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Class Schedule Optimization

Solo project solving a class-schedule assignment problem in Python by combining three optimization paradigms on the same problem — comparing tradeoffs in solution quality, runtime, and stakeholder-weighted preference handling.

Final project for DS1324 — Prescriptive Analytics, BS Data Science, University of Asia and the Pacific. Solo.

Methods compared

  1. Linear Programming (LP) — exact optimization with deterministic constraints.
  2. Genetic Algorithm (GA) — heuristic search via DEAP / PyGAD; useful when the search space is too large or non-convex for pure LP.
  3. Analytic Hierarchy Process (AHP) — multi-criteria decision making to weight competing stakeholder preferences (faculty preferences, room utilization, student conflicts) before feeding into the optimizer.

Repository layout

notebook/      Main project notebooks (final + earlier dated version)
documents/     Full documentation, project outline

Stack

Python · PuLP (LP) · DEAP / PyGAD (Genetic Algorithm) · numpy · pandas

Status

Course final project, completed Feb–May 2026. Solo.


David Nathaniel P. Riego · BS Data Science, UA&P (Aug 2023 – Aug 2027 expected) · LinkedIn

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Solo project combining Linear Programming, Genetic Algorithms, and AHP on the same class-schedule assignment problem. DS1324 @ UA&P.

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