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dws
| Type: | Model Group |
| Submitter: | Philipp Leise |
| Description: | MILP for designing a decentralized water supply system for drinking water in skyscrapers. The nonlinear characteristics of pumps are integrated with the help of an aggregated convex combination. The models vary in the total number of floors and load scenarios for water demand. First stage variables represent the layout decisions, second stage variables represent the operational parameters, such as the continuous rotating speed of pumps or binary switching decisions. |
Parent Model Group (dws)
All other model groups below were be compared against this "query" model group.  |
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Model Group Composite (MGC) image
Composite of the decomposed CCM images for every instance in the query model group.
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Component Instances (Decomposed)
These are the decomposed CCM images for each instance in the query model group.
MIC Top 5 Model Groups
These are the 5 MGC images that are most similar to the MGC image for the query model group, according to the ISS metric. |
FIXME - These are model group composite images.
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| Name | mc | pizza | neos-pseudoapplication-74 | noip | neos-pseudoapplication-73 | |
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Rank / ISS
The image-based structural similarity (ISS) metric measures the Euclidean distance between the image-based feature vectors for the query model group and all other model groups. A smaller ISS value indicates greater similarity.
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1 / 1.334 | 2 / 1.562 | 3 / 1.697 | 4 / 1.723 | 5 / 1.732 |
Model Group Summary
The table below contains summary information for dws, and for the five most similar model groups to dws according to the MIC.
| MODEL GROUP | SUBMITTER | DESCRIPTION | ISS | RANK | |
|---|---|---|---|---|---|
| Parent Model Group | dws | Philipp Leise | MILP for designing a decentralized water supply system for drinking water in skyscrapers. The nonlinear characteristics of pumps are integrated with the help of an aggregated convex combination. The models vary in the total number of floors and load scenarios for water demand. First stage variables represent the layout decisions, second stage variables represent the operational parameters, such as the continuous rotating speed of pumps or binary switching decisions. | 0.000000 | - |
| MIC Top 5 | mc | F. Ortega, L. Wolsey | Fixed cost network flow problems | 1.333518 | 1 |
| pizza | Gleb Belov | These are the models from MiniZinc Challenges 2012-2016 (see www.minizinc.org), compiled for MIP WITH INDICATOR CONSTRAINTS using the develop branch of MiniZinc and CPLEX 12.7.1 on 30 April 2017. Thus, these models can only be handled by solvers accepting indicator constraints. For models compiled with big-M/domain decomposition only, see my previous submission to MIPLIB.To recompile, create a directory MODELS, a list lst12_16.txt of the models with full paths to mzn/dzn files of each model per line, and say$> ~/install/libmzn/tests/benchmarking/mzn-test.py -l ../lst12_16.txt -slvPrf MZN-CPLEX -debug 1 -addOption "-timeout 3 -D fIndConstr=true -D fMIPdomains=false" -useJoinedName "-writeModel MODELS_IND/%s.mps" Alternatively, you can compile individual model as follows: $> mzn-cplex -v -s -G linear -output-time ../challenge_2012_2016/mznc2016_probs/zephyrus/zephyrus.mzn ../challenge_2012_2016/mznc2016_p/zephyrus/14__8__6__3.dzn -a -timeout 3 -D fIndConstr=true -D fMIPdomains=false -writeModel MODELS_IND/challenge_2012_2016mznc2016_probszephyruszephyrusmzn-challenge_2012_2016mznc2016_probszephyrus14__8__6__3dzn.mps | 1.561922 | 2 | |
| neos-pseudoapplication-74 | Jeff Linderoth | (None provided) | 1.697100 | 3 | |
| noip | Christopher Hojny | integer programming formulation that verifies that no integer programming formulation of a given 0/1-point set exists | 1.722881 | 4 | |
| neos-pseudoapplication-73 | NEOS Server Submission | Imported from the MIPLIB2010 submissions. | 1.732331 | 5 |
