Resources | AC Group | TU Wien

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.

Virtual Network Mapping Problem

(Contact: Johannes Inführ, Günther Raidl)

Benchmark Set for the VNMP

VNMP Benchmark Set

Solutions Achieved by Heuristic and Exact Approaches

Benchmark Set for the VNMP-DRL

Data Supplement for EvoCOP 2013

Data Supplement

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

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

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)


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)


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]


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

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.

We constructed instances which contain:

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

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].

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

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.

Resources | AC Group | TU Wien

Problem Instances

Electric Autonomous Dial-a-Ride Problem

Graph Burning Problem

Interactive Job Scheduling Problem

Roman Domination Problem

EV Fleet Charging and Allocation Problem

EV charging scheduling problem with SOC-dependent maximum charging power

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

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

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

Battery Swapping Station Location Problem (BSSLP)

Service Point Distribution Problem (SPDP)

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

String Problems

The Longest Common Subsequence Problem (LCSP)

The Longest Common Palindromic Subsequence Problem (LCPSP)

The Constraint Longest Common Subsequence Problem (CLCSP)

The Shortest Common Supersequence Problem (SCSP)

Particle Therapy Patient Scheduling

Particle Therapy Patient Scheduling Problem (PTPSP)

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

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

Districting and Routing Problem for Security Control

Dial-a-Ride Problem

Network Design Problem with Relays

Directed Network Design Problem with Relays

Rooted Delay-Constrained Minimum Spanning Tree Problems

Consensus Tree Problem

Periodic Vehicle Routing Problem with Time Windows

Periodic Vehicle Routing Problem and Periodic Traveling Salesman Problem

Vehicle Routing Problem with Compartments

Bi-level Vehicle Routing Problem

Balancing Bicycle Sharing System (BBSS) Problem

Mulitmodal Home-Healthcare Scheduling (MHS) Problem

Competitive Facility Location Problems

Multi Layer Hierarchical Ring Network Design Problem

Two Dimensional Pre-Marshalling Problem

Generalized Vehicle Routing Problem with Stochastic Demands

Virtual Network Mapping Problem

Benchmark Set for the VNMP

Solutions Achieved by Heuristic and Exact Approaches

Benchmark Set for the VNMP-DRL

Data Supplement for EvoCOP 2013

Problem Instances

Electric Autonomous Dial-a-Ride Problem

Graph Burning Problem

Interactive Job Scheduling Problem

Roman Domination Problem

EV Fleet Charging and Allocation Problem

EV charging scheduling problem with SOC-dependent maximum charging power

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

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

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

Battery Swapping Station Location Problem (BSSLP)

Service Point Distribution Problem (SPDP)

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

String Problems

The Longest Common Subsequence Problem (LCSP)

The Longest Common Palindromic Subsequence Problem (LCPSP)

The Constraint Longest Common Subsequence Problem (CLCSP)

The Shortest Common Supersequence Problem (SCSP)

Particle Therapy Patient Scheduling

Particle Therapy Patient Scheduling Problem (PTPSP)

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

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

Districting and Routing Problem for Security Control

Dial-a-Ride Problem

Network Design Problem with Relays

Directed Network Design Problem with Relays

Rooted Delay-Constrained Minimum Spanning Tree Problems

Consensus Tree Problem

Periodic Vehicle Routing Problem with Time Windows

Periodic Vehicle Routing Problem and Periodic Traveling Salesman Problem

Vehicle Routing Problem with Compartments

Bi-level Vehicle Routing Problem

Balancing Bicycle Sharing System (BBSS) Problem

Mulitmodal Home-Healthcare Scheduling (MHS) Problem

Competitive Facility Location Problems

Multi Layer Hierarchical Ring Network Design Problem