Bill Ballou
9e00901179
Full React+TypeScript app with LP-based optimization engine, drag-and-drop specialization ranking (with touch/arrow support), course selection UI, results dashboard with decision tree, and two optimization modes (maximize-count, priority-order).
38 lines
2.9 KiB
Markdown
38 lines
2.9 KiB
Markdown
## Why
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EMBA students must select 12 elective courses (one per set) across three terms, earning 30 total credits. The program offers 14 specializations, each requiring 9+ credits from qualifying courses — but credits cannot be duplicated across specializations. Determining which specializations are achievable, and which course selections optimize toward preferred specializations, is a non-trivial credit allocation problem that students currently solve by intuition. A web application that solves this exactly would let students make informed decisions with full visibility into trade-offs.
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## What Changes
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- New React web application for EMBA specialization planning
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- Embeds the full course-specialization matrix (46 courses, 14 specializations) as static data — no backend required
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- Users rank specializations by priority (drag-to-reorder)
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- Users pin course selections for any subset of the 12 elective sets
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- Two optimization modes the user toggles between:
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- **Maximize Count**: find the largest set of achievable specializations, using priority ranking to break ties among equal-count solutions
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- **Priority Order**: guarantee the #1-ranked specialization first, then greedily add #2, #3, etc. (lexicographic optimization)
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- Exact LP-based solver (not greedy heuristic) for credit allocation feasibility
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- Decision tree for open (unpinned) sets: for each open set, show what each course choice enables or eliminates, ordered by decision impact
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- Handles all program constraints: credit non-duplication, required course gates, Strategy S1/S2 cap, same-set mutual exclusions
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## Capabilities
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### New Capabilities
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- `course-data`: Static data model embedding the 46 courses, 12 elective sets, 14 specializations, course-specialization qualification matrix (■/S1/S2 markers), and required course mappings
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- `optimization-engine`: LP-based credit allocation solver supporting both optimization modes (maximize-count and priority-order), with feasibility checking across specialization subsets, Strategy S2 enumeration, and required course gate enforcement
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- `specialization-ranking`: User interface for ordering specializations by priority via drag-and-drop, with mode toggle between maximize-count and priority-order optimization
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- `course-selection`: Interface for pinning/unpinning course choices across the 12 elective sets, with immediate recalculation on change
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- `results-dashboard`: Analysis output showing per-specialization status (achieved/achievable/unreachable/missing_required), credit allocation breakdown, and decision tree for open sets ordered by impact
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### Modified Capabilities
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_(none — greenfield project)_
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## Impact
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- New React application (single-page, client-side only)
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- Dependencies: React, an in-browser LP solver (e.g., `javascript-lp-solver`), drag-and-drop library
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- No backend, database, or API — all computation runs in the browser
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- Data sourced from `SPECIALIZATION_EVALUATION.md` (already in repo)
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