Instance - biella1

biella1: Instance-to-Instance Comparison Results

Type:	Instance
Submitter:	Double-Click SAS
Description:	Crew scheduling instance
	MIPLIB Entry


	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	dc1c [MIPLIB]	trento1 [MIPLIB]	dolom1 [MIPLIB]	pythago7825 [MIPLIB]	core2586-950 [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 / 0.802	2 / 0.803	3 / 0.926	4 / 1.143	5 / 1.145
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	dc1c [MIPLIB]	trento1 [MIPLIB]	dolom1 [MIPLIB]	nsr8k [MIPLIB]	dc1l [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 / 0.802	2 / 0.803	3 / 0.926	8 / 1.211	63 / 1.637
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 biella1, the five most similar instances to biella1 according to the MIC, and the five most similar instances to biella1 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	biella1 [MIPLIB]	Double-Click SAS	Crew scheduling instance	0.000000	-
MIC Top 5	dc1c [MIPLIB]	Double-Click SAS	Crew scheduling instance. This problem was solved on ISM supercomputer Fujitsu PRIMERGY RX200S5 (http://www.ism.ac.jp/computer_system/eng/sc/index.html) by ParaSCIP in approximately 1100 hours with 14 times restarted 15 jobs.	0.801579	1
	trento1 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.802628	2
	dolom1 [MIPLIB]	Double-Click SAS	Crew scheduling instance. Solved with ParaSCIP with SCIP 3.0.1 linked to CPLEX 12.5 as an LP solver on HLRN III with 12288 cores in two runs First run found a feasible solution whose objective function value is 6615265.0000. Giving the feasible solution, the second run solved the instance in 13684.7671 sec.	0.926130	3
	pythago7825 [MIPLIB]	Felix J. L. Willamowski	2-coloring of Pythagorean triples	1.143015	4
	core2586-950 [MIPLIB]	A. Caprara, M. Fischetti, P. Toth	Set covering instance coming from Italian railway models	1.144924	5
MIPLIB Top 5	dc1c [MIPLIB]	Double-Click SAS	Crew scheduling instance. This problem was solved on ISM supercomputer Fujitsu PRIMERGY RX200S5 (http://www.ism.ac.jp/computer_system/eng/sc/index.html) by ParaSCIP in approximately 1100 hours with 14 times restarted 15 jobs.	0.801579	1
	trento1 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.802628	2
	dolom1 [MIPLIB]	Double-Click SAS	Crew scheduling instance. Solved with ParaSCIP with SCIP 3.0.1 linked to CPLEX 12.5 as an LP solver on HLRN III with 12288 cores in two runs First run found a feasible solution whose objective function value is 6615265.0000. Giving the feasible solution, the second run solved the instance in 13684.7671 sec.	0.926130	3
	nsr8k [MIPLIB]	MIPLIB submission pool	Crew scheduling instance	1.210959	8
	dc1l [MIPLIB]	Double-Click SAS	Crew scheduling instance	1.636803	63

biella1: Instance-to-Model Comparison Results

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

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	2hopcds	neos-pseudoapplication-103	f2gap	iis	mod
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 / 1.421	2 / 1.604	3 / 1.605	4 / 1.618	5 / 1.653

Model Group Summary

The table below contains summary information for the five most similar model groups to biella1 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	2hopcds	Austin Buchanan	A problem in wireless networks. The objective is to select a minimum number of relay nodes so that any two nonadjacent nodes can communicate by way of the chosen relay nodes in at most s hops, where s is a problem input. The 2-hop case of this problem can be formulated as a set cover/hitting set problem with n binary variables and n^2 constraints: _{ k N(i) N(j) } x_k 1 for nonadjacent node pairs {i,j}. Despite the formulation's simplicity, models with as few as 120 variables are left unsolved after one hour using Gurobi 7.0.2.	1.420611	1
	neos-pseudoapplication-103	Hans Mittelmann	Collection of anonymous submissions to the NEOS Server for Optimization	1.603637	2
	f2gap	Salim Haddadi	Restrictions of well-known hard generalized assignment problem models (D10400,D20400,D40400,D15900,D30900,D60900,D201600,D401600,D801600)	1.605034	3
	iis	Marc Pfetsch	23 "middlehard" Set-Covering Models for MIPLIB: they have a small number of variables compared to the number of constraints and CPLEX 12.1 needs about one hour to solve them.For more information, have a look into the readme file which explains how the models can be created.	1.618434	4
	mod	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.652558	5

biella1: Instance-to-Instance Comparison Results

Parent Instance (biella1)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

biella1: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary