Perishable Supply Chain: Spoilage & Transit Simulator (2D)

Transport Mode

Route & Climate

Refrigeration temperature 4 °C

Route distance 180 km

Simulation speed 4×

Fleet Status

Avg. freshness

100%

Transit time

0.0 h

Spoilage loss

0 kg/h

Net revenue

$0/h

Shipments/trip

—

Deliveries done

How it works (2D model)

This 2D companion uses the identical spoilage model as the 3D simulator, viewed as a top-down route map instead of an orbiting 3D scene. Each shipment leaves a farm at 100% quality and travels farm → distribution hub → store along the selected mode's route. Freshness decays with a simplified exponential spoilage law driven by temperature and elapsed transit time:

Q(t) = 100 · exp(-k · T · t)
k = 0.012 per (°C·hour)   [decay constant]
T = refrigeration temperature (°C, clamped ≥ 0)
t = hours elapsed in transit

Transit time itself is distance ÷ mode speed. A faster mode shortens t and reduces spoilage even at the same temperature, but each mode also trades off capacity per trip:

Truck : 90 km/h,  260 crates/trip
Train : 70 km/h, 900 crates/trip
Drone : 140 km/h,  6 crates/trip

Transport mode — sets route speed and per-trip capacity; trains are slow but bulk, drones are fast but tiny.
Refrigeration temperature — lower T flattens the exponential, keeping crates fresher for longer; below 0 °C is treated as 0 °C (no negative decay).
Route distance — longer routes mean more hours in transit, compounding the exponential loss.
Crate colour fades from green (fresh) through yellow to red (spoiled) as Q(t) falls; net revenue subtracts the market value of spoiled produce from delivered-fresh revenue.

Real-world relevance: this is the same shape of model used by cold-chain logistics planners to size refrigeration budgets and choose transport modes for perishable freight — balancing spoilage cost against fuel and vehicle cost. Drag the map to pan and scroll or pinch to zoom in on the route.

Fresh produce Degrading Spoiled

drag to pan · scroll to zoom