ORMM

Operations Research Models & Methods (ORMM) is inspired by Paul A. Jensen's Excel Add-ins. His Excel packages were last updated in 2011, and while I believe they do still work (for the most part), his work may become outdated in a couple of ways:

• Excel is not as commonly used for OR, except in settings where security is of the utmost concern and/or modern languages like Python, R, Julia, C, C++, MATLAB, AMPL, or other modeling software are not available.
• From what I understand, Microsoft has been trying to phase out VBA and move to Javascript. If this happens, this could significantly impact whether or not his packages will work.
• While his website and packages are still available here, some sections are/may become unusable. The animations rely on Flash, which is being phased out in google chrome and other web browsers.

This python package aims to accomplish some of the same goals as Paul Jensen's website and add-ins did, mainly to

1. Be an educational tool that shows how abstract models (linear programs, integer programs, nonlinear programs, etc.) can be applied to real-life scenarios to solve complex problems.
2. Help the practitioner by providing modeling frameworks, methods for solving these models, and problem classes so a user can more easily see how they may be able to frame their business problem/objective through the lens of Operations Research.

This repository contains subpackages for grouping the different types of OR Models & Methods. Currently this subpackage list includes

1. mathprog: A subpackage for mathematical programs, including linear programs, mixed integer linear programs, nonlinear programs, and stochastic programs. Note for this subpackage that models and methods are not necessarily implemented in their abstract form, like Paul Jensen did - there are many python libraries that accomplish this task far better than I could (Pyomo, PuLP, GLPK to name a few). Thus, this subpackage here is dedicated to providing many problem classes, which show how these can be applied to real-life problems and provide an abstract/concrete model for that particular class of problems. Note that the abstract models can be built upon based on a unique business problem that may have more or fewer constraints, or a more complex objective to maximize/minimize.
2. markov: A subpackage for discrete state markov analysis. Currently this only has implementations for discrete time markov processes, but continous time will be added in the near future. This includes the main function markov_analysis, which returns a dictionary of the results, as well as a print_markov function. The main method requires a transition matrix, but can then run simulations, analyze steady state and transient probabilities, and run cost analyses if additional arguments are passed.
3. network: A subpackage for network models and methods. Currently this has implementations for creating a Graph object with arcs, nodes, and arc costs. This graph object has methods for solving the transportation model and the shortest path tree problem.

Installation

$ pip install ormm

Examples

The mathprog subpackage has multiple problem classes, as well as functions for printing the solution of a solved concrete model and for returning a pandas dataframe containing information for sensitivity analysis. Some of the problem classes are

• Resource Allocation: Optimize using scarce resources for valued activities.
• Blending Problem: Optimize the mixing of ingredients to satisfy requirements while minimizing cost.
• Employee Scheduling: Minimize the number of workers hired while meeting the minimum number of workers required for each period.
• Rental Scheduling: Minimize the cost of the plans purchased (which rent units for different amounts of time) while satisfying the number of units needed for each period.
• Aggregate Planning: Minimize the production and holding costs while meeting the demand for each period.

For more details on optional parameters, usage, and the problem descriptions, see the documentation.

Developer Environment

To use the same packages used in development (for creating additions / modifications), you may use the bash command below to install the dev requirements (recommended to do this in your virtualenv). This includes being able to run tests and add to the documentation.

$ pip install -e .[dev]