supplynetworkplanning

Type: Model Group
Submitter: Gerald Gamrath
Description: Supply network planning problems.

Parent Model Group (supplynetworkplanning)

All other model groups below were be compared against this "query" model group.

Model group: supplynetworkplanning
Model Group Composite (MGC) image Composite of the decomposed CCM images for every instance in the query model group.

Component Instances (Decomposed)

These are the decomposed CCM images for each instance in the query model group.

These are component instance images.
Component instance: snp-02-004-104 Component instance: snp-06-004-052 Component instance: snp-10-052-052 Component instance: snp-10-004-052 Component instance: snp-04-052-052
Name snp-02-004-104 snp-06-004-052 snp-10-052-052 snp-10-004-052 snp-04-052-052

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.
Model group: neos-pseudoapplication-93 Model group: milo Model group: ran Model group: nj Model group: neos-pseudoapplication-40
Name neos-pseudoapplication-93 milo ran nj neos-pseudoapplication-40
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.
1 / 2.095 2 / 2.118 3 / 2.189 4 / 2.206 5 / 2.219

Model Group Summary

The table below contains summary information for supplynetworkplanning, and for the five most similar model groups to supplynetworkplanning according to the MIC.

MODEL GROUP SUBMITTER DESCRIPTION ISS RANK
Parent Model Group supplynetworkplanning Gerald Gamrath Supply network planning problems. 0.000000 -
MIC Top 5 neos-pseudoapplication-93 NEOS Server Submission Model coming from the NEOS Server with unknown application 2.094746 1
milo Tamas Terlaky The models come from structural design optimization where the objective is to minimize the total weight of 2 and 3 dimensional cantilevers. The 2D examples are simpler, and GuRobi can solve the 40_1 and 58_1 models, while struggles with 75_1. The 3D examples are more challenging. The x_0 and x_1 models are two different modeling of the same identical problems, so their optimal value is the same. The 1_x and 2_x problems are solved by GuRoBi, the 3_x and 4_x are not solved in reasonable time. 2.117754 2
ran J. Aronson Fixed charge transportation problem 2.189095 3
nj Jonathan Eckstein Electoral district design with various levels of symmetry breaking constraints. 2.205751 4
neos-pseudoapplication-40 Jeff Linderoth (None provided) 2.219300 5


Updated August 19, 2019