hgms30

aggregations variable_bound set_partitioning set_packing set_covering invariant_knapsack equation_knapsack mixed_binary

Submitter	Variables	Constraints	Density	Status	Group	Objective	MPS File
Jesus Rodriguez	23797	281917	1.26772e-04	open	hgms	-44338.74480308597*	hgms30.mps.gz

Maintenance scheduling of generators in hydropower systems

Location of instance in collection

Instance Statistics

Detailed explanation of the following tables can be found here.

Size Related Properties
	Original	Presolved
Variables	23797	21914
Constraints	281917	279397
Binaries	547	524
Integers	0	0
Continuous	23250	21390
Implicit Integers	0	0
Fixed Variables	1860	0
Nonzero Density	0.000126772	0.000112151
Nonzeroes	850486	686670

Constraint Classification Properties
	Original	Presolved
Total	281917	279397
Empty	0	0
Free	0	0
Singleton	49	0
Aggregations	742	742
Precedence	0	0
Variable Bound	8854	161607
Set Partitioning	65	65
Set Packing	36	36
Set Covering	8	8
Cardinality	0	0
Invariant Knapsack	54	52
Equation Knapsack	97	97
Bin Packing	0	0
Knapsack	0	0
Integer Knapsack	0	0
Mixed Binary	272012	116790
General Linear	0	0
Indicator	0	0

Structure

Available nonzero structure and decomposition information. Further information can be found here.

Nonzero entries of original problem

Nonzero entries after trivial presolving

Decomposed structure of original problem (dec-file)

Decomposed structure after trivial presolving (dec-file)

	value	min	median	mean	max
Components	2.898725
Constraint %		0.000358	0.0735319	0.0093100	1.664720
Variable %		0.008400	0.0633466	0.0126066	0.777409
Score	0.578388

Best Known Solution(s)

Find solutions below. Download the archive containing all solutions from the Download page.

## Warning in lapply(df["exactobjval"], as.numeric): NAs introduced by coercion

ID	Objective	Exact	Submitter	Date	Description
3	-44338.74		Mars Davletshin	2023-12-28	The solution was obtained with machine learning-based Large Neighborhood Search with Huawei OptVerse solver (0.7.0).
2	-44338.32		Robert Ashford and Alkis Vazacopoulus	2019-12-18	Found using ODH\|CPlex
1	-44335.22	-44335.22	-	2018-10-12	Solution found during MIPLIB2017 problem selection.

Similar instances in collection

The following instances are most similar to hgms30 in the collection. This similarity analysis is based on 100 scaled instance features describing properties of the variables, objective function, bounds, constraints, and right hand sides.

Instance	Status	Variables	Binaries	Continuous	Constraints	Nonz.	Submitter	Group	Objective	Tags
hgms62	open	48597	547	48050	582237	1753238	Jesus Rodriguez	hgms	-44837.24534313961*	aggregations variable_bound set_partitioning set_packing set_covering invariant_knapsack equation_knapsack mixed_binary
hgms-det	hard	1322	547	775	9752	32367	Jesus Rodriguez	hgms	-47314.08587415493	aggregations variable_bound set_partitioning set_packing set_covering invariant_knapsack equation_knapsack mixed_binary
neos-1122047	easy	5100	100	5000	57791	163640	NEOS Server Submission	neos-pseudoapplication-46	161	benchmark benchmark_suitable precedence variable_bound mixed_binary
neos-5251015-ogosta	hard	136971	232	136739	486531	1955388	Hans Mittelmann	neos-pseudoapplication-19	0.1058	feasibility aggregations variable_bound set_partitioning set_packing cardinality mixed_binary
irish-electricity	easy	61728	9888	51840	104259	523257	Paula Carroll	–	3723497.591396	benchmark benchmark_suitable precedence variable_bound invariant_knapsack binpacking knapsack mixed_binary

Reference

@unpublished{key ,
author = {Jesus Rodriguez, Miguel Anjos, Pascal Côté, Guy Desaulniers},
title = {Generator maintenance scheduling in hydro-power systems: MIP formulations and model selection},
note = {Manuscript in preparation},
year = {2017}
}