Instance - stockholm


	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	zeil [MIPLIB]	supportcase14 [MIPLIB]	neos16 [MIPLIB]	neos-3610173-itata [MIPLIB]	neos-3610051-istra [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 / 1.189	2 / 1.214	3 / 1.271	4 / 1.276	5 / 1.280
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	neos-619167 [MIPLIB]	neos-3754224-navua [MIPLIB]	eva1aprime5x5opt [MIPLIB]	eva1aprime6x6opt [MIPLIB]	neos-5018451-chiese [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.	144 / 1.629	493 / 1.855	568 / 1.916	698 / 2.046	907 / 2.617
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 stockholm, the five most similar instances to stockholm according to the MIC, and the five most similar instances to stockholm according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	stockholm [MIPLIB]	Paul Rubin	Toll booth placement problem	0.000000	-
MIC Top 5	zeil [MIPLIB]	Andreas Bärmann	A model that computes an optimal adaptation of a given timetable draft for a small portion of the German railway network. The aim is to shift the planned departure times of the trains slightly, such that the maximum power consumption (averaed over 15-minute intervalls of the planning horizon) is as small as possible.	1.188885	1
	supportcase14 [MIPLIB]	Michael Winkler	MIP instances collected from Gurobi forum with unknown application	1.213597	2
	neos16 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application	1.271144	3
	neos-3610173-itata [MIPLIB]	Jeff Linderoth	(None provided)	1.275517	4
	neos-3610051-istra [MIPLIB]	Jeff Linderoth	(None provided)	1.279988	5
MIPLIB Top 5	neos-619167 [MIPLIB]	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	1.628533	144
	neos-3754224-navua [MIPLIB]	Jeff Linderoth	(None provided)	1.855338	493
	eva1aprime5x5opt [MIPLIB]	Yoshihiro Kanno	MILP approach to generate structures with negative thermal expansion coefficients	1.915631	568
	eva1aprime6x6opt [MIPLIB]	Yoshihiro Kanno	MILP approach to generate structures with negative thermal expansion coefficients	2.045878	698
	neos-5018451-chiese [MIPLIB]	Jeff Linderoth	(None provided)	2.616746	907

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	scp	neos-pseudoapplication-91	stein	fillomino	rococo
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 / 2.194	2 / 2.205	3 / 2.243	4 / 2.253	5 / 2.274

Model Group Summary

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

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	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	2.194354	1
	neos-pseudoapplication-91	Jeff Linderoth	(None provided)	2.205414	2
	stein	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	2.242915	3
	fillomino	Gleb Belov	These are the models from MiniZinc Challenges 2012-2016 (see www.minizinc.org), compiled for MIP WITH INDICATOR CONSTRAINTS using the develop branch of MiniZinc and CPLEX 12.7.1 on 30 April 2017. Thus, these models can only be handled by solvers accepting indicator constraints. For models compiled with big-M/domain decomposition only, see my previous submission to MIPLIB.To recompile, create a directory MODELS, a list lst12_16.txt of the models with full paths to mzn/dzn files of each model per line, and say$> ~/install/libmzn/tests/benchmarking/mzn-test.py -l ../lst12_16.txt -slvPrf MZN-CPLEX -debug 1 -addOption "-timeout 3 -D fIndConstr=true -D fMIPdomains=false" -useJoinedName "-writeModel MODELS_IND/%s.mps" Alternatively, you can compile individual model as follows: $> mzn-cplex -v -s -G linear -output-time ../challenge_2012_2016/mznc2016_probs/zephyrus/zephyrus.mzn ../challenge_2012_2016/mznc2016_p/zephyrus/14__8__6__3.dzn -a -timeout 3 -D fIndConstr=true -D fMIPdomains=false -writeModel MODELS_IND/challenge_2012_2016mznc2016_probszephyruszephyrusmzn-challenge_2012_2016mznc2016_probszephyrus14__8__6__3dzn.mps	2.252936	4
	rococo	A. Chabrier, E. Danna, C. Le Pape, L. Perron	Model for dimensioning the arc capacities in a telecommunication network. Solved by Gurobi 4.5.1 (4 threads) in 66892.47 seconds.	2.274472	5

Type:	Instance
Submitter:	Paul Rubin
Description:	Toll booth placement problem
	MIPLIB Entry

Model Group Assignment from MIPLIB:	no model group assignment
Assigned Model Group Rank/ISS in the MIC:	N.A. / N.A.

stockholm: Instance-to-Instance Comparison Results

Parent Instance (stockholm)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

stockholm: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary