Instance - neos-3402294-bobin


	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	icir97_potential [MIPLIB]	physiciansched3-3 [MIPLIB]	physiciansched3-4 [MIPLIB]	comp12-2idx [MIPLIB]	hypothyroid-k1 [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 / 1.273	2 / 1.289	3 / 1.310	4 / 1.322	5 / 1.327
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	graphdraw-mainerd [MIPLIB]	graphdraw-opmanager [MIPLIB]	neos-3695882-vesdre [MIPLIB]	graphdraw-grafo2 [MIPLIB]	graphdraw-mainerd** [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.	757 / 2.209	837 / 2.311	855 / 2.344	871 / 2.372	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.

Instance Summary

The table below contains summary information for neos-3402294-bobin, the five most similar instances to neos-3402294-bobin according to the MIC, and the five most similar instances to neos-3402294-bobin according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	neos-3402294-bobin [MIPLIB]	Jeff Linderoth	(None provided)	0.000000	-
MIC Top 5	icir97_potential [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.272612	1
	physiciansched3-3 [MIPLIB]	Pelin Damci-Kurt	Physician scheduling problem for hospitalist, radiology and kidney specialist groups.	1.289348	2
	physiciansched3-4 [MIPLIB]	Pelin Damci-Kurt	Physician scheduling problem for hospitalist, radiology and kidney specialist groups.	1.309991	3
	comp12-2idx [MIPLIB]	Matias Sørensen	Instances comp01-21 of curriculum based course timetabling from the International Timetabling Competition 2007. These are time-assignment models (Stage I of the decomposed model), which are smaller than the full model, but still hard to solve.	1.321808	4
	hypothyroid-k1 [MIPLIB]	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	1.327073	5
MIPLIB Top 5	graphdraw-mainerd [MIPLIB]	Cézar Augusto Nascimento e Silva	In the Graph Drawing problem a set of symbols must be placed in a plane and their connections routed. The objective is to produce aesthetically pleasant, easy to read diagrams. As a primary concern one usually tries to minimize edges crossing, edges' length, waste of space and number of bents in the connections. When formulated with these constraints the problem becomes NP-Hard . In practice many additional complicating requirements can be included, such as non-uniform sizes for symbols. Thus, some heuristics such as the generalized force-direct method and Simulated Annealing have been proposed to tackle this problem. uses a grid structure to approach the Entity-Relationship (ER) drawing problem, emphasizing the differences between ER drawing and the more classical circuit drawing problems. presented different ways of producing graph layouts (e.g.: tree, orthogonal, visibility representations, hierarchic, among others) for general graphs with applications on different subjects. The ability to automatically produce high quality layouts is very important in many applications, one of these is Software Engineering: the availability of easy to understand ER diagrams, for instance, can improve the time needed for developers to master database models and increase their productivity. Our solution approach involves two phases: (\\(i\\)) firstly the optimal placement of entities is solved, i.e.: entities are positioned so as to minimize the distances between connected entities; and (\\(ii\\)) secondly, edges are routed minimizing bends and avoiding the inclusion of connectors too close. We present the model for the first phase of our problem.	2.208903	757
	graphdraw-opmanager [MIPLIB]	Cézar Augusto Nascimento e Silva	In the Graph Drawing problem a set of symbols must be placed in a plane and their connections routed. The objective is to produce aesthetically pleasant, easy to read diagrams. As a primary concern one usually tries to minimize edges crossing, edges' length, waste of space and number of bents in the connections. When formulated with these constraints the problem becomes NP-Hard . In practice many additional complicating requirements can be included, such as non-uniform sizes for symbols. Thus, some heuristics such as the generalized force-direct method and Simulated Annealing have been proposed to tackle this problem. uses a grid structure to approach the Entity-Relationship (ER) drawing problem, emphasizing the differences between ER drawing and the more classical circuit drawing problems. presented different ways of producing graph layouts (e.g.: tree, orthogonal, visibility representations, hierarchic, among others) for general graphs with applications on different subjects. The ability to automatically produce high quality layouts is very important in many applications, one of these is Software Engineering: the availability of easy to understand ER diagrams, for instance, can improve the time needed for developers to master database models and increase their productivity. Our solution approach involves two phases: (\\(i\\)) firstly the optimal placement of entities is solved, i.e.: entities are positioned so as to minimize the distances between connected entities; and (\\(ii\\)) secondly, edges are routed minimizing bends and avoiding the inclusion of connectors too close. We present the model for the first phase of our problem.	2.310579	837
	neos-3695882-vesdre [MIPLIB]	Hans Mittelmann	Collection of anonymous submissions to the NEOS Server for Optimization	2.344287	855
	graphdraw-grafo2 [MIPLIB]	Cézar Augusto Nascimento e Silva	In the Graph Drawing problem a set of symbols must be placed in a plane and their connections routed. The objective is to produce aesthetically pleasant, easy to read diagrams. As a primary concern one usually tries to minimize edges crossing, edges' length, waste of space and number of bents in the connections. When formulated with these constraints the problem becomes NP-Hard . In practice many additional complicating requirements can be included, such as non-uniform sizes for symbols. Thus, some heuristics such as the generalized force-direct method and Simulated Annealing have been proposed to tackle this problem. uses a grid structure to approach the Entity-Relationship (ER) drawing problem, emphasizing the differences between ER drawing and the more classical circuit drawing problems. presented different ways of producing graph layouts (e.g.: tree, orthogonal, visibility representations, hierarchic, among others) for general graphs with applications on different subjects. The ability to automatically produce high quality layouts is very important in many applications, one of these is Software Engineering: the availability of easy to understand ER diagrams, for instance, can improve the time needed for developers to master database models and increase their productivity. Our solution approach involves two phases: (\\(i\\)) firstly the optimal placement of entities is solved, i.e.: entities are positioned so as to minimize the distances between connected entities; and (\\(ii\\)) secondly, edges are routed minimizing bends and avoiding the inclusion of connectors too close. We present the model for the first phase of our problem.	2.371927	871
	graphdraw-mainerd** [MIPLIB]	Jeff Linderoth	(None provided)	N.A.**	N.A.**

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	hypothyroid	neos-pseudoapplication-91	bnatt	scp	neos-pseudoapplication-21
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 / 1.858	2 / 1.983	3 / 2.049	4 / 2.079	5 / 2.101

Model Group Summary

The table below contains summary information for the five most similar model groups to neos-3402294-bobin according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	hypothyroid	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	1.858132	1
	neos-pseudoapplication-91	Jeff Linderoth	(None provided)	1.983454	2
	bnatt	Tatsuya Akutsu	We are submitting ILP data for identification of a singletonattractor in a Boolean newtork, which is a well-known problemin computational systems biology.This problem is known to be NP-hard and we developed a methodto transform an model of the problem to an integer linearprogram (ILP).We used ILPs from artificially generated Boolean networks ofindegree 3.The size of the networks are: 350, 400, 500.Even for the case of 500, we could not find a solution within6 hours using CPLEX 11.2 on a PC with XEON 5470 3.33GHz CPU.(This ILP corresponds to the case of size=350.File format is (zipped) CPLEX LP format.)The details of the method appeared in:T. Akutsu, M. Hayashida and T. Tamura, Integer programming-basedmethods for attractor detection and control of Boolean networks,Proc. The combined 48th IEEE Conference on Decision and Controland 28th Chinese Control Conference (IEEE CDC/CCC 2009), 5610-5617, 2009.	2.048505	3
	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	2.079248	4
	neos-pseudoapplication-21	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	2.100802	5

Type:	Instance
Submitter:	Jeff Linderoth
Description:	(None provided)
	MIPLIB Entry

Model Group Assignment from MIPLIB:	neos-pseudoapplication-71
Assigned Model Group Rank/ISS in the MIC:	137 / 2.893

neos-3402294-bobin: Instance-to-Instance Comparison Results

Parent Instance (neos-3402294-bobin)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

neos-3402294-bobin: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary