Order Configuration¶

Configuration for order generation and characteristics.

Schema¶

orders:
  arrival_process:                 # Required
    type: <string>
    rate_per_min: <float>

  lines_per_order:                 # Required
    type: <string>
    mean: <float>
    dispersion: <float>

  sku_popularity:                  # Required
    type: <string>
    alpha: <float>

  due_times:                       # Optional
    type: <string>
    minutes: <float>

Arrival Process¶

Defines how orders arrive over time.

Poisson Process¶

Orders arrive randomly with specified rate.

orders:
  arrival_process:
    type: poisson
    rate_per_min: 1.0  # 60 orders per hour

rate_per_min¶

Type: float Required: Yes

Order arrival rate per minute.

rate_per_min	Orders/Hour
0.5	30
1.0	60
2.0	120
5.0	300

Lines Per Order¶

Defines how many items/lines each order contains.

Negative Binomial¶

orders:
  lines_per_order:
    type: negbinomial
    mean: 3.0
    dispersion: 1.0

mean¶

Type: float Required: Yes

Average number of lines per order.

lines_per_order:
  type: negbinomial
  mean: 4.5  # Average 4.5 items per order

dispersion¶

Type: float Default: 1.0

Controls variance in order sizes.

Dispersion	Effect
< 1.0	Less variance, more consistent
1.0	Standard variance
> 1.0	More variance, some very large orders

lines_per_order:
  type: negbinomial
  mean: 3.0
  dispersion: 1.5  # Higher variance

SKU Popularity¶

Defines which SKUs are selected for orders.

Zipf Distribution¶

Models realistic SKU popularity where few SKUs are very popular.

orders:
  sku_popularity:
    type: zipf
    alpha: 1.0

alpha¶

Type: float Default: 1.0

Controls popularity skew.

Alpha	Effect
0.5	Mild skew, more even distribution
1.0	Standard Zipf (80/20 rule approximately)
1.5	Strong skew, few SKUs dominate
2.0	Extreme skew

# Highly concentrated picking
sku_popularity:
  type: zipf
  alpha: 1.5  # Top SKUs get most picks

Due Times¶

Optional configuration for order due times.

Fixed Offset¶

Orders are due a fixed time after arrival.

orders:
  due_times:
    type: fixed_offset
    minutes: 60  # Due 60 minutes after arrival

minutes¶

Type: float

Minutes after order arrival when order is due.

due_times:
  type: fixed_offset
  minutes: 45  # 45-minute SLA

Examples¶

Standard E-Commerce¶

orders:
  arrival_process:
    type: poisson
    rate_per_min: 1.5  # 90 orders/hour

  lines_per_order:
    type: negbinomial
    mean: 2.5
    dispersion: 1.0

  sku_popularity:
    type: zipf
    alpha: 1.2

  due_times:
    type: fixed_offset
    minutes: 120

High-Volume Retail¶

orders:
  arrival_process:
    type: poisson
    rate_per_min: 5.0  # 300 orders/hour

  lines_per_order:
    type: negbinomial
    mean: 1.5          # Smaller orders
    dispersion: 0.8

  sku_popularity:
    type: zipf
    alpha: 1.0

  due_times:
    type: fixed_offset
    minutes: 30        # Fast turnaround

Wholesale Distribution¶

orders:
  arrival_process:
    type: poisson
    rate_per_min: 0.5  # 30 orders/hour

  lines_per_order:
    type: negbinomial
    mean: 15.0         # Large orders
    dispersion: 2.0    # High variance

  sku_popularity:
    type: zipf
    alpha: 0.8         # More even SKU distribution

  due_times:
    type: fixed_offset
    minutes: 240       # Same-day shipping

Peak Hours Simulation¶

orders:
  arrival_process:
    type: poisson
    rate_per_min: 3.5  # 210 orders/hour (peak)

  lines_per_order:
    type: negbinomial
    mean: 3.0
    dispersion: 1.2

  sku_popularity:
    type: zipf
    alpha: 1.3

  due_times:
    type: fixed_offset
    minutes: 60

Order Rate Calculation¶

From Orders per Hour¶

rate_per_min = orders_per_hour / 60

Examples:

Orders/Hour	rate_per_min
60	1.0
120	2.0
300	5.0

Expected Throughput¶

Expected orders = rate_per_min × effective_duration_minutes

Example:

Rate: 1.0/min
Duration: 60 min
Warmup: 10 min
Expected: 1.0 × 50 = 50 orders