Cluster Details

When the app has created a clustering for the selected DC, you can find more details for each cluster on the Cluster Details page. The Clusters table is divided in four parts, described below. At the bottom of the table there are four aggregators (sum, min, max, mean). The map and the legend widget that are shown on this Cluster Details page, are the same as on the Create Clusters page.

General Cluster data

The First part of the Clusters table contains some general cluster data for each cluster:

  • #Customers - The number of customers in a cluster.
  • Customer Density - This number indicates the density of the customers in a cluster. The higher the number, the closer the locations in the cluster.
  • Average Distance Between Customers - The average distance (in the provided THU) between each combination of two customer locations in the cluster.
  • Average Stem Distance - The average distance (in the provided THU) from DC to customer locations in the cluster.
  • Total Demand - The total demand for all locations in a cluster.
  • Average Drop Size - The average drop size over the locations in the cluster.

Cluster Data for Max Driving Time Constraint

The second part of the Clusters table contains calculations where the driving time is the only limiting factor. So, a vehicle has ‘unlimited’ capacity. The calculated data is displayed for the vehicle that has the biggest capacity within the available time. The following calculations are displayed:

  • Average Number of Drops per Trip - The average number of drops that fit in the maximum allowed truck driving time, given the average vehicle speed and (un)load times and the average stem distance and distance between customers.
  • Average Vehicle Load per Trip - Average Number of Drops per Trip multiplied by the Average Drop Size. This can be more than the vehicle capacity, as the driving time is the limit.
  • Minimum Vehicle Type - The minimum available vehicle type that is required to transport the average vehicle load per trip. When there is no vehicle that fits all the load, the biggest vehicle is selected.
  • Vehicle Utilization [%] - The average vehicle load per trip divided by the vehicle capacity, corrected with its fill rate at 100%. When this percentage is 100% or lower, the cluster does not max out on vehicle capacity (max drive time is the only limit). When the vehicle utilization is above 100%, the cluster maxes out on both drive time and vehicle capacity.

Cluster Data for Max Load Constraint

The third part of the Clusters table contains the calculations where the vehicle capacity is the only limiting factor (max load). So, there is no time limit in this case. The vehicle for which the calculated data is displayed, is the vehicle with the lowest total cost for the cluster. The following calculations are displayed:

  • Average Number of Drops per Trip - The average number of drops that can be done when the vehicle is fully loaded. So vehicle capacity (corrected with its fill rate) divided by the average drop size.
  • Average Vehicle Load per Trip - As the vehicles are fully loaded, the average vehicle load per trip equals the vehicle capacity corrected with its fill rate.
  • Minimum Vehicle Type - The vehicle type that has the lowest total cost for the cluster.
  • Vehicle Utilization [%] - Since the vehicles are fully loaded, the vehicle utilization equals the vehicle fill rate.

Cluster Data selected

In the previous parts, you can see all kind of calculations where either the maximum driving time, or the maximum vehicle load was the limiting factor. In reality, it can be different for each cluster, whether you max out on drive time or on vehicle load. In this fourth part of the Clusters table, for each cluster the calculated data is displayed for the constraint that is limiting in that cluster.

  • Constraint - In this column, you can see for the cluster whether it maxes out on drive time or on vehicle load.

The other columns are equal to the columns in Cluster Data for Max Driving Time Constraint or Cluster Data for Max Load Constraint), depending on the limiting cluster constraint. An explanation of these columns can be found above.