IE437 · Assignment 1 · Data & assumptions
From the classroom brief to the two proposals
One comparison before the two case studies
| Comparison | Course brief | Case A · Response + BO | Case B · Synthetic consumers |
|---|---|---|---|
| Starting records | Film, cinema, screening date/time, purchaser, paid tickets. One completed purchase per row; no dataset. | Joined schedules and purchase outcomes would train the model. The demo uses declared coefficients. | Purchasing accounts ≠ individual attendees. Fictional people and histories are generated. |
| Added data | Full schedules, runtimes, seats and operating rules are not supplied. | 6 films × 6 sites × 3 screens; 90 reference showings. Added runtimes, seats, rules and response factors. | 24 films across 2027; profiles, tastes and histories; movie features/dates, seats, prices and rules. |
| Case assumptions | Planning horizon, current workflow and policies are left for students to define and justify. | One-day plan. Declared time/site/competition effects estimate attendance under operating rules. | Annual horizon; 10,000 local synthetic people. Preferences/frequency → movie choices → seats. |
| Proposed strategy | Choose and justify a programming strategy. No specific model or algorithm is prescribed. | Learn schedule → audience response; use Bayesian optimization (BO) to select schedule evaluations. | Model consumers → choices → demand → schedule search. Learned M1 is proposed. |
| Executable demo | Inputs → execution → inspection → revision, with a reference-plan comparison. | Upload → settings → ILS → compare/export. Synthetic response; no learned fitting or BO. | Profiles/movies → demand → plan/choices → compare/replan. Rule-based M0 + local search; M1 not applied. |
The examples add fictional planning inputs to make each strategy executable. Buying three tickets does not supply three attendee histories.