Industrial Optimization
Steel-Coil Slitting Optimizer
Designed and implemented a steel-coil slitting optimization engine in Python using Google OR-Tools, cutting manual planning effort for production scheduling at Benaka Electronics.
SWE Intern
Role
5
Phases Shipped
01 — Problem
Production schedulers were manually working out how to slit steel coils into order-sized strips, a combinatorial problem that eats planning time and leaves material-waste on the table.
02 — Context
Benaka Electronics needed the scheduling turned into software as part of an MSRIT-cohort engineering placement, with the engine certified for real production use, not just a proof of concept.
03 — System Built
A constraint-based optimization engine that takes coil dimensions and order requirements and outputs a slitting plan, replacing manual spreadsheet planning.
04 — Engineering
Modeled the slitting problem as a constraint-satisfaction/optimization problem in Google OR-Tools. Delivered and certified the optimizer across five development phases, including persistence for saving and recalling schedules. [EVIDENCE NEEDED: exact objective function and constraint set].
05 — Decisions
Chose OR-Tools over a hand-rolled heuristic to get a solver-grade guarantee on feasibility, trading some tuning flexibility for correctness on a problem where a bad plan wastes physical material.
06 — Result
Delivered and certified across five production phases, reducing manual planning effort for the scheduling team.
07 — What I Learned
Industrial optimization has a different bar than research code — persistence, phase sign-off, and operator trust matter as much as the algorithm.
08 — Next Iteration
Generalizing the same OR-Tools formulation to adjacent scheduling problems beyond slitting.