rail02: Instance-to-Instance Comparison Results

Type: Instance
Submitter: Thomas Schlechte
Description: Track allocation problem modeled as arc coupling problem
MIPLIB Entry

Parent Instance (rail02)

All other instances below were be compared against this "query" instance.

rail02 Raw rail02 Decomposed rail02 Composite of MIC top 5 rail02 Composite of MIPLIB top 5 rail02 Model Group Composite
Raw This is the CCM image before the decomposition procedure has been applied.
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.
Composite of MIC Top 5 Composite of the five decomposed CCM images from the MIC Top 5.
Composite of MIPLIB Top 5 Composite of the five decomposed CCM images from the MIPLIB Top 5.
Model Group Composite Image Composite of the decomposed CCM images for every instance in the same model group as this query.

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.
rail01 decomposed n3seq24 decomposed neos-5018451-chiese decomposed neos-4984115-canche decomposed blp-ar98 decomposed
Name rail01 [MIPLIB] n3seq24 [MIPLIB] neos-5018451-chiese [MIPLIB] neos-4984115-canche [MIPLIB] blp-ar98 [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.
1 / 0.736 2 / 0.789 3 / 0.820 4 / 0.876 5 / 0.886
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.
rail01 raw n3seq24 raw neos-5018451-chiese raw neos-4984115-canche raw blp-ar98 raw

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.
rail01 decomposed rail03 decomposed bab6 decomposed neos-555001 decomposed neos-780889 decomposed
Name rail01 [MIPLIB] rail03 [MIPLIB] bab6 [MIPLIB] neos-555001 [MIPLIB] neos-780889 [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.
1 / 0.736 15 / 1.113 180 / 1.677 356 / 1.855 425 / 1.908
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.
rail01 raw rail03 raw bab6 raw neos-555001 raw neos-780889 raw

Instance Summary

The table below contains summary information for rail02, the five most similar instances to rail02 according to the MIC, and the five most similar instances to rail02 according to MIPLIB 2017.

INSTANCE SUBMITTER DESCRIPTION ISS RANK
Parent Instance rail02 [MIPLIB] Thomas Schlechte Track allocation problem modeled as arc coupling problem 0.000000 -
MIC Top 5 rail01 [MIPLIB] Thomas Schlechte Track allocation problem modeled as arc coupling problem 0.736224 1
n3seq24 [MIPLIB] R. Meirich Static line planning models on the Dutch IC network 0.788898 2
neos-5018451-chiese [MIPLIB] Jeff Linderoth (None provided) 0.820154 3
neos-4984115-canche [MIPLIB] Jeff Linderoth (None provided) 0.875617 4
blp-ar98 [MIPLIB] M. Lübbecke Railway line planning instance 0.885714 5
MIPLIB Top 5 rail01 [MIPLIB] Thomas Schlechte Track allocation problem modeled as arc coupling problem 0.736224 1
rail03 [MIPLIB] Thomas Schlechte Track allocation problem modeled as arc coupling problem The problem was solved by CPLEX 12.4. It took approximately 170 hours. 1.113418 15
bab6 [MIPLIB] Elmar Swarat Vehicle routing with profit and an integrated crew scheduling like bab2 - bab5. Instances differ in multi-commodity-flow formulation (path oder arc formulation) or time discretization and some are quite easy to solve while others (bab2, bab3 and bab6) are very difficult. 1.676555 180
neos-555001 [MIPLIB] NEOS Server Submission Imported from the MIPLIB2010 submissions. 1.854838 356
neos-780889 [MIPLIB] NEOS Server Submission Imported from the MIPLIB2010 submissions. 1.907962 425


rail02: Instance-to-Model Comparison Results

Model Group Assignment from MIPLIB: rail0
Assigned Model Group Rank/ISS in the MIC: 1 / 1.234

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.
Model group: rail0 Model group: neos-pseudoapplication-69 Model group: blp Model group: c1s1 Model group: chromaticindex
Name rail0 neos-pseudoapplication-69 blp c1s1 chromaticindex
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.
1 / 1.235 2 / 1.436 3 / 1.535 4 / 1.566 5 / 1.636

Model Group Summary

The table below contains summary information for the five most similar model groups to rail02 according to the MIC.

MODEL GROUP SUBMITTER DESCRIPTION ISS RANK
MIC Top 5 rail0 Thomas Schlechte Track allocation problem modeled as arc coupling problem The problem was solved by CPLEX 12.4. It took approximately 170 hours. 1.234672 1
neos-pseudoapplication-69 Jeff Linderoth (None provided) 1.435889 2
blp M. Lübbecke Railway line planning model. Solved using CPLEX 12.3 (12 threads) on an Intel Xeon X5650 @ 2.67GHz, 12MB cache, 24GB RAM in 4947.5 sec.\xa0Solved using Gurobi 4.6.1 (12 threads) in 1867.9 sec. 1.535119 3
c1s1 M. Vyve, Y. Pochet Lot sizing model. 1.565952 4
chromaticindex Pierre Le Bodic Simple edge-coloring model on chains of Petersen-like subgraphs, designed to fool MIP solvers into producing very large Branch-and-Bound trees. 1.635710 5


Updated August 16, 2019