Instance - physiciansched6-2


	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	physiciansched6-1 [MIPLIB]	neos-3402294-bobin [MIPLIB]	gen-ip016 [MIPLIB]	physiciansched3-4 [MIPLIB]	bnatt500 [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 / 1.268	2 / 1.451	3 / 1.459	4 / 1.463	5 / 1.466
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	physiciansched6-1 [MIPLIB]	neos-1330346 [MIPLIB]	acc-tight5 [MIPLIB]	acc-tight4 [MIPLIB]	neos-3148108-pahi [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 / 1.268	431 / 1.883	454 / 1.891	627 / 1.976	931 / 2.484
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 physiciansched6-2, the five most similar instances to physiciansched6-2 according to the MIC, and the five most similar instances to physiciansched6-2 according to MIPLIB 2017.

	INSTANCE	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Instance	physiciansched6-2 [MIPLIB]	Pelin Damci-Kurt	Physician scheduling problem for hospitalist, radiology and kidney specialist groups.	0.000000	-
MIC Top 5	physiciansched6-1 [MIPLIB]	Pelin Damci-Kurt	Physician scheduling problem for hospitalist, radiology and kidney specialist groups.	1.268196	1
	neos-3402294-bobin [MIPLIB]	Jeff Linderoth	(None provided)	1.450841	2
	gen-ip016 [MIPLIB]	Simon Bowly	Randomly generated integer and binary programming instances. These results are part of an early phase of work aimed at generating diverse and challenging MIP instances for experimental testing. We have aimed to produce small integer and binary programming instances which are reasonably difficult to solve and have varied structure, eliciting a range of behaviour in state of the art algorithms. Solved with XPRESS in a few seconds.	1.459029	3
	physiciansched3-4 [MIPLIB]	Pelin Damci-Kurt	Physician scheduling problem for hospitalist, radiology and kidney specialist groups.	1.463339	4
	bnatt500 [MIPLIB]	Tatsuya Akutsu	We are submitting ILP data for identification of a singletonattractor in a Boolean newtork, which is a well-known problemin computational systems biology.This problem is known to be NP-hard and we developed a methodto transform an instance of the problem to an integer linearprogram (ILP).We used ILPs from artificially generated Boolean networks ofindegree 3.The size of the networks are: 350, 400, 500.Even for the case of 500, we could not find a solution within6 hours using CPLEX 11.2 on a PC with XEON 5470 3.33GHz CPU.(This ILP corresponds to the case of size=350.File format is (zipped) CPLEX LP format.)The details of the method appeared in:T. Akutsu, M. Hayashida and T. Tamura, Integer programming-basedmethods for attractor detection and control of Boolean networks,Proc. The combined 48th IEEE Conference on Decision and Controland 28th Chinese Control Conference (IEEE CDC/CCC 2009), 5610-5617, 2009.	1.466380	5
MIPLIB Top 5	physiciansched6-1 [MIPLIB]	Pelin Damci-Kurt	Physician scheduling problem for hospitalist, radiology and kidney specialist groups.	1.268196	1
	neos-1330346 [MIPLIB]	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	1.883260	431
	acc-tight5 [MIPLIB]	J. Walser	ACC basketball scheduling instance	1.891193	454
	acc-tight4 [MIPLIB]	J. Walser	ACC basketball scheduling instance	1.975732	627
	neos-3148108-pahi [MIPLIB]	Jeff Linderoth	(None provided)	2.484172	931

These are model group composite (MGC) images for the MIC top 5 model groups.
Name	scp	neos-pseudoapplication-21	enlight	stein	bnatt
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 / 1.788	2 / 1.848	3 / 1.864	4 / 1.993	5 / 1.994

Model Group Summary

The table below contains summary information for the five most similar model groups to physiciansched6-2 according to the MIC.

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
MIC Top 5	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	1.787518	1
	neos-pseudoapplication-21	NEOS Server Submission	Imported from the MIPLIB2010 submissions.	1.847904	2
	enlight	A. Zymolka	Model to solve model of a combinatorial game ``EnLight'' Imported from the MIPLIB2010 submissions.	1.864032	3
	stein	MIPLIB submission pool	Imported from the MIPLIB2010 submissions.	1.993343	4
	bnatt	Tatsuya Akutsu	We are submitting ILP data for identification of a singletonattractor in a Boolean newtork, which is a well-known problemin computational systems biology.This problem is known to be NP-hard and we developed a methodto transform an model of the problem to an integer linearprogram (ILP).We used ILPs from artificially generated Boolean networks ofindegree 3.The size of the networks are: 350, 400, 500.Even for the case of 500, we could not find a solution within6 hours using CPLEX 11.2 on a PC with XEON 5470 3.33GHz CPU.(This ILP corresponds to the case of size=350.File format is (zipped) CPLEX LP format.)The details of the method appeared in:T. Akutsu, M. Hayashida and T. Tamura, Integer programming-basedmethods for attractor detection and control of Boolean networks,Proc. The combined 48th IEEE Conference on Decision and Controland 28th Chinese Control Conference (IEEE CDC/CCC 2009), 5610-5617, 2009.	1.993916	5

Type:	Instance
Submitter:	Pelin Damci-Kurt
Description:	Physician scheduling problem for hospitalist, radiology and kidney specialist groups.
	MIPLIB Entry

Model Group Assignment from MIPLIB:	physiciansched
Assigned Model Group Rank/ISS in the MIC:	106 / 2.643

physiciansched6-2: Instance-to-Instance Comparison Results

Parent Instance (physiciansched6-2)

MIC Top 5 Instances

MIPLIB Top 5 Instances

Instance Summary

physiciansched6-2: Instance-to-Model Comparison Results

MIC Top 5 Model Groups

Model Group Summary