Capacity Studies

Examples of fleet sizing and capacity planning.

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Fleet Sizing Study

Determine optimal number of robots for a given throughput target.

Scenario

# fleet_sizing.yaml
simulation:
  duration_s: 3600

stations:
  - { id: S1, node: 30, type: pick, concurrency: 2 }
  - { id: S2, node: 31, type: pick, concurrency: 2 }

orders:
  generation:
    type: constant
    rate_per_hour: 500  # Target throughput

Run Sweep

waremax sweep fleet_sizing.yaml \
  --param "robots.count=[5,10,15,20,25,30,35]" \
  --runs 3

Results

Robots	Throughput	Utilization	Task Time	Status
5	280/hr	95%	64s	Under capacity
10	420/hr	89%	52s	Under capacity
15	495/hr	82%	45s	Near target
20	502/hr	72%	43s	Target met
25	505/hr	62%	42s	Excess capacity
30	498/hr	52%	44s	Congestion
35	485/hr	45%	48s	Congestion

Analysis

Throughput vs Fleet Size:

500│           ┌───┬───┐
   │         ┌─┘   │   └─┐
400│       ┌─┘     │     └──
   │     ┌─┘       │
300│   ┌─┘         │
   │ ┌─┘           │
200│─┘             │
   └───────────────┴─────────
   5  10  15  20  25  30  35
              Robots

Peak throughput at 20 robots
Diminishing returns after 15
Congestion after 30

Recommendation

Optimal: 20 robots

• Meets 500/hr target
• 72% utilization (healthy)
• Buffer for variability

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Station Capacity Study

Determine required station slots for target throughput.

Scenario

# station_capacity.yaml
robots:
  count: 20  # Fixed fleet

orders:
  generation:
    type: constant
    rate_per_hour: 600  # Target

Run Sweep

waremax sweep station_capacity.yaml \
  --param "stations[0].concurrency=[1,2,3]" \
  --param "stations[1].concurrency=[1,2,3]"

Results

S1 Slots	S2 Slots	Total	Throughput	Avg Queue
1	1	2	320/hr	12.5
2	1	3	450/hr	6.2
1	2	3	445/hr	6.5
2	2	4	580/hr	2.1
3	2	5	605/hr	1.2
2	3	5	600/hr	1.4
3	3	6	610/hr	0.8

Recommendation

Optimal: 5 slots (3+2 or 2+3)

• Meets 600/hr target
• Reasonable queue lengths
• Cost-effective

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Growth Planning Study

Plan capacity for future growth.

Current State

• Throughput: 400/hr
• Robots: 10
• Stations: 4 slots

Growth Targets

Year	Target	Increase
Year 1	600/hr	+50%
Year 2	900/hr	+50%
Year 3	1200/hr	+33%

Capacity Matrix

waremax sweep growth_scenario.yaml \
  --param "orders.rate=[400,600,900,1200]" \
  --param "robots.count=[10,15,20,25,30,35,40]" \
  --param "total_station_slots=[4,6,8,10,12]"

Results Summary

Target	Min Robots	Min Slots	Recommended
400/hr	10	4	Current
600/hr	15	5	+5 robots, +1 slot
900/hr	22	7	+7 robots, +2 slots
1200/hr	30	10	+8 robots, +3 slots

Growth Plan

Year 0 (Current)
├── Robots: 10
├── Stations: 4 slots
└── Capacity: 400/hr

Year 1
├── Add: 5 robots, 1 station slot
├── Total: 15 robots, 5 slots
└── Capacity: 600/hr

Year 2
├── Add: 7 robots, 2 station slots
├── Total: 22 robots, 7 slots
└── Capacity: 900/hr

Year 3
├── Add: 8 robots, 3 station slots
├── Total: 30 robots, 10 slots
└── Capacity: 1200/hr

---

Peak Load Study

Size for peak demand periods.

Scenario

# peak_load.yaml
simulation:
  duration_s: 14400  # 4 hours

orders:
  generation:
    type: variable
    schedule:
      - { time: 0, rate: 300 }      # Normal
      - { time: 3600, rate: 600 }   # Peak (1 hour)
      - { time: 7200, rate: 300 }   # Normal

Run Sweep

waremax sweep peak_load.yaml \
  --param "robots.count=[15,20,25,30]"

Results

Robots	Normal Rate	Peak Rate	Queue @ Peak	Recovery
15	300/hr OK	450/hr	45 tasks	30 min
20	300/hr OK	550/hr	20 tasks	12 min
25	300/hr OK	600/hr	5 tasks	3 min
30	300/hr OK	600/hr	0 tasks	0 min

Recommendation

Choose based on priorities:

• Cost-focused: 20 robots (12 min recovery acceptable)
• Service-focused: 25 robots (minimal queue buildup)
• Zero-queue: 30 robots (over-provisioned for normal times)

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Charging Infrastructure Study

Size charging stations for fleet.

Parameters

battery:
  capacity_wh: 500
  consumption_rate_w: 50
  charge_rate_w: 200
  charge_threshold_pct: 20

robots:
  count: 20

Calculations

Operating time: 500 × 0.8 / 50 = 8 hours
Charge time: 500 × 0.75 / 200 = 1.88 hours

Daily charges per robot: 24 / (8 + 1.88) ≈ 2.4
Total daily charges: 20 × 2.4 = 48

Required bay-hours: 48 × 1.88 = 90.2
With 24 hours: 90.2 / 24 = 3.76 bays minimum

Sweep

waremax sweep charging_study.yaml \
  --param "charging_stations[0].bays=[2,3,4,5,6]" \
  --duration 86400  # 24 hours

Results

Bays	Avg Queue	Max Queue	Throughput Impact
2	3.5	8	-5%
3	1.8	5	-2%
4	0.6	3	-0.5%
5	0.2	2	0%
6	0.1	1	0%

Recommendation

4-5 bays for 20 robots

• 4 bays: Minimal impact, occasional queues
• 5 bays: No throughput impact

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Related

• Capacity Planning Tutorial
• Benchmarking
• Battery Management