Optimization
This portfolio captures the work I completed for a course, Decision Analytics for Business & Policy, at Carnegie Mellon University in Fall 2021. It demonstrates the use of modeling frameworks and computational tools to address complex, ill-defined and large-scale decision-making problems that arise in policy and business. The work done here involves advanced methods of decision-making: deterministic optimization (large-scale), stochastic/robust optimization, and sequential decision-making. Different case problems from a variety of real-world settings have been solved here, along with a final project. Links for each of these components have been provided below. To view my course repository on GitHub, please click here.
Key Learnings
From the course, Decision Analytics for Business & Policy, I learnt how to design end-to-end decision analytics pipelines by: identifying components of decision problems that are (not) susceptible to analytical modeling, and selecting the appropriate tools; translating decision problems to logical, mathematical formulations, and clearly identifying the objectives, decision variables, constraints, input/output; translating these mathematical formulations into code using Gurobi in Python; applying findings back to the decision problem context in business and policy through written and verbal communication.
Portfolio
Here are the case studies and final project that I completed during the course of this class.
Case Studies
To view the case prompt, associated data files, Python code and reported solutions, please click on the folder hyperlinks below.
I. Integrated Manufacturing & Inventory Planning using Linear Optimization
II. Emergency Response using Integer Optimization
III. Traveling Salesman Problem using Integer Optimization
IV. World Health Organization’s Nutrition Policy using Multi-Objective Optimization
V. Aircraft Configuration using Stochastic Optimization
Final Project
Brief Description: “To prepare for large public health emergencies, the Allegheny County is prioritizing the list of Points of Dispense (PODs) to open, which are used to distribute essential supplies and medicines to the public during such emergencies. Your goal for this project is to formulate a facility location model, and optimally select a list of PODs from the candidate sites. You need to compare two different formulations: minimizing the total/weighted travel distance of the population, and minimizing the maximum travel distance for anyone.”
Please click on the hyperlinks below to view the project details: