Instance - supportcase31


	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	k16x240b [MIPLIB]	ab51-40-100 [MIPLIB]	ab69-40-100 [MIPLIB]	ab67-40-100 [MIPLIB]	ab72-40-100 [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 / 0.361	2 / 0.367	3 / 0.376	4 / 0.379	5 / 0.379
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	blp-ic97 [MIPLIB]	blp-ic98 [MIPLIB]	gmut-76-40 [MIPLIB]	gmut-76-50 [MIPLIB]	gmut-75-50 [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.	320 / 1.185	472 / 1.408	927 / 2.871	954 / 3.147	965 / 3.365
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 supportcase31, the five most similar instances to supportcase31 according to the MIC, and the five most similar instances to supportcase31 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	supportcase31 [MIPLIB]	Domenico Salvagnin	Instance coming from IBM developerWorks forum with unknown application.	0.000000	-
MIC Top 5	k16x240b [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.360858	1
	ab51-40-100 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.367177	2
	ab69-40-100 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.375609	3
	ab67-40-100 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.378622	4
	ab72-40-100 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.378886	5
MIPLIB Top 5	blp-ic97 [MIPLIB]	M. Lübbecke	Railway line planning instance. Solved using CPLEX 12.3 (12 threads) on an Intel Xeon X5650 @ 2.67GHz, 12MB cache, 24GB RAM in 4947.5 sec.\xa0Solved using Gurobi 4.6.1 (12 threads) in 1867.9 sec.	1.184787	320
	blp-ic98 [MIPLIB]	M. Lübbecke	Railway line planning instance	1.407539	472
	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.	2.871143	927
	gmut-76-50 [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.	3.146931	954
	gmut-75-50 [MIPLIB]	Nora Konnyu	Timber harvest scheduling model. Solved by ParaXpress in a 12288 core supercomputer run on HLRN III. 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.	3.364911	965

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	neos-pseudoapplication-2	sp_product	ab	seqsolve	n37
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 / 0.399	2 / 0.600	3 / 0.767	4 / 0.826	5 / 0.830

Model Group Summary

The table below contains summary information for the five most similar model groups to supportcase31 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	neos-pseudoapplication-2	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	0.399267	1
	sp_product	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.599690	2
	ab	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	0.767033	3
	seqsolve	Irv Lustig	The 3 problems in this group (seqsolve1-seqsolve3) represent a hierarchical optimization process, which is derived from a customer problem for assigning people to sites into blocks of time on days of the week. The specialty of this submission is that the best known solution for seqsolveX can be used as a MIP start for seqsolveX+1. For a description of the connections between the problems, please refer to the README.txt contained in the model data for this submission, which also includes MIP start files and a Gurobi log file.	0.826483	4
	n37	J. Aronson	Fixed charge transportation problem	0.830239	5

Type:	Instance
Submitter:	Domenico Salvagnin
Description:	Instance coming from IBM developerWorks forum with unknown application.
	MIPLIB Entry

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

supportcase31: Instance-to-Instance Comparison Results

Parent Instance (supportcase31)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

supportcase31: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary