Prepare Network
Enter facilities, customers, demand, capacity, and costs.
Allocate customer demand to facilities while balancing capacity, distance-based transport cost, and fixed facility cost.
Use this tool when you need to decide which warehouses or facilities should serve each customer region under capacity and cost constraints.
It estimates the lowest-cost feasible allocation, open facilities, flow volumes, utilization, average distance, and savings versus a current assignment.
Facility coordinates, capacities, fixed costs, customer coordinates, demand, optional current facility, and transport cost per unit/km.
The optimizer shows where demand can be served at lower cost while still respecting available facility capacity.
Enter facilities, customers, demand, capacity, and costs.
Minimize transport and open-facility cost.
Review routes, distances, utilization, and flow volumes.
Compare current and optimized network decisions.
Prepare facility and customer records before optimizing the allocation.
Upload a CSV file with one row for each facility and each customer or demand point.
Example rows: Facility | Midlands DC | 52.4862 | -1.8904 | 5200 | 32000 and Customer | London Region | 51.5072 | -0.1276 | 2600 | Southern DC
What ATH does next: The tool tests feasible facility sets, allocates customer demand to open facilities, and selects the lowest-cost network within the capacity constraints.
Network data, optimization, diagnostics, and exports remain in your browser. The optional map view loads background map tiles from OpenStreetMap for the visible geographic area.
For operational use, upload real facility-to-customer lane distances from routing, TMS, GIS, or carrier data. If no matrix is uploaded, ATH uses straight-line Haversine distance from latitude and longitude as a transparent approximation.
Distance example: Facility | Customer | Distance km then Midlands DC | London Region | 178
Limitation: Straight-line distance is useful for early screening, but it does not reflect road distance, travel time, tolls, delivery windows, congestion, vehicle constraints, or carrier rate structures. Map route lines are visual connections unless you provide route distances.
No distance matrix uploaded. Straight-line distance will be used.
Capacity and fixed cost are used when the optimizer decides whether a facility should be open.
| Name | Latitude | Longitude | Capacity | Fixed cost | Remove |
|---|
Current facility is optional. Add it when you want a current-versus-optimized comparison.
| Name | Latitude | Longitude | Demand | Current facility | Remove |
|---|
The optimizer minimizes transport cost plus fixed facility cost while satisfying demand and capacity where feasible.
Review optimized cost, utilization, flow allocation, and scenario comparison.
| Facility | Customer | Flow | Distance km | Distance source | Transport cost |
|---|
Facilities, demand points, and optimized flows are shown as an illustrative map-based network view. Route lines show facility-to-customer connections; they are not road-route geometry.
Map background tiles are requested from OpenStreetMap. Facility names, customer names, demand, capacity, cost, flow volumes, and optimization results are not sent by ATH.
Supply chain network optimization helps decide how customer demand should be assigned to facilities when distance, transport cost, fixed operating cost, and facility capacity all matter.
This ATH tool focuses on a single-echelon allocation problem: facilities serve customer or demand points directly. It is useful for practical network reviews, but it should be paired with operational judgement when lead time, service policy, resilience, carrier constraints, or carbon targets are important.
Keep demand, capacity, and transport-cost units consistent. A pallet-based model should not mix cases, tonnes, and orders without conversion.
A mathematically feasible allocation can still be operationally weak if utilization is very high or if service-time constraints are missing.
Current-versus-optimized savings are meaningful only when current facility assignments and fixed facility costs are realistic.
For each open-facility set, minimize total cost = sum(flow × distance × transport cost per unit/km) + sum(open facility fixed costs), subject to customer demand and facility capacity constraints. Distance comes from the uploaded route matrix where a matching facility-customer lane exists; otherwise ATH uses Haversine straight-line distance from coordinates.
The browser enumerates feasible open facility sets for modest scenarios and solves each allocation using min-cost flow. This gives an exact result within the current browser limits.
The model is single-echelon and does not yet include facility-location generation, service-level constraints, lead times, carbon, route vehicles, resilience, or multi-period demand.
Yes. For modest scenarios it tests feasible open-facility combinations and includes fixed facility cost in the objective.
If you upload a route or distance matrix, matching facility-customer lanes use those distances. Any lane without an uploaded distance uses Haversine straight-line distance from latitude and longitude. Straight-line distance is transparent and useful for early screening, but it is not road distance, travel time, or carrier-rated distance.
No. It is a practical browser-based allocation optimizer. Larger multi-echelon or mixed-integer models usually need a dedicated solver.
The tool stops and shows a high-risk diagnostic because demand cannot be fully satisfied under the entered capacity constraints.
No. CSV parsing, optimization, diagnostics, route drawing, and exports run locally in your browser. The map background uses OpenStreetMap tiles, so tile requests are made for the visible map area, but ATH does not send your facility, customer, demand, capacity, cost, or optimization data.