IE 5137 — Computational Modeling in Industrial Engineering
4 semester hoursGraduateLecturetypical days: T/ThBostonTraditional
Builds computational models for industrial engineering applications. Offers students an opportunity to learn how to identify the problem, split it into subsystems, develop mathematical models of each sub-system, and implement in Python. Selected problems are specific to industrial engineering applications with examples of inventory systems, queuing systems, production planning and control, supply chain management, transportation, network flows, forecasting, scheduling, Monte Carlo simulation, regression analysis, sensitivity analysis, and decision support systems in data science and machine learning to test and learn from models. Students also have an opportunity to learn how to use Python libraries to implement the corresponding data structures and algorithms.
Prerequisites
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 1 | 8 | 30 | 27% | 22.0 |
| Spring 2025 | 1 | 9 | 30 | 30% | 21.0 |
| Summer A 2025 | 1 | 6 | 30 | 20% | 24.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 0% · T 61% · W 0% · Th 65% · F 35%
Common patterns: R (39% of sections), TF (35% of sections), TR (26% of sections) — in patterns, R means Thursday
Professors
Fall
Spring
- Sivarit Sultornsanee (100% of students) · reviews
Summer A
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (IE course descriptions) · Student reviews on RateMyHusky · All IE courses · Plan it at numap.app