Instance - s100


	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-2991472-kalu [MIPLIB]	acc-tight2 [MIPLIB]	control20-5-10-5 [MIPLIB]	neos-5125849-lopori [MIPLIB]	d20200 [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 / 1.417	2 / 1.433	3 / 1.434	4 / 1.436	5 / 1.444
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	tbfp-network [MIPLIB]	neos-4531126-vouga [MIPLIB]	s55 [MIPLIB]	datt256 [MIPLIB]	supportcase6 [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.	35 / 1.557	58 / 1.586	338 / 1.789	963 / 2.990	966 / 3.021
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 s100, the five most similar instances to s100 according to the MIC, and the five most similar instances to s100 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	s100 [MIPLIB]	Daniel Espinoza	Wine Scheduling problem with 100 jobs and four processing machines	0.000000	-
MIC Top 5	neos-2991472-kalu [MIPLIB]	Jeff Linderoth	(None provided)	1.417445	1
	acc-tight2 [MIPLIB]	J. Walser	ACC basketball scheduling instance	1.433475	2
	control20-5-10-5 [MIPLIB]	Qie He	Optimal control of a discrete-time switched system model Numerically challenging. Different solvers report this instance as solved to optimality, infeasible, or unbounded.	1.433673	3
	neos-5125849-lopori [MIPLIB]	Jeff Linderoth	(None provided)	1.436136	4
	d20200 [MIPLIB]	COR@L test set	Instance coming from the COR@L test set with unknown origin	1.444066	5
MIPLIB Top 5	tbfp-network [MIPLIB]	Rob Pratt	Two formulations (big-M and network-based) for traveling baseball fan problem. Uses data from 2014 Major League Baseball regular season. Paper uses 2014 data: http://support.sas.com/resources/papers/proceedings14/SAS101-2014.pdf Blog post uses 2015 data: http://blogs.sas.com/content/operations/2015/04/03/the-traveling-baseball-fan-problem/	1.557418	35
	neos-4531126-vouga [MIPLIB]	Jeff Linderoth	(None provided)	1.585568	58
	s55 [MIPLIB]	Daniel Espinoza	Wine Scheduling problem with 55 jobs and four processing machines	1.789382	338
	datt256 [MIPLIB]	Jon Dattorro	Model to find solution to the ``Eternity II'' puzzle	2.989864	963
	supportcase6 [MIPLIB]	Michael Winkler	MIP instances collected from Gurobi forum with unknown application	3.020925	966

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	dws	neos-pseudoapplication-42	pizza	neos-pseudoapplication-73	neos-pseudoapplication-50
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 / 1.653	2 / 1.863	3 / 1.895	4 / 1.899	5 / 1.991

Model Group Summary

The table below contains summary information for the five most similar model groups to s100 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	dws	Philipp Leise	MILP for designing a decentralized water supply system for drinking water in skyscrapers. The nonlinear characteristics of pumps are integrated with the help of an aggregated convex combination. The models vary in the total number of floors and load scenarios for water demand. First stage variables represent the layout decisions, second stage variables represent the operational parameters, such as the continuous rotating speed of pumps or binary switching decisions.	1.652552	1
	neos-pseudoapplication-42	Jeff Linderoth	(None provided)	1.863294	2
	pizza	Gleb Belov	These are the models from MiniZinc Challenges 2012-2016 (see www.minizinc.org), compiled for MIP WITH INDICATOR CONSTRAINTS using the develop branch of MiniZinc and CPLEX 12.7.1 on 30 April 2017. Thus, these models can only be handled by solvers accepting indicator constraints. For models compiled with big-M/domain decomposition only, see my previous submission to MIPLIB.To recompile, create a directory MODELS, a list lst12_16.txt of the models with full paths to mzn/dzn files of each model per line, and say$> ~/install/libmzn/tests/benchmarking/mzn-test.py -l ../lst12_16.txt -slvPrf MZN-CPLEX -debug 1 -addOption "-timeout 3 -D fIndConstr=true -D fMIPdomains=false" -useJoinedName "-writeModel MODELS_IND/%s.mps" Alternatively, you can compile individual model as follows: $> mzn-cplex -v -s -G linear -output-time ../challenge_2012_2016/mznc2016_probs/zephyrus/zephyrus.mzn ../challenge_2012_2016/mznc2016_p/zephyrus/14__8__6__3.dzn -a -timeout 3 -D fIndConstr=true -D fMIPdomains=false -writeModel MODELS_IND/challenge_2012_2016mznc2016_probszephyruszephyrusmzn-challenge_2012_2016mznc2016_probszephyrus14__8__6__3dzn.mps	1.894959	3
	neos-pseudoapplication-73	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	1.899329	4
	neos-pseudoapplication-50	NEOS Server Submission	Model coming from the NEOS Server with unknown application	1.990585	5

Type:	Instance
Submitter:	Daniel Espinoza
Description:	Wine Scheduling problem with 100 jobs and four processing machines
	MIPLIB Entry

Model Group Assignment from MIPLIB:	Spinoza
Assigned Model Group Rank/ISS in the MIC:	71 / 2.401

s100: Instance-to-Instance Comparison Results

Parent Instance (s100)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

s100: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary