi]
2014
[5]	Balancing Bicycle Sharing Systems: An Analysis of Path Relinking and Recombination within a GRASP Hybrid Petrina Papazek, Christian Kloimüllner, Bin Hu, Günther R. Raidl Parallel Problem Solving from Nature – PPSN XIII (Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipic, Jim Smith, eds.), volume 8672 of LNCS, pages 792–801, 2014, Springer. [bibtex] [pdf] [doi]
[4]	Balancing Bicycle Sharing Systems: An Approach for the Dynamic Case Christian Kloimüllner, Petrina Papazek, Bin Hu, Günther R. Raidl Evolutionary Computation in Combinatorial Optimization – EvoCOP 2014 (Christian Blum, Gabriela Ochoa, eds.), volume 8600 of LNCS, pages 73–84, 2014, Springer. [bibtex] [pdf] [doi]
2013
[3]	Balancing Bicycle Sharing Systems: A Variable Neighborhood Search Approach Marian Rainer-Harbach, Petrina Papazek, Bin Hu, Günther R. Raidl Evolutionary Computation in Combinatorial Optimisation – 13th European Conference, EvoCOP 2013 (M. Middendorf, C. Blum, eds.), volume 7832 of LNCS, pages 121-132, 2013, Springer. [bibtex] [pdf]
[2]	Balancing Bicycle Sharing Systems: Improving a VNS by Efficiently Determining Optimal Loading Operations Günther R. Raidl, Bin Hu, Marian Rainer-Harbach, Petrina Papazek Hybrid Metaheuristics, 8th Int. Workshop, HM 2013 (M. J. Blesa, others, eds.), volume 7919 of LNCS, pages 130–143, 2013, Springer. [bibtex] [pdf]
[1]	A PILOT/VND/GRASP Hybrid for the Static Balancing of Public Bicycle Sharing Systems Petrina Papazek, Günther R. Raidl, Marian Rainer-Harbach, Bin Hu Computer Aided Systems Theory – EUROCAST 2013 (Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia, eds.), volume 8111 of LNCS, pages 372–379, 2013, Springer. [bibtex] [pdf]

Mulitmodal Home-Healthcare Scheduling (MHS) Problem

Contact: Günther R. Raidl, Matthias Prandtstetter

Competitive Facility Location Problems

Contact: Benjamin Biesinger, Bin Hu, Günther R. Raidl

Multi Layer Hierarchical Ring Network Design Problem

Contact: Christian Schauer, Günther R. Raidl

Two Dimensional Pre-Marshalling Problem

Contact: Günther Raidl Benchmark instances for the Two Dimensional Pre-Marshalling Problem (2D-PMP) with 4,6,8,10,12,and 14 columns width, 4 stacks height and 50% and 75% fullness ratio. By combining these parameters we obtain a total of 612=12 categories of instances. Each category contains 50 instances. The instances were generated using the instance generator found at (https://sites.google.com/site/gciports/premarshalling-problem/bay-generator) provided by:

Exposito-Izquierdo, C., Melian-Batista, B., Moreno-Vega, M.: Pre-marshalling problem: Heuristic solution method and instances generator. Expert Systems with Applications 39(9) (2012) 8337-8349

File names are encoded using the following key: instance_w_h_c_x.txt

w - width
h - height
c - number of containers within bay (defined by fullness ratio)
x - instance number within category

Generalized Vehicle Routing Problem with Stochastic Demands

Contact: Benjamin Biesinger, Bin Hu, Günther R. Raidl

Benchmark instances for the Generalized Vehicle Routing Problem with Stochastic Demands based on the instances for the GVRP (http://www.personal.soton.ac.uk/tb12v07/gvrp.html) using the same format. In the demand section the expected demand d for each cluster is listed along with a number x. This number determines the possible demand values which are given by D={d-ceil(x*d),…,d+ceil(x*d)} and each demand value has equal probability 1 / |D|.
Full result tables of “A Variable Neighborhood Search for the Generalized Vehicle Routing Problem with Stochastic Demands”, accepted by the EvoCOP 2015 coference.