joshua-cogliati-inl / logos

Discrete optimization models (i.e., stochastic optimization, distributionally robust optimization and conditional value-at-risk optimization) that can be employed for capital budgeting optimization problems

Logos: Operation Optimization Toolkit

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).

Optimization for Capital Investments

Optimization Approaches

• Deterministic Optimization
• Stochastic Optimization
• Distributionally Robust Optimization
• Conditional Value-at-Risk Optimization

Problems that can be solved by this package:

• 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

Installation:

path/to/LOGOS/build.sh --install

How to run:

• source activate LOGOS_libraries
• path/to/LOGOS/.logos -i inputfile.xml -o outputfile.csv

Tests

python run_tests.py

Docs

path/to/LOGOS/doc

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Licensing

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This software is licensed under the terms you may find in the file named "LICENSE" in this directory.

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