Vose SoftwareModelRisk Optimizer example · Museums
Museum: Exhibit Tour Sequencing
Which viewing slot should each exhibit occupy so the crowd spreads evenly through the day?
The optimisation problem
The most even crowd through the day
- Decide
- A viewing slot for each of 14 exhibits (8 slots)
- Goal
- The most even crowd through the day
- Limit
- None: a pure assignment problem
- Engine
- Vertex Detector
What the Optimizer found
| Starting plan | Optimizer’s plan | |
|---|---|---|
| Decisions changed | – | 4 of 14 |
| Goal: Mean crowd variance across slots | 100,471 | 73,002 |
All 14 decisions starting plan, then the Optimizer’s
| Exhibit 1 | 1 | 1 |
|---|---|---|
| Exhibit 2 | 2 | 3 |
| Exhibit 3 | 3 | 5 |
| Exhibit 4 | 4 | 4 |
| Exhibit 5 | 5 | 8 |
| Exhibit 6 | 6 | 2 |
| Exhibit 7 | 7 | 7 |
| Exhibit 8 | 8 | 8 |
| Exhibit 9 | 1 | 1 |
| Exhibit 10 | 2 | 2 |
| Exhibit 11 | 3 | 3 |
| Exhibit 12 | 4 | 4 |
| Exhibit 13 | 5 | 5 |
| Exhibit 14 | 6 | 6 |
ModelRisk Optimizer run on 13 Oct 2026: the Vertex Detector engine, with Turbo simulation, tried 354 plans, each simulated with 10,000 samples, in 4 seconds. Both plans above were then simulated again with the same 10,000 samples.
What the workbook models
A museum routes visitors through 14 exhibits across 8 timed slots. Each exhibit has an uncertain 'pull' (how many visitors it draws). Bunching popular exhibits in the same slot causes congestion.
Decision, objective and constraint
- Decision
- 14 discrete slot assignments (each exhibit then one of 8 slots)
- Objective
- Minimise the Mean variance of crowd load across the 8 slots
- Constraints
- none — a pure assignment problem
The engine: Vertex Detector
14 independent discrete assignment variables with no combinatorial group. Method=Auto routes independent discrete decisions to the Vertex Detector, which explores the discrete assignment space efficiently.
The model
Assign each of 14 exhibits to a viewing slot so visitor congestion (crowd variance across the day) is minimised.
InputDecision (the Optimizer changes it)Calculated or uncertain (sampled each iteration)Objective
Parameters
| Visitors per hour (base) | 600 |
|---|
Exhibits (decision = time slot 1 to 8)
| Exhibit | Slot (dec 1-8) | Pull weight (unc.) | Load |
|---|---|---|---|
| Exhibit 1 | 1 | uncertain | calculated |
| Exhibit 2 | 2 | uncertain | calculated |
| Exhibit 3 | 3 | uncertain | calculated |
| Exhibit 4 | 4 | uncertain | calculated |
| Exhibit 5 | 5 | uncertain | calculated |
| Exhibit 6 | 6 | uncertain | calculated |
| Exhibit 7 | 7 | uncertain | calculated |
| Exhibit 8 | 8 | uncertain | calculated |
| Exhibit 9 | 1 | uncertain | calculated |
| Exhibit 10 | 2 | uncertain | calculated |
| Exhibit 11 | 3 | uncertain | calculated |
| Exhibit 12 | 4 | uncertain | calculated |
| Exhibit 13 | 5 | uncertain | calculated |
| Exhibit 14 | 6 | uncertain | calculated |
Results
| Crowd variance across slots: objective | calculated | |
|---|---|---|
| calculated | ||
| calculated | ||
| calculated | ||
| calculated | ||
| calculated | ||
| calculated | ||
| calculated | ||
| calculated |
Decision cells hold starting values: the workbook opens at the starting plan above, and the Optimizer finds its plan when you run it. The figures are illustrative.
Run it yourself. Open the workbook with ModelRisk, open the Optimizer from the ribbon (the saved task opens with it) and press Start.
Glossary
- Slot
- a timed viewing window in the visitor route
Find similar models
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- Uses
Download the workbook and run the Optimizer. It opens in Excel with ModelRisk.
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An illustrative example: the figures are invented for teaching. Last updated 9 October 2026.