Selects those warehouses - from a user defined set - that minimize fixed* and variable warehousing costs, plus transport costs given the number of warehouses to be selected (from each group).
By grouping warehouses per region you ensure that each region gets a warehouse.
By putting (current) warehouses in the 'must‑be‑included'‑group you ensure that those are included.
Calculates transport costs based on distances and cost curve (smaller shipments are relatively more expensive).
Supports multiple warehouse cost drivers for more detailed warehouse cost modeling.
* More precisely, binary-fixed, as the fixed costs of a warehouse are applicable only if that warehouse is selected
= selected warehouse
= non-selected warehouse
Map display size factor - customers
Map display size facor - warehouses
Note: map is updated after solver (re)run.
In the demo data fixed/variable cost are the same for each warehouse. This has been done on purpose. If you change number of warehouses, and rerun, then selected warehouses will look logical on the map. If different fixed/variable costs are applicable, then selected warehouses will no longer be necessarily located "in the middle of customers". This may look weird, and cause some confusion at first sight. But from a cost-perspective it will make perfect sense. Adjust the demo data to see for yourself.
Full Truck Load - capacity
Full Truck Load - costs per kilometer
Distance circuitry factor
Shipment size is expressed as % Full Truck Load (FTL) and via the shipment costs curve converted into a less than full truck (LTL) rate.
This curve captures that the larger a shipment, the lower the costs per unit transported. For example, a shipment size of 50% FTL brings 72% FTL costs (or 72% FTL rate), so per each unit transported it is 1.44 times more expensive than FTL transport.
The rate can be made region specific via the warehouse transport costs index.
Enter data semicolon or tab separated. You can copy data in from Excel.