tokyometro

aggregations precedence variable_bound set_partitioning equation_knapsack mixed_binary general_linear

Submitter Variables Constraints Density Status Group Objective MPS File
Hsiang-Yun WU 4537 7719 7.69764e-04 open 8471.9* tokyometro.mps.gz

The layout model for Tokyo Metro Map

Instance Statistics

Detailed explanation of the following tables can be found here.

Size Related Properties
Original Presolved
Variables 4537 4157
Constraints 7719 7367
Binaries 2496 2148
Integers 2041 2009
Continuous 0 0
Implicit Integers 0 593
Fixed Variables 0 0
Nonzero Density 0.000769764 0.000822051
Nonzeroes 26958 25175
Constraint Classification Properties
Original Presolved
Total 7719 7367
Empty 0 0
Free 0 0
Singleton 28 0
Aggregations 150 289
Precedence 0 171
Variable Bound 622 488
Set Partitioning 386 265
Set Packing 0 0
Set Covering 0 0
Cardinality 0 0
Invariant Knapsack 0 0
Equation Knapsack 0 3
Bin Packing 0 0
Knapsack 0 0
Integer Knapsack 0 0
Mixed Binary 0 2
General Linear 6533 6149
Indicator 0 0

Structure

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

value min median mean max
Components 2.733197
Constraint % 0.0135740 0.0393647 0.0135740 0.814443
Variable % 0.0467181 0.0997950 0.0934361 0.607335
Score 0.212130

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 8471.9 8471.9 0 0 0 - 2018-10-10 Solution found during MIPLIB2017 problem selection.

Similar instances in collection

The following instances are most similar to tokyometro 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
lectsched-5-obj easy 21805 21389 416 0 38884 239608 Harald Schilly lectsched 24 benchmark benchmark_suitable aggregations precedence variable_bound set_covering invariant_knapsack integer_knapsack general_linear
allcolor10 open 14872 476 13776 620 34014 89136 Domenico Salvagnin allcolor 159* decomposition aggregations precedence variable_bound set_partitioning integer_knapsack general_linear
lectsched-4-obj easy 7901 7665 236 0 14163 82428 Harald Schilly lectsched 4 benchmark_suitable aggregations precedence variable_bound set_covering integer_knapsack general_linear
allcolor58 open 84376 2492 78288 3596 197154 515184 Domenico Salvagnin allcolor 3378* decomposition aggregations precedence variable_bound set_partitioning integer_knapsack general_linear
neos-4165869-wannon hard 31728 480 31248 0 85865 270474 Hans Mittelmann neos-pseudoapplication-44 293 precedence variable_bound set_partitioning set_packing invariant_knapsack integer_knapsack general_linear

Reference

@Misc{TokyoMetro:17, 
  author = {H.-Y. Wu and S. Takahashi}, 
  title = {A MIP Dataset for the Layout of Tokyo Metro},
  Year = {2017}
}

@phdthesis{phdwu:13,
        author = {H.-Y. Wu},
        title = {Constrained Optimization Approaches to Customizing Layout and Annotation for Metro Maps},
        school = {The University of Tokyo},
        year = {2013},
        month = {March},
        address = {Chiba, Japan},
}

Last Update Nov 22, 2019 by Gregor Hendel
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