Instance - neos-885086


	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	gmut-76-40 [MIPLIB]	gmut-75-50 [MIPLIB]	gmut-76-50 [MIPLIB]	tpl-tub-ss16 [MIPLIB]	tpl-tub-ws1617 [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 / 1.669	2 / 1.702	3 / 1.837	4 / 2.104	5 / 2.135
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	neos-1171737 [MIPLIB]	neos-1430701 [MIPLIB]	neos-1442119 [MIPLIB]	neos-1171448 [MIPLIB]	neos-1171737** [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.	345 / 3.045	656 / 3.281	733 / 3.320	904 / 3.398	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-885086, the five most similar instances to neos-885086 according to the MIC, and the five most similar instances to neos-885086 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	neos-885086 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application	0.000000	-
MIC Top 5	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.	1.668878	1
	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.	1.702300	2
	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.	1.836999	3
	tpl-tub-ss16 [MIPLIB]	János Höner	Model for the Post-Enrollment Course Timetabling Problem at TU Berlin from the summer term 2016 and the winter term 2016/2017	2.103563	4
	tpl-tub-ws1617 [MIPLIB]	János Höner	Model for the Post-Enrollment Course Timetabling Problem at TU Berlin from the summer term 2016 and the winter term 2016/2017	2.134660	5
MIPLIB Top 5	neos-1171737 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application	3.044592	345
	neos-1430701 [MIPLIB]	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	3.281148	656
	neos-1442119 [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application	3.319741	733
	neos-1171448 [MIPLIB]	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	3.397867	904
	neos-1171737** [MIPLIB]	NEOS Server Submission	Instance coming from the NEOS Server with unknown application.	N.A.**	N.A.**

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	gmu	maxfeassub	neos-pseudoapplication-49	2hopcds	assign1
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 / 2.343	2 / 2.618	3 / 2.651	4 / 2.663	5 / 2.678

Model Group Summary

The table below contains summary information for the five most similar model groups to neos-885086 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	gmu	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.	2.342575	1
	maxfeassub	Marc Pfetsch	Set covering problems arising from a Benders algorithm for finding maximum feasible subsystems. More details on the generation is given in the README file in the tarball.	2.617705	2
	neos-pseudoapplication-49	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	2.651431	3
	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.	2.663032	4
	assign1	Robert Fourer	Imported from the MIPLIB2010 submissions.	2.677873	5

Type:	Instance
Submitter:	NEOS Server Submission
Description:	Instance coming from the NEOS Server with unknown application
	MIPLIB Entry

Model Group Assignment from MIPLIB:	neos-pseudoapplication-8
Assigned Model Group Rank/ISS in the MIC:	26 / 3.054

neos-885086: Instance-to-Instance Comparison Results

Parent Instance (neos-885086)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

neos-885086: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary