Three constraints that fight each other every day
Supply
9 springs on three cost tiers (owned, leased and spot), each with a daily extraction licence cap.
Quality
Each bottling plant accepts water only from its nominated springs, so the cheapest spring is not always allowed.
Fleet
24/7 shifts, 28 to 60 kL tankers, road-access limits for long combinations and one or two loading bays per site.
See the plan move
Press Animate day to watch every tanker. Click any blue spring to take it offline and the whole network re-plans around it.
Live network on real roads
Fixed shuttle lanes versus the optimised plan
Change the day, watch it re-plan
Every control re-solves the whole network live. Demand volumes and cost assumptions sit under Advanced assumptions.
Advanced assumptions: costs
What the plan actually does
Every tanker, hour by hour
How hard each spring works
Share of each daily licence cap used by the optimised plan.
Where the water comes from
Spot water is the expensive recourse tier: the optimiser holds it back until owned and leased licence caps bind (summer peak or outages), a common three-tier procurement structure.
Which spring may supply which plant
Customers accept water only from nominated sources (taste/composition). Sites marked ▲ cannot receive A-double combinations due to road-access restrictions.
200 simulated days with traffic and delays
Real operating rules, not a simplified model
How the baseline and the optimiser work
Baseline: dedicated shuttle lanes, the common manual-planning pattern: each tanker is locked to one customer lane with its traditional sources and shuttles back and forth all shift. Both modes queue for limited loading/unloading bays (Springs A and E and the metro plants have 2 bays; other sites 1), so congestion and waiting emerge naturally as demand and service times rise. Optimised: a multi-start randomised construction (GRASP-style, 12 starts) followed by inter-route local search - swap and relocation of planned jobs between vehicles on the dispatch board (no water is ever transferred between tankers) - jointly choosing procurement tier, source, vehicle configuration and job sequence. Distances and travel times come from the OSRM road network matrix (heavy-vehicle time approximated as car +8%; straight-line fallback if offline). This in-browser heuristic demonstrates the concept; the production model is an exact time-space MILP solved in Gurobi with a tactical allocation layer.
Your network, planned the same way.
This demo runs on illustrative data. With your springs, plants, fleet and costs, the same engine plans real days and tests them before you commit.
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