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Welcome to the LOGOS wiki!
LOGOS is a software package which contains a set of discrete optimization models that can be employed for capital budgeting optimization problems. More specifically, provided a set of items (characterized by cost and reward values) and constraints, these models select the best combination of items which maximizes overall reward and satisfies the provided constraints. The developed models are based on different versions of the knapsack optimization algorithms. Two main classes of optimization models have been initially developed: deterministic and stochastic. Stochastic optimization models evolve deterministic models by explicitly considering data uncertainties (associated to constraints or item cost and reward). These models can be employed as stand-alone models or interfaced with the INL developed RAVEN code to propagate data uncertainties and analyze the generated data (i.e., sensitivity analysis).
- Deterministic Optimization
- Stochastic Optimization
- Distributionally Robust Optimization
- Conditional Value-at-Risk Optimization
- Deterministic Capital Budgeting
- Risk-informed stochastic Capital Budgeting
- Multiple Knapsack problem optimization
- Multi-dimensional Knapsack problem optimization
- Multi-choice Knapsack problem optimization
- Multi-choice multi-dimensional Knapsack problem optimization
- SSC cashflow and NPV models
path/to/LOGOS/build.sh --install
- source activate LOGOS_libraries
- path/to/LOGOS/.logos -i inputfile.xml -o outputfile.csv
python run_tests.py
path/to/LOGOS/doc
To help develop LOGOS as plugin for RAVEN, one first needs to download and install the RAVEN code. RAVEN can be downloaded from Github. Installation instruction instructions are also available.
RAVEN installation instructions
RAVEN plugin installation for development
The development of SR2ML follows the same quality standards as RAVEN. Detailed information on the development workflow can be found here.