OR 6205 · Deterministic Operations Research | Priscila de Azevedo Drummond

Course Overview

Course: OR 6205 — Deterministic Operations Research (Graduate)
Role: Teaching Fellow — Sole Instructor of Record
Institution: Northeastern University, Dept. of Mechanical & Industrial Engineering
Term: Spring 2026 · 5 students
Award: 🏆 2026 COE Outstanding Graduate Teaching Award

This course introduces graduate students to the theory and application of deterministic operations research methods, including linear programming, duality theory, sensitivity analysis, network models, and integer programming. Students use Python (Gurobi) to solve real-world optimization problems and engage with the ethical dimensions of modeling.

Learning Objectives

By the end of this course, students are able to:

Formulate linear and integer programming models from written problem descriptions
Solve LP problems graphically, by hand (simplex), and computationally (Gurobi/Python)
Analyze models using duality theory and sensitivity analysis
Interpret optimization results in context and provide actionable recommendations
Evaluate the ethical implications of modeling decisions — whose interests are represented, what is optimized, and what trade-offs are embedded in the model

Course Design Philosophy

This course was designed using backward design — learning objectives drove every assessment and in-class activity. Key design choices:

Active learning over lecture. Based on student feedback mid-semester, I transitioned from slide-based delivery to collaborative board work, step-by-step examples, and multi-format supplementary materials. Students said: “That’s so cool” — a moment that confirmed the shift was right.

Transparent assessment with rework. All assignments come with detailed rubrics. Students may revise graded work for additional credit, reinforcing that learning happens through iteration, not single attempts.

Ethics integrated throughout. Each case analysis asks students to consider whose interests are represented in the model and what unintended consequences might arise — grounded in Freire’s concept of critical consciousness.

Real-world case analysis. Each student identifies a real-world scenario that could benefit from LP optimization. Selected cases are worked through collaboratively in class.

Selected Student Feedback (TRACE Evaluations, Spring 2026)

“I did like that she was very happy to teach this course — I could tell that she loves this topic so much, and I like that she was able to listen to our feedback to make the course better. Definitely, the group project was the most fun project to do.”

“The instructor was very well-prepared and explained concepts clearly. Class time was used effectively, and the feedback on assignments was helpful. They were also approachable and created a respectful and engaging learning environment.”

“The instructor did a great job creating an inclusive and respectful environment where everyone felt comfortable participating.”

TRACE Rating Highlights (course mean vs. dept. mean):

Displayed enthusiasm for the course: 4.80 (dept. avg: 4.55)
Provided sufficient feedback: 4.60 (dept. avg: 4.29)
Facilitated inclusive learning environment: 4.60 (dept. avg: 4.54)

Course Materials

The following materials are available for review:

Reflection

This course was my most significant teaching experience to date. With full instructional autonomy, I redesigned the delivery mid-semester based on student feedback — moving from slides to collaborative board work. Students who once struggled began completing assessments independently. Students who were once disengaged began contributing to class discussion.

The 2026 COE Outstanding Graduate Teaching Award, received for this course, affirmed what I already felt: that listening to students and adapting in real time is the heart of teaching.