tbfp-network

benchmark binary benchmark_suitable set_partitioning cardinality

Submitter Variables Constraints Density Status Group Objective MPS File
Rob Pratt 72747 2436 1.21796e-03 easy 24.16319444 tbfp-network.mps.gz

Two formulations (big-M and network-based) for traveling baseball fan problem. Uses data from 2014 Major League Baseball regular season. Paper uses 2014 data: http://support.sas.com/resources/papers/proceedings14/SAS101-2014.pdf Blog post uses 2015 data: http://blogs.sas.com/content/operations/2015/04/03/the-traveling-baseball-fan-problem/

Instance Statistics

Detailed explanation of the following tables can be found here.

Size Related Properties
Original Presolved
Variables 72747 72747
Constraints 2436 2436
Binaries 72747 72747
Integers 0 0
Continuous 0 0
Implicit Integers 0 0
Fixed Variables 0 0
Nonzero Density 0.00121796 0.00121796
Nonzeroes 215837 215837
Constraint Classification Properties
Original Presolved
Total 2436 2436
Empty 0 0
Free 0 0
Singleton 0 0
Aggregations 0 0
Precedence 0 0
Variable Bound 0 0
Set Partitioning 42 58
Set Packing 0 0
Set Covering 0 0
Cardinality 2394 2378
Invariant Knapsack 0 0
Equation Knapsack 0 0
Bin Packing 0 0
Knapsack 0 0
Integer Knapsack 0 0
Mixed Binary 0 0
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 0.301030
Constraint % 97.6190 97.6190 97.6190 97.6190
Variable % 99.9285 99.9285 99.9285 99.9285
Score 0.000698

Best Known Solution(s)

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

ID Objective Exact Int. Viol Cons. Viol Obj. Viol Submitter Date Description
1 24.16319 24.16319 0 0 0 - 2018-10-11 Solution found during MIPLIB2017 problem selection.

Similar instances in collection

The following instances are most similar to tbfp-network 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
neos-4531126-vouga open 169996 169996 0 0 7694 967980 Jeff Linderoth neos-pseudoapplication-87 544045.0657848* binary decomposition numerics set_partitioning cardinality invariant_knapsack binpacking mixed_binary
datt256 open 262144 262144 0 0 11077 1503730 Jon Dattorro no_solution binary set_partitioning cardinality
s100 hard 364417 364417 0 0 14733 1777920 Daniel Espinoza Spinoza -0.1697235270583 benchmark binary benchmark_suitable aggregations set_partitioning set_packing cardinality invariant_knapsack knapsack
supportcase6 easy 130052 130051 1 0 771 584976 Michael Winkler 51906.47737 benchmark benchmark_suitable set_partitioning cardinality general_linear
nu120-pr9 easy 7350 7308 42 0 2220 22176 MIPLIB submission pool nus-prxy 24945 decomposition numerics aggregations variable_bound cardinality general_linear

Reference

@INPROCEEDINGS{ChapmanGalatiPratt2014,
  author = {Tonya Chapman and Matt Galati and Rob Pratt},
  title = {The Traveling Baseball Fan Problem and the OPTMODEL Procedure},
  booktitle = {Proceedings of the SAS Global Forum 2014 Conference},
  year = {2014},
  address = {Cary, NC},
  publisher = {SAS Institute Inc.},
  note = {\url{http://support.sas.com/resources/papers/proceedings14/SAS101-2014.pdf}}
}

Last Update Okt 02, 2019 by Gregor Hendel
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