hgms-det

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 1322 9752 2.5106e-03 hard hgms -47314.08587415493 hgms-det.mps.gz

Maintenance scheduling of generators in hydropower systems

Instance Statistics

Detailed explanation of the following tables can be found here.

Size Related Properties
Original Presolved
Variables 1322 1237
Constraints 9752 9626
Binaries 547 524
Integers 0 0
Continuous 775 713
Implicit Integers 0 0
Fixed Variables 62 0
Nonzero Density 0.00251060 0.00225709
Nonzeroes 32367 26876
Constraint Classification Properties
Original Presolved
Total 9752 9626
Empty 0 0
Free 0 0
Singleton 49 0
Aggregations 75 75
Precedence 0 0
Variable Bound 299 5389
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 9069 3904
General Linear 0 0
Indicator 0 0

Structure

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

value min median mean max
Components 2.096910
Constraint % 0.0103993 0.471073 0.296381 1.62230
Variable % 0.1512860 0.551462 0.378215 7.41301
Score 0.580151

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 Int. Viol Cons. Viol Obj. Viol Submitter Date Description
2 -47314.09 0 0 0 Robert Bixby 2020-02-10 Solved with Gurobi 9.0 within 11 hours
1 -47314.09 -47314.09 0 0 0 - 2018-10-12 Solution found during MIPLIB2017 problem selection.

Similar instances in collection

The following instances are most similar to hgms-det 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 Integers Continuous Constraints Nonz. Submitter Group Objective Tags
hgms30 open 23797 547 0 23250 281917 850486 Jesus Rodriguez hgms -44338.3173275985* aggregations variable_bound set_partitioning set_packing set_covering invariant_knapsack equation_knapsack mixed_binary
hgms62 open 48597 547 0 48050 582237 1753238 Jesus Rodriguez hgms -44837.24534313961* aggregations variable_bound set_partitioning set_packing set_covering invariant_knapsack equation_knapsack mixed_binary
rocI-3-11 easy 5132 3894 762 476 8165 20540 Joerg Rambau rocI -50403 numerics aggregations precedence variable_bound set_partitioning mixed_binary general_linear
rocI-4-11 easy 6839 5192 1016 631 10883 27383 Joerg Rambau rocI -6020203 benchmark benchmark_suitable aggregations precedence variable_bound set_partitioning mixed_binary general_linear
neos-4333596-skien easy 1005 460 0 545 812 5811 Jeff Linderoth neos-pseudoapplication-43 -14610731.01 benchmark_suitable aggregations precedence variable_bound set_partitioning cardinality 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}
}

Last Update Mar 04, 2024 by Julian Manns
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