Instance - n2seq36q


	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.

Decomposed These decomposed images were created by GCG.
Name	rwth-timetable [MIPLIB]	tpl-tub-ws1617 [MIPLIB]	nh97_tension [MIPLIB]	tpl-tub-ss16 [MIPLIB]	neos-3148108-pahi [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 / 1.338	2 / 1.354	3 / 1.385	4 / 1.421	5 / 1.545
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIC top 5.

Decomposed These decomposed images were created by GCG.
Name	bab5 [MIPLIB]	bab3 [MIPLIB]	neos-3555904-turama [MIPLIB]	neos-941313 [MIPLIB]	neos-859770 [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.	17 / 1.766	67 / 2.009	137 / 2.122	414 / 2.290	748 / 2.415
Raw These images represent the CCM images in their raw forms (before any decomposition was applied) for the MIPLIB top 5.

Instance Summary

The table below contains summary information for n2seq36q, the five most similar instances to n2seq36q according to the MIC, and the five most similar instances to n2seq36q according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	n2seq36q [MIPLIB]	R. Meirich	Static line planning models on the Dutch IC network	0.000000	-
MIC Top 5	rwth-timetable [MIPLIB]	Gerald Lach	University Course Timetabling from the RWTH Aachen	1.337524	1
	tpl-tub-ws1617 [MIPLIB]	János Höner	Model for the Post-Enrollment Course Timetabling Problem at TU Berlin from the summer term 2016 and the winter term 2016/2017	1.353864	2
	nh97_tension [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.384923	3
	tpl-tub-ss16 [MIPLIB]	János Höner	Model for the Post-Enrollment Course Timetabling Problem at TU Berlin from the summer term 2016 and the winter term 2016/2017	1.420913	4
	neos-3148108-pahi [MIPLIB]	Jeff Linderoth	(None provided)	1.545240	5
MIPLIB Top 5	bab5 [MIPLIB]	Elmar Swarat	Vehicle routing with profits and an integrated crew scheduling problem formulated by two coupled multi-commodity flow problems	1.765910	17
	bab3 [MIPLIB]	Elmar Swarat	Vehicle routing with profits and an integrated crew scheduling problem formulated by two coupled multi-commodity flow problems	2.008821	67
	neos-3555904-turama [MIPLIB]	Hans Mittelmann	Collection of anonymous submissions to the NEOS Server for Optimization	2.122112	137
	neos-941313 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application	2.289652	414
	neos-859770 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application	2.414614	748

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	huefner	cryptanalysis	scp	neos-pseudoapplication-21	generated
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 / 1.784	2 / 1.959	3 / 1.982	4 / 2.006	5 / 2.033

Model Group Summary

The table below contains summary information for the five most similar model groups to n2seq36q according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	huefner	Falk Hueffner	The NP-hard Balanced Subgraph problem (variant of MaxCut) encoded as ILPs. Real-world models from two applications from bioinformatics, finding monotone subsystems in gene regulatory networks (http://dx.doi.org/10.1007/s10878-009-9212-2) and finding optimal layouts of tanglegrams (http://dx.doi.org/10.1007/978-3-642-11269-0).	1.783932	1
	cryptanalysis	Gleb Belov	Linearized Constraint Programming models of the MiniZinc Challenges 2012-2016. I should be able to produce versions with indicator constraints supported by Gurobi and CPLEX, however don't know if you can use them and if there is a standard format. These MPS were produced by Gurobi 7.0.2 using the MiniZinc develop branch on eb536656062ca13325a96b5d0881742c7d0e3c38	1.959010	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	1.981764	3
	neos-pseudoapplication-21	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	2.006126	4
	generated	Simon Bowly	Randomly generated integer and binary programming models. These results are part of an early phase of work aimed at generating diverse and challenging MIP models for experimental testing. We have aimed to produce small integer and binary programming models which are reasonably difficult to solve and have varied structure, eliciting a range of behaviour in state of the art algorithms.	2.033197	5

Type:	Instance
Submitter:	R. Meirich
Description:	Static line planning models on the Dutch IC network
	MIPLIB Entry

Model Group Assignment from MIPLIB:	nseq
Assigned Model Group Rank/ISS in the MIC:	15 / 2.242

n2seq36q: Instance-to-Instance Comparison Results

Parent Instance (n2seq36q)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

n2seq36q: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary