Overview_02.ipynb

Kernel: Python 3 (system-wide)

# execute to import notebook styling for tables and width etc.
from IPython.core.display import HTML
import urllib.request
response = urllib.request.urlopen('https://raw.githubusercontent.com/DataScienceUWL/DS775v2/master/ds755.css')
HTML(response.read().decode("utf-8"));

Overview 02: Sensitivity Analysis and Abstract Modeling

Topics

• Sensitivity analysis

• Abstract modeling for setting up larger LP problems

Learning Outcomes

The student will be able to:

• code a linear programming problem with the model and data separated using dense arrays

• write reusable models that can be repurposed by simply changing the data

• identify sensitive parameters in a linear programming problem.

• compute and interpret the shadow price for a resource.

• compute and interpret the allowable range for and objective function coefficient in a linear programming problem.

• compute and interpret the allowable range for a constraint boundary in a linear programming problem.

• produce and interpret a sensitivity report for a linear programming problem.

Student "To Do" Checklist

• Reading

  • read Hillier Section 4.7

  • read the first 7 paragraphs in Hillier Section 7.1

  • read Hillier Section 7.3 (but keep in mind that we will use Python and not Excel)

• Work your way through the Jupyter notebook called Lesson_02.ipynb. This is the main presentation and replaces the Storybook presentations used in our other courses. Use the self-assessments to measure your understanding. There is a separate Jupyter notebook file with self-assessment solutions in the lesson folder. Self-assessments are not collected and do not count toward your grade.

• Complete the homework notebook in CoCalc and transfer your answers to the Canvas Quiz by the due date which is shown both in Canvas and CoCalc.