Model Group - gmu

gmu

Type:	Model Group
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.


	Model Group Composite (MGC) image Composite of the decomposed CCM images for every instance in the query model group.

	These are component instance images.
	Name	gmut-75-50	gmut-76-50	gmu-35-40	gmut-76-40	gmu-35-50

	FIXME - These are model group composite images.
	Name	maxfeassub	assign1	neos-pseudoapplication-76	neos-pseudoapplication-101	generated
	Rank / ISS The image-based structural similarity (ISS) metric measures the Euclidean distance between the image-based feature vectors for the query model group and all other model groups. A smaller ISS value indicates greater similarity.	1 / 2.231	2 / 2.380	3 / 2.395	4 / 2.404	5 / 2.414

Model Group Summary

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

	MODEL GROUP	SUBMITTER	DESCRIPTION	ISS	RANK
Parent Model Group	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.	0.000000	-
MIC Top 5	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.	2.230555	1
	assign1	Robert Fourer	Imported from the MIPLIB2010 submissions.	2.380332	2
	neos-pseudoapplication-76	Jeff Linderoth	(None provided)	2.395192	3
	neos-pseudoapplication-101	NEOS Server Submission	Model coming from the NEOS Server with unknown application. Infeasibility claimed by CPLEX 12.6 and CPLEX 12.6.1 with extreme numerical caution emphasi after 4 and 2 hours computation, respectively.	2.404104	4
	generated	Simon Bowly	Randomly generated integer and binary programming models. These results are part of an early phase of work aimed at generating diverse and challenging MIP models for experimental testing. We have aimed to produce small integer and binary programming models which are reasonably difficult to solve and have varied structure, eliciting a range of behaviour in state of the art algorithms.	2.414367	5

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