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
Idaho National Laboratory is a cutting edge research facility which is a constantly producing high quality research and software. Feel free to take a look at our other software and scientific offerings at:
Primary Technology Offerings Page
Supported Open Source Software
Raw Experiment Open Source Software
Unsupported Open Source Software
This software is licensed under the terms you may find in the file named "LICENSE" in this directory.
By contributing to this software project, you are agreeing to the following terms and conditions for your contributions:
You agree your contributions are submitted under the Apache license. You represent you are authorized to make the contributions and grant the license. If your employer has rights to intellectual property that includes your contributions, you represent that you have received permission to make contributions and grant the required license on behalf of that employer.