opti202 initial push

optimization202/ESUPS_case_study/.gitignore

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+*.bak
+*.idea/
+*__pycache__/
+*output/
+*.lp
+*.mps
+*.log
+*.sol
+*log_files/
+*.coverage
+*htmlcov/
+*.html
+*.zip
+
+# Jupyter Notebook
+.ipynb_checkpoints

optimization202/ESUPS_case_study/README.md

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+# Technician Assignment With Mathematical Optimization
+
+## Problem
+You are consulting for a telecom company that dispatches technicians to perform various tasks for customers, 
+ranging from equipment installations and setups to repairs. Considering various constraints such as:
+- Technician availability
+- Skillset matching
+- Customer time window
+- Service priority
+- Start and return of technicians to the central depot
+- Limited resources/vans
+
+The goal is to decide:
+- Which technicians are assigned to which customers and in what sequence?
+- How to allocate the limited resources, ensuring each technician has access to a van?
+
+We use a two-step solution approach, using Mathematical Optimization (MO) in both, to solve this problem.
+
+## Repo Guide
+- The first model is called _Technician Routing_ where we decide which technicians are assigned to which customers 
+and in what sequence. This model is described and solved in the [technician_routing](technician_routing.ipynb) notebook.
+- The second model is called _Resource Assignment_ where we decide which technicians should be assigned to which vans.
+  This model is described and solved in the `resource_assignment` notebook. 
+  - There are two versions of this notebook: a partial version ([resource_assignment](resource_assignment.ipynb)) 
+    that can be used for teaching, and one with the complete description, formulation, and the code ([resource_assignment_complete](resource_assignment_complete.ipynb)). 
+- The [data-Sce0.xlsx](data-Sce0.xlsx) is the input used for the _Technician Routing_ model.
+- The [routes.csv](routes.csv) and [orders.csv](orders.csv) are the outputs of _Technician Routing_ model 
+  and are used as inputs for the _Resource Assignment_ model. Of course, you can run the `technician_routing` notebook and 
+  or even modify it as you please to get new results. Ensure the format of the output is similar
+  in case you like to use the `resource_assignment` notebook afterward.
+
+The model size is intentionally small to ensure even those without access to a Gurobi license can run the model (check out [License Requirement](#license-requirement) section for more info about the license).
+
+## Google Colab
+If you like to access the notebooks in Google Colab, click: 
+- [technician_routing](https://colab.research.google.com/github/decision-spot/technician_assignment/blob/main/technician_routing.ipynb)
+- [resource_assignment](https://colab.research.google.com/github/decision-spot/technician_assignment/blob/main/resource_assignment_complete.ipynb)
+
+Note that you must sign in with a Google account to be able to run the code in Colab.
+
+## License Requirement
+The problem is modeled using Gurobi Python API. So, a Gurobi license is required to run this model.
+If you don't have a license, you can request a free commercial evaluation license 
+or a free academic license [here](https://www.gurobi.com/downloads/).
+The data used in the models is small enough to run the model with a Gurobi restricted license
+(available via `pip install gurobi` as shown in the notebook).

optimization202/Modeling_Session_1/README.md

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+# Opti 202 Day 1 Exercises
+
+## Problems
+We revisit a problem from modeling sessions in Opti 101 and 201. Given demand data produced by a hypothetical machine learning model, as well as transportation costs and production capacity, determine the optimal production and shipment of widgets from a set of production facilities to distribution locations that minimize cost. 
+
+We also take a deep dive into a nonlinear regression problem modeling **envyme kinetics**. 
+
+
+## Beware Spoilers!
+We also put **completed** versions of the modeling notebook and the exercise in the repo. If you want to work through the notebooks without everything filled in make sure not to open the completed versions.
+
+
+## Exercise Set 1 Notebook Links
+
+[Google Colab Link - Exercise *without* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/exercise_set1.ipynb)
+
+
+[Google Colab Link - Exercise *with* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb)
+
+
+[Google Colab Link - Exercise *without* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb)
+
+
+[Google Colab Link - Exercise *with* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb)
+
+
+----
+For details on licensing or on running the notebooks, see the overview on [Modeling Examples](../../)
+
+© Gurobi Optimization, LLC