Vose SoftwareModelRisk Optimizer example · Manufacturing
Ceramics Studio: Kiln Firing Assignment
Which kiln should fire each ware batch so no single kiln carries the peak energy load?
The optimisation problem
Lowest mean peak kiln energy
- Decide
- A kiln for each of 15 batches (5 kilns)
- Goal
- Lowest mean peak kiln energy
- Limit
- None: a balancing problem
- Engine
- Vertex Detector
What the Optimizer found
| Starting plan | Optimizer’s plan | |
|---|---|---|
| Decisions changed | – | 2 of 15 |
| Goal: Mean peak kiln energy (kWh) | 204 | 203 |
All 15 decisions starting plan, then the Optimizer’s
| Batch 1 | 1 | 1 |
|---|---|---|
| Batch 2 | 2 | 4 |
| Batch 3 | 3 | 3 |
| Batch 4 | 4 | 2 |
| Batch 5 | 5 | 5 |
| Batch 6 | 1 | 1 |
| Batch 7 | 2 | 2 |
| Batch 8 | 3 | 3 |
| Batch 9 | 4 | 4 |
| Batch 10 | 5 | 5 |
| Batch 11 | 1 | 1 |
| Batch 12 | 2 | 2 |
| Batch 13 | 3 | 3 |
| Batch 14 | 4 | 4 |
| Batch 15 | 5 | 5 |
ModelRisk Optimizer run on 13 Oct 2026: the Vertex Detector engine, with Turbo simulation, tried 350 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 ceramics studio fires 15 ware batches across 5 kilns. Each batch's energy draw is uncertain (lognormal). Loading too many heavy batches into one kiln creates an energy peak (and a demand charge).
Decision, objective and constraint
- Decision
- 15 discrete kiln assignments (each batch then one of 5 kilns)
- Objective
- Minimise the Mean peak kiln energy (the busiest kiln)
- Constraints
- none — assignment/balancing
The engine: Vertex Detector
15 independent discrete assignment variables, no group then Method=Auto routes to the Vertex Detector.
The model
Assign each of 15 ware batches to a kiln so peak energy load is balanced across kilns.
InputDecision (the Optimizer changes it)Calculated or uncertain (sampled each iteration)Objective
Parameters
| Energy tariff ($/kWh) | 0.18 |
|---|
Ware batches (decision = kiln 1 to 5)
| Batch | Kiln (dec 1-5) | kWh (unc.) |
|---|---|---|
| Batch 1 | 1 | uncertain |
| Batch 2 | 2 | uncertain |
| Batch 3 | 3 | uncertain |
| Batch 4 | 4 | uncertain |
| Batch 5 | 5 | uncertain |
| Batch 6 | 1 | uncertain |
| Batch 7 | 2 | uncertain |
| Batch 8 | 3 | uncertain |
| Batch 9 | 4 | uncertain |
| Batch 10 | 5 | uncertain |
| Batch 11 | 1 | uncertain |
| Batch 12 | 2 | uncertain |
| Batch 13 | 3 | uncertain |
| Batch 14 | 4 | uncertain |
| Batch 15 | 5 | uncertain |
Results
| Peak kiln energy (kWh): objective | 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
- Peak load
- the energy of the most heavily loaded kiln
Find similar models
- Product
- Type
- Application
- Uses
Download the workbook and run the Optimizer. It opens in Excel with ModelRisk.
Related example models
- ModelRisk★★★★★Capacity Expansion DecisionHow many presses should we add?Sixty months of seasonal, uncertain demand against four capacity options with overtime, subcontracting and lost sales.
- ModelRiskOptimizer★★★★★Spare Parts and Maintenance Strategy Chosen by the OptimizerWhich replacement age and how many spare kits cost least while keeping the downtime promised to a customer?Bearing lives simulated on every spindle; the replacement age and spare kits chosen together by the Optimizer within a limit on downtime.
- ModelRisk★★★★★Supply-Chain DisruptionWhat do supplier disruptions cost us, and which protection pays?Random disruptions at four suppliers and a regional disaster; safety stock, a second source and insurance compared.
An illustrative example: the figures are invented for teaching. Last updated 9 October 2026.