timtab1: Instance-to-Instance Comparison Results

Type: Instance
Submitter: C. Liebchen, R. Möhring
Description: Public transport scheduling problem
MIPLIB Entry

Parent Instance (timtab1)

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

timtab1 Raw timtab1 Decomposed timtab1 Composite of MIC top 5 timtab1 Composite of MIPLIB top 5 timtab1 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.
g503inf decomposed gsvm2rl12 decomposed gsvm2rl3 decomposed gt2 decomposed neos-5192052-neckar decomposed
Name g503inf [MIPLIB] gsvm2rl12 [MIPLIB] gsvm2rl3 [MIPLIB] gt2 [MIPLIB] neos-5192052-neckar [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 / 0.862 2 / 0.880 3 / 0.882 4 / 0.906 5 / 0.937
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.
g503inf raw gsvm2rl12 raw gsvm2rl3 raw gt2 raw neos-5192052-neckar 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.
timtab1CUTS decomposed ic97_potential decomposed nh97_tension decomposed icir97_potential decomposed icir97_tension decomposed
Name timtab1CUTS [MIPLIB] ic97_potential [MIPLIB] nh97_tension [MIPLIB] icir97_potential [MIPLIB] icir97_tension [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.
84 / 1.087 416 / 1.324 498 / 1.385 607 / 1.522 856 / 2.091
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.
timtab1CUTS raw ic97_potential raw nh97_tension raw icir97_potential raw icir97_tension raw

Instance Summary

The table below contains summary information for timtab1, the five most similar instances to timtab1 according to the MIC, and the five most similar instances to timtab1 according to MIPLIB 2017.

INSTANCE SUBMITTER DESCRIPTION ISS RANK
Parent Instance timtab1 [MIPLIB] C. Liebchen, R. Möhring Public transport scheduling problem 0.000000 -
MIC Top 5 g503inf [MIPLIB] MIPLIB submission pool Imported from the MIPLIB2010 submissions. 0.862331 1
gsvm2rl12 [MIPLIB] Toni Sorrell Suport vector machine with ramp loss. GSVM2-RL is the formulation found in Hess E. and Brooks P. (2015) paper, The Support Vector Machine and Mixed Integer Linear Programming: Ramp Loss SVM with L1-Norm Regularization 0.879944 2
gsvm2rl3 [MIPLIB] Toni Sorrell Suport vector machine with ramp loss. GSVM2-RL is the formulation found in Hess E. and Brooks P. (2015) paper, The Support Vector Machine and Mixed Integer Linear Programming: Ramp Loss SVM with L1-Norm Regularization 0.881901 3
gt2 [MIPLIB] MIPLIB submission pool Imported from the MIPLIB2010 submissions. 0.906331 4
neos-5192052-neckar [MIPLIB] Jeff Linderoth (None provided) 0.936801 5
MIPLIB Top 5 timtab1CUTS [MIPLIB] C. Liebchen, R. Möhring Public transport scheduling problem 1.086992 84
ic97_potential [MIPLIB] L. Peeters A model for cyclic railway timetable optimization 1.323844 416
nh97_tension [MIPLIB] MIPLIB submission pool Imported from the MIPLIB2010 submissions. 1.384745 498
icir97_potential [MIPLIB] MIPLIB submission pool Imported from the MIPLIB2010 submissions. 1.521857 607
icir97_tension [MIPLIB] MIPLIB submission pool Imported from the MIPLIB2010 submissions. 2.090911 856


timtab1: Instance-to-Model Comparison Results

Model Group Assignment from MIPLIB: timtab
Assigned Model Group Rank/ISS in the MIC: 4 / 1.367

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: neos-pseudoapplication-74 Model group: neos-pseudoapplication-109 Model group: scp Model group: timtab Model group: neos-pseudoapplication-2
Name neos-pseudoapplication-74 neos-pseudoapplication-109 scp timtab neos-pseudoapplication-2
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 / 1.233 2 / 1.326 3 / 1.341 4 / 1.367 5 / 1.415

Model Group Summary

The table below contains summary information for the five most similar model groups to timtab1 according to the MIC.

MODEL GROUP SUBMITTER DESCRIPTION ISS RANK
MIC Top 5 neos-pseudoapplication-74 Jeff Linderoth (None provided) 1.233018 1
neos-pseudoapplication-109 Jeff Linderoth (None provided) 1.326231 2
scp Shunji Umetani This is a random test model generator for SCP using the scheme of the following paper, namely the column cost c[j] are integer randomly generated from [1,100]; every column covers at least one row; and every row is covered by at least two columns. see reference: E. Balas and A. Ho, Set covering algorithms using cutting planes, heuristics, and subgradient optimization: A computational study, Mathematical Programming, 12 (1980), 37-60. We have newly generated Classes I-N with the following parameter values, where each class has five models. We have also generated reduced models by a standard pricing method in the following paper: S. Umetani and M. Yagiura, Relaxation heuristics for the set covering problem, Journal of the Operations Research Society of Japan, 50 (2007), 350-375. You can obtain the model generator program from the following web site. https://sites.google.com/site/shunjiumetani/benchmark 1.340937 3
timtab C. Liebchen, R. Möhring Public transport scheduling problem 1.367374 4
neos-pseudoapplication-2 NEOS Server Submission Imported from the MIPLIB2010 submissions. 1.415451 5