Instance - gmu-35-50

gmu-35-50: Instance-to-Instance Comparison Results

Type:	Instance
Submitter:	Nora Konnyu
Description:	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.
	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	gmu-35-40 [MIPLIB]	neos-3615091-sutlej [MIPLIB]	30n20b8 [MIPLIB]	gsvm2rl3 [MIPLIB]	square47 [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 / 0.913	2 / 1.210	3 / 1.244	4 / 1.273	5 / 1.277
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	gmu-35-40 [MIPLIB]	cap6000 [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.	1 / 0.913	109 / 1.536	847 / 2.305	917 / 2.640	925 / 2.735
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 gmu-35-50, the five most similar instances to gmu-35-50 according to the MIC, and the five most similar instances to gmu-35-50 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	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.	0.000000	-
MIC Top 5	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.	0.912558	1
	neos-3615091-sutlej [MIPLIB]	Hans Mittelmann	Collection of anonymous submissions to the NEOS Server for Optimization	1.210098	2
	30n20b8 [MIPLIB]	E. Coughlan, M. Lübbecke, J. Schulz	Multi-mode resource leveling with availability constraint	1.243966	3
	gsvm2rl3 [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	1.272989	4
	square47 [MIPLIB]	Sascha Kurz	Squaring the square For a given integer n, determine the minimum number of squares in a tiling of an \\(n\\times n\\) square using using only integer sided squares of smaller size. (Although the models get quite large even for moderate n, they can be solved to optimality for all \\(n \\le 61\\), while challenging the MIP solver, especially the presolver.)	1.276892	5
MIPLIB Top 5	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.	0.912558	1
	cap6000 [MIPLIB]	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.535965	109
	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.	2.304712	847
	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.	2.639525	917
	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.	2.735073	925

gmu-35-50: Instance-to-Model Comparison Results

Model Group Assignment from MIPLIB:	gmu
Assigned Model Group Rank/ISS in the MIC:	113 / 2.748

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	square	supportvectormachine	neos-pseudoapplication-89	scp	stein
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 / 1.691	2 / 1.775	3 / 1.901	4 / 1.921	5 / 1.999

Model Group Summary

The table below contains summary information for the five most similar model groups to gmu-35-50 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	square	Sascha Kurz	Squaring the square For a given integer n, determine the minimum number of squares in a tiling of an \\(n\\times n\\) square using using only integer sided squares of smaller size. (Although the models get quite large even for moderate n, they can be solved to optimality for all \\(n \\le 61\\), while challenging the MIP solver, especially the presolver.)	1.690660	1
	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	1.774730	2
	neos-pseudoapplication-89	NEOS Server Submission	Model coming from the NEOS Server with unknown application	1.901400	3
	scp	Shunji Umetani	This is a random test model generator for SCP using the scheme of the following paper, namely the column cost c[j] are integer randomly generated from [1,100]; every column covers at least one row; and every row is covered by at least two columns. see reference: E. Balas and A. Ho, Set covering algorithms using cutting planes, heuristics, and subgradient optimization: A computational study, Mathematical Programming, 12 (1980), 37-60. We have newly generated Classes I-N with the following parameter values, where each class has five models. We have also generated reduced models by a standard pricing method in the following paper: S. Umetani and M. Yagiura, Relaxation heuristics for the set covering problem, Journal of the Operations Research Society of Japan, 50 (2007), 350-375. You can obtain the model generator program from the following web site. https://sites.google.com/site/shunjiumetani/benchmark	1.921094	4
	stein	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.999455	5

gmu-35-50: Instance-to-Instance Comparison Results

Parent Instance (gmu-35-50)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

gmu-35-50: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary