comp12-2idx

decomposition precedence variable_bound set_partitioning cardinality invariant_knapsack mixed_binary general_linear

Submitter Variables Constraints Density Status Group Objective MPS File
Matias Sørensen 11863 16803 3.69616e-04 open coursetimetabling 291* comp12-2idx.mps.gz

Instances comp01-21 of curriculum based course timetabling from the International Timetabling Competition 2007. These are time-assignment models (Stage I of the decomposed model), which are smaller than the full model, but still hard to solve.

Instance Statistics

Detailed explanation of the following tables can be found here.

Size Related Properties
Original Presolved
Variables 11863 10796
Constraints 16803 16677
Binaries 11820 10753
Integers 43 43
Continuous 0 0
Implicit Integers 0 0
Fixed Variables 0 0
Nonzero Density 0.000369616 0.000407859
Nonzeroes 73677 73433
Constraint Classification Properties
Original Presolved
Total 16803 16677
Empty 4 0
Free 0 0
Singleton 122 0
Aggregations 0 0
Precedence 4065 4065
Variable Bound 7337 7337
Set Partitioning 1 1
Set Packing 0 0
Set Covering 830 0
Cardinality 87 87
Invariant Knapsack 650 5100
Equation Knapsack 0 0
Bin Packing 0 0
Knapsack 0 0
Integer Knapsack 0 0
Mixed Binary 3664 44
General Linear 43 43
Indicator 0 0

Structure

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

value min median mean max
Components 0.845098
Constraint % 4.52719 16.4918 18.3576 20.4293
Variable % 5.27047 16.5324 18.4652 19.8222
Score 0.809132

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
3 291 0 0 0 Edward Rothberg 2019-12-13 Obtained with Gurobi 9.0
2 299 0 0 0 Ed Klotz 2019-11-15 Found by the Optimization Direct Heuristic and CPLEX 12.9
1 391 391 0 0 0 - 2018-10-10 Solution found during MIPLIB2017 problem selection.

Similar instances in collection

The following instances are most similar to comp12-2idx 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
comp21-2idx hard 10863 10792 71 0 14038 57301 Matias Sørensen coursetimetabling 74 benchmark decomposition benchmark_suitable precedence variable_bound set_partitioning cardinality invariant_knapsack mixed_binary general_linear
comp07-2idx easy 17264 17155 109 0 21235 86577 Matias Sørensen coursetimetabling 6 benchmark decomposition benchmark_suitable precedence variable_bound set_packing cardinality invariant_knapsack mixed_binary general_linear
comp08-2idx easy 11554 11487 67 0 12536 51608 Matias Sørensen coursetimetabling 37 decomposition benchmark_suitable precedence variable_bound set_packing cardinality invariant_knapsack mixed_binary general_linear
neos-983171 easy 8965 6557 0 2408 6711 36691 NEOS Server Submission neos-pseudoapplication-34 2360 benchmark_suitable precedence variable_bound set_packing invariant_knapsack integer_knapsack mixed_binary general_linear
neos18 easy 3312 3312 0 0 11402 24614 NEOS Server Submission neos-pseudoapplication-49 16 binary decomposition benchmark_suitable precedence variable_bound set_covering invariant_knapsack

Reference

ITC2007 webpage: www.cs.qub.ac.uk/itc2007/

Model reference: @Article{Lach2012,
author="Lach, Gerald
and L{\"u}bbecke, Marco E.",
title="Curriculum based course timetabling: new solutions to Udine benchmark instances",
journal="Annals of Operations Research",
year="2012",
volume="194",
number="1",
pages="255--272",
abstract="We present an integer programming approach to the university course timetabling problem, in which weekly lectures have to be scheduled and assigned to rooms. Students' curricula impose restrictions as to which courses may not be scheduled in parallel. Besides some hard constraints (no two courses in the same room at the same time, etc.), there are several soft constraints in practice which give a convenient structure to timetables; these should be met as well as possible.",
issn="1572-9338",
doi="10.1007/s10479-010-0700-7",
url="http://dx.doi.org/10.1007/s10479-010-0700-7"
}

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