Instance - bab6

bab6: Instance-to-Instance Comparison Results

Type:	Instance
Submitter:	Elmar Swarat
Description:	Vehicle routing with profit and an integrated crew scheduling like bab2 - bab5. Instances differ in multi-commodity-flow formulation (path oder arc formulation) or time discretization and some are quite easy to solve while others (bab2, bab3 and bab6) are very difficult.
	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	neos-3661949-lesse [MIPLIB]	neos-2746589-doon [MIPLIB]	mik-250-20-75-5 [MIPLIB]	gsvm2rl5 [MIPLIB]	gsvm2rl9 [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 / 1.129	2 / 1.131	3 / 1.143	4 / 1.145	5 / 1.152
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	rail03 [MIPLIB]	rail02 [MIPLIB]	rail01 [MIPLIB]	bab5 [MIPLIB]	bab2 [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.	251 / 1.592	377 / 1.677	398 / 1.691	672 / 1.878	785 / 2.022
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 bab6, the five most similar instances to bab6 according to the MIC, and the five most similar instances to bab6 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	bab6 [MIPLIB]	Elmar Swarat	Vehicle routing with profit and an integrated crew scheduling like bab2 - bab5. Instances differ in multi-commodity-flow formulation (path oder arc formulation) or time discretization and some are quite easy to solve while others (bab2, bab3 and bab6) are very difficult.	0.000000	-
MIC Top 5	neos-3661949-lesse [MIPLIB]	Jeff Linderoth	(None provided)	1.128867	1
	neos-2746589-doon [MIPLIB]	Jeff Linderoth	(None provided)	1.130615	2
	mik-250-20-75-5 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.142547	3
	gsvm2rl5 [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	1.145027	4
	gsvm2rl9 [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	1.152421	5
MIPLIB Top 5	rail03 [MIPLIB]	Thomas Schlechte	Track allocation problem modeled as arc coupling problem The problem was solved by CPLEX 12.4. It took approximately 170 hours.	1.592023	251
	rail02 [MIPLIB]	Thomas Schlechte	Track allocation problem modeled as arc coupling problem	1.676555	377
	rail01 [MIPLIB]	Thomas Schlechte	Track allocation problem modeled as arc coupling problem	1.691012	398
	bab5 [MIPLIB]	Elmar Swarat	Vehicle routing with profits and an integrated crew scheduling problem formulated by two coupled multi-commodity flow problems	1.877535	672
	bab2 [MIPLIB]	Elmar Swarat	Vehicle Routing with profits and an integrated crew scheduling formulated by two coupled multi-commodity flow problems.	2.022234	785

bab6: Instance-to-Model Comparison Results

Model Group Assignment from MIPLIB:	bab
Assigned Model Group Rank/ISS in the MIC:	216 / 3.659

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	neos-pseudoapplication-4	neos-pseudoapplication-108	mik_250	drayage	supportvectormachine
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 / 1.816	2 / 1.820	3 / 1.846	4 / 1.948	5 / 1.974

Model Group Summary

The table below contains summary information for the five most similar model groups to bab6 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	neos-pseudoapplication-4	Jeff Linderoth	(None provided)	1.816102	1
	neos-pseudoapplication-108	NEOS Server Submission	Model coming from the NEOS Server with unknown application	1.820277	2
	mik_250	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.845694	3
	drayage	F. Jordan Srour	The .rar file contains three folders: 1) R_mps with all of the models (165, organized into 5 groups R0_, R25_, R50_, R75_, and R100_*), 2) results_and_runtimes with datafiles on the runtime and results, and 3) doc with documentation on the models in the form of a pdf.	1.948346	4
	supportvectormachine	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	1.974056	5

bab6: Instance-to-Instance Comparison Results

Parent Instance (bab6)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

bab6: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary