1,721,407 research outputs found
multiple level warehouse layout problem
No full textLarge data set for multiple level warehouse layout proble
Datasets for supply chain security with automated machine learning
No full textThree datasets for evaluating AML models for supply chain securit
Critical Mineral and Risk Factors Data
No full textDaily adjusted closing price for critical minerals and other risk factor
r-flip and MST2 algorithms: data files and results
No full textThe results of r-flip (r=1 and r=2) and MST2 algorithms for quadratic unconstrained binary optimization instances with size of 3000-8000 and 30,00
The study of Stock options, innovation, retention and performance
No full textThis is a study to analyze the relationship between stock options, innovation, employee retention, company financial performance
Bank Data
No full textData used for study: Internal and external analysis of community banks’ performanc
Large-Scale Planar Graph Instances for Max-Cut Problem
No full textThere are 225 large-scale planar graph instances for Max-Cut problems with Q matrix from 1000 by 1000 to 90000 by 90000. The instances are similar to G-set benchmark in the literature.
There are three different types of weights on the instances. The MCxx_yy_D99_a.txt instance has 1 and -1 weight. The MCxx_yy_D99_b.txt instance has random value between -10 and 10. The MCxx_yy_D99_c.txt instance has random value between -1000 and 1000.
For each instance, there is a text-file in the following format (rudy-output format):
n m
h_1 t_1 c_{h_1,t_1}
h_2 t_2 c_{h_2,t_2}
...
h_n t_n c_{h_n,t_n}
where n is the number of nodes, m the number of edges and for each edge, h_i and t_i are the end-nodes and c_{h_i,t_i} the weight. Nodes are numbered from 1 up to n. All instances are generated as complete grap
very large-scale networks
No full textnetwork nodes from 7500 to15000, density from 1% to 50%, weighted and unweighte
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