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ivu06: Instance-to-Instance Comparison Results
Type: | Instance |
Submitter: | S. Weider |
Description: | Set partitioning instance resulting from a column generation algorithm used for duty scheduling in public transportation. Solved in June 2014 using CPLEX 12.6 with 48 threads in about 25 days. |
MIPLIB Entry |
Parent Instance (ivu06)
All other instances below were be compared against this "query" instance.
Raw
This is the CCM image before the decomposition procedure has been applied.
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Decomposed
This is the CCM image after a decomposition procedure has been applied. This is the image used by the MIC's image-based comparisons for this query instance.
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Composite of MIC Top 5
Composite of the five decomposed CCM images from the MIC Top 5.
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Composite of MIPLIB Top 5
Composite of the five decomposed CCM images from the MIPLIB Top 5.
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Model Group Composite Image
Composite of the decomposed CCM images for every instance in the same model group as this query.
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MIC Top 5 Instances
These are the 5 decomposed CCM images that are most similar to decomposed CCM image for the the query instance, according to the ISS metric.
Decomposed
These decomposed images were created by GCG.
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Name | ds-big [MIPLIB] | sorrell3 [MIPLIB] | sorrell7 [MIPLIB] | ex10 [MIPLIB] | t1717 [MIPLIB] | |
Rank / ISS
The image-based structural similarity (ISS) metric measures the Euclidean distance between the image-based feature vectors for the query instance and all other instances. A smaller ISS value indicates greater similarity.
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1 / 1.038 | 2 / 1.112 | 3 / 1.113 | 4 / 1.173 | 5 / 1.195 | |
Raw
These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.
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MIPLIB Top 5 Instances
These are the 5 instances that are most closely related to the query instance, according to the instance statistic-based similarity measure employed by MIPLIB 2017
Decomposed
These decomposed images were created by GCG.
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Name | ds-big [MIPLIB] | eilC76-2 [MIPLIB] | eilA101-2 [MIPLIB] | nw04 [MIPLIB] | ds-big** [MIPLIB] | |
Rank / ISS
The image-based structural similarity (ISS) metric measures the Euclidean distance between the image-based feature vectors for the query instance and all model groups. A smaller ISS value indicates greater similarity.
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1 / 1.038 | 39 / 2.034 | 55 / 2.334 | 142 / 3.169 | N.A.** / N.A.** | |
Raw
These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.
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Instance Summary
The table below contains summary information for ivu06, the five most similar instances to ivu06 according to the MIC, and the five most similar instances to ivu06 according to MIPLIB 2017.
INSTANCE | SUBMITTER | DESCRIPTION | ISS | RANK | |
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Parent Instance | ivu06 [MIPLIB] | S. Weider | Set partitioning instance resulting from a column generation algorithm used for duty scheduling in public transportation. Solved in June 2014 using CPLEX 12.6 with 48 threads in about 25 days. | 0.000000 | - |
MIC Top 5 | ds-big [MIPLIB] | Steffen Weider | Bus driver duty scheduling problem | 1.037667 | 1 |
sorrell3 [MIPLIB] | Toni Sorrell | These instances are based on Neil Sloane's Challenge problems: Independent Sets in Graphs. | 1.112392 | 2 | |
sorrell7 [MIPLIB] | Toni Sorrell | These instances are based on Neil Sloane's Challenge problems: Independent Sets in Graphs. | 1.112760 | 3 | |
ex10 [MIPLIB] | Iulian Ober | Formulations of Boolean SAT instance | 1.173380 | 4 | |
t1717 [MIPLIB] | R. Borndörfer | Vehicle scheduling set partitioning problem from Berlin's Telebus handicapped people's transportation system | 1.195366 | 5 | |
MIPLIB Top 5 | ds-big [MIPLIB] | Steffen Weider | Bus driver duty scheduling problem | 1.037667 | 1 |
eilC76-2 [MIPLIB] | J. Linderoth | Set partitioning problem approximation for capicated vehicle routing problem instance from TSPLIB Imported from the MIPLIB2010 submissions. | 2.034150 | 39 | |
eilA101-2 [MIPLIB] | J. Linderoth | Set partitioning problem approximation for capicated vehicle routing problem instance from TSPLIB | 2.333537 | 55 | |
nw04 [MIPLIB] | MIPLIB submission pool | Imported from the MIPLIB2010 submissions. | 3.169351 | 142 | |
ds-big** [MIPLIB] | S. Weider | Set partitioning instance resulting from a column generation algorithm used for duty scheduling in public transportation | N.A.** | N.A.** |
ivu06: Instance-to-Model Comparison Results
Model Group Assignment from MIPLIB: | ivu |
Assigned Model Group Rank/ISS in the MIC: | 1 / 1.067 |
MIC Top 5 Model Groups
These are the 5 model group composite (MGC) images that are most similar to the decomposed CCM image for the query instance, according to the ISS metric.
These are model group composite (MGC) images for the MIC top 5 model groups.
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Name | ivu | reblock | core | air | maritime | |
Rank / ISS
The image-based structural similarity (ISS) metric measures the Euclidean distance between the image-based feature vectors for the query instance and all other instances. A smaller ISS value indicates greater similarity.
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1 / 1.067 | 2 / 1.175 | 3 / 1.399 | 4 / 1.439 | 5 / 1.450 |
Model Group Summary
The table below contains summary information for the five most similar model groups to ivu06 according to the MIC.
MODEL GROUP | SUBMITTER | DESCRIPTION | ISS | RANK | |
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MIC Top 5 | ivu | S. Weider | Set partitioning model resulting from a column generation algorithm used for duty scheduling in public transportation. Solved in June 2014 using CPLEX 12.6 with 48 threads in about 25 days. | 1.067374 | 1 |
reblock | Andreas Bley | Multi-period mine production scheduling model. Solved using ug[SCIP/spx], a distributed massively parallel version of SCIP run on 2,000 cores at the HLRN-II super computer facility. | 1.174643 | 2 | |
core | A. Caprara, M. Fischetti, P. Toth | Set covering model coming from Italian railway models | 1.399316 | 3 | |
air | G. Astfalk | Airline crew scheduling set partitioning problem | 1.439409 | 4 | |
maritime | Dimitri Papageorgiou | Maritime Inventory Routing Problems: Jiang-Grossmann Models. These models are available at https://mirplib.scl.gatech.edu/models, along with a host of additional information such as the underlying data used to generate the model, best known upper and lower bounds, and more. They involve a single product maritime inventory routing problem and explore the use of continuous and discrete time models. A continuous-time model based on time slots for single docks is used for some models. A model based on event points to handle parallel docks is used in others. A discrete time model based on a single commodity fixed-charge network flow problem (FCNF) is used for other models. All the models are solved for multiple randomly generated models of different problems to compare their computational efficiency. | 1.450105 | 5 |