Optimization, Simulation & Digital Twins for Supply Networks (Python)

Course Description

Prerequisite: MNS 407 This course develops the ability to model, solve, and communicate supply chain network decisions using optimization, simulation, and introductory digital twin methods. Students formulate and analyze core logistics and operations problems, including transportation planning, assignment decisions, and facility location, and implement these models in Python to generate defensible, data-supported solutions. The course also introduces practical routing heuristics and solution evaluation approaches used when real-world problems exceed the limits of exact optimization. To capture operational realities that optimization alone may not represent, students build discrete-event simulation models to analyze variability, congestion, and capacity constraints and to quantify impacts on throughput, service, and cost. The course culminates in an applied project in which students integrate an optimization model with a simulation model to create a basic digital twin that supports a capacity planning recommendation, reported through clearly defined KPIs and sensitivity analysis.

Code

SCM 4500

Credits

3.0

Subject

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Programs That Include This Course

• Bachelor's Bachelor of Science in Logistics and Supply Chain Management with Analytics