Instance - cap6000


	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-4322846-ryton [MIPLIB]	thor50dday [MIPLIB]	neos-876808 [MIPLIB]	ns1430538 [MIPLIB]	graph40-40-1rand [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 / 0.285	2 / 0.297	3 / 0.309	4 / 0.311	5 / 0.321
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	n3div36 [MIPLIB]	bab1 [MIPLIB]	gmu-35-40 [MIPLIB]	gmu-35-50 [MIPLIB]	gmut-76-40 [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.	18 / 0.428	294 / 1.125	443 / 1.370	571 / 1.536	925 / 2.879
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 cap6000, the five most similar instances to cap6000 according to the MIC, and the five most similar instances to cap6000 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	cap6000 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.000000	-
MIC Top 5	neos-4322846-ryton [MIPLIB]	Jeff Linderoth	(None provided)	0.285321	1
	thor50dday [MIPLIB]	Daniel Rehfeldt	Steiner tree problem in graphs instance, consisting of a complete graph with 231 vertices of which 50 are terminals. The vertices correspond to cities (mostly capitals) around the world, the edges weights correspond to the distances in km.	0.297345	2
	neos-876808 [MIPLIB]	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	0.309226	3
	ns1430538 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application.	0.310922	4
	graph40-40-1rand [MIPLIB]	Michael Bastubbe	Packing Cuts in Undirected Graphs. Instances are described in 4.1.	0.320780	5
MIPLIB Top 5	n3div36 [MIPLIB]	R. Meirich	Static line planning models on the Dutch IC network. Solved with Gurobi 4.5.1 on a 12-core Linux system in 1700.37 sec.	0.428207	18
	bab1 [MIPLIB]	Elmar Swarat, Laura Traverso, Julia Buwaya	Integrated vehicle routing and crew scheduling of toll inspectors on German highways	1.124538	294
	gmu-35-40 [MIPLIB]	Nora Konnyu	Timber harvest scheduling model These are harvest scheduling models of hypothetical forest planning problems where net timber revenues are maximized over a planning horizon subject to four sets of constraints: 1. Each management unit can be harvested only once over the planning horizon, 2. Volume harvested in one planning period should not be less or more than some portion of that in the preceding period, 3. Area-weighted average age of the forest by the end of the plan should notbe less than a certain target age. 4. Clearcut size in any planning period has to be below a specific limit. Decision variable are management units and generalized management units (group of management units with a combined area not exceeding the limit on clearcut size) and can be either fully harvested or left untouched in any planning period, therefore there is a binary restriction on the decision variables.	1.369504	443
	gmu-35-50 [MIPLIB]	Nora Konnyu	Timber harvest scheduling model These are harvest scheduling models of hypothetical forest planning problems where net timber revenues are maximized over a planning horizon subject to four sets of constraints: 1. Each management unit can be harvested only once over the planning horizon, 2. Volume harvested in one planning period should not be less or more than some portion of that in the preceding period, 3. Area-weighted average age of the forest by the end of the plan should notbe less than a certain target age. 4. Clearcut size in any planning period has to be below a specific limit. Decision variable are management units and generalized management units (group of management units with a combined area not exceeding the limit on clearcut size) and can be either fully harvested or left untouched in any planning period, therefore there is a binary restriction on the decision variables.	1.535965	571
	gmut-76-40 [MIPLIB]	Nora Konnyu	Timber harvest scheduling model These are harvest scheduling models of hypothetical forest planning problems where net timber revenues are maximized over a planning horizon subject to four sets of constraints: 1. Each management unit can be harvested only once over the planning horizon, 2. Volume harvested in one planning period should not be less or more than some portion of that in the preceding period, 3. Area-weighted average age of the forest by the end of the plan should notbe less than a certain target age. 4. Clearcut size in any planning period has to be below a specific limit. Decision variable are management units and generalized management units (group of management units with a combined area not exceeding the limit on clearcut size) and can be either fully harvested or left untouched in any planning period, therefore there is a binary restriction on the decision variables.	2.878769	925

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	sp_product	map	n37	neos-pseudoapplication-2	radiation
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 / 0.586	2 / 0.639	3 / 0.679	4 / 0.709	5 / 0.753

Model Group Summary

The table below contains summary information for the five most similar model groups to cap6000 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	sp_product	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.585624	1
	map	Kiyan Ahmadizadeh	Land parcel selection problems motivated by Red-Cockaded Woodpecker conservation problem	0.639385	2
	n37	J. Aronson	Fixed charge transportation problem	0.678831	3
	neos-pseudoapplication-2	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	0.709498	4
	radiation	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	0.752787	5

Type:	Instance
Submitter:	MIPLIB submission pool
Description:	Imported from the MIPLIB2010 submissions.
	MIPLIB Entry

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

cap6000: Instance-to-Instance Comparison Results

Parent Instance (cap6000)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

cap6000: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary