The data extraction and modelling page allows users to upload a datafile with site and auditor information. On clicking "Run Scheduling", the tool will carry out data validation, data extraction of relevant geographical information, clustering of sites based on distance, and finally optimisation modelling to create an initial auditor schedule which can be adjusted in the Schedule Editor.
Input excel files must contain:
"Auditors" worksheet with the columns [Auditor Name, Postcode, Transport, Audit Split]
"Sites" worksheet with the columns [ID, Postcode, Complexity]
Clustering Distance Threshold (km) - sets the maximum distance between any two points to be considered part of the same cluster in the initial clustering phase.
Weekly Inter-Cluster Travel Time (mins) - the maximum travel time (minutes) between different clusters within a week (clusters will be assigned to different weeks if the travel time between them is greater than this amount).
Site Grouping Density Within Weeks: Controls how tightly sites are grouped for an auditor within a single week.
- Low Density (Fewer, larger groups): Auditors might have fewer, but potentially larger, groups of sites per week. This could mean more travel between sites within that group but fewer distinct "trips" from home/base.
- Medium Density: A balance between grouping and spreading out.
- High Density (More, smaller groups): Auditors are more likely to have multiple smaller, more compact groups of sites per week. This could reduce travel time *within* a group but might mean more "starts" from a home/base to different small groups.
Adherence to Auditor Workload Splits: Determines how strictly the model tries to match the "Audit Split" percentages defined in the input file.
- Loose: The model has more flexibility to assign work, even if it deviates significantly from the target splits, to achieve other objectives (like minimizing travel).
- Moderate: A balanced approach. The model tries to meet splits but can deviate if other factors are strong.
- Strict: The model will heavily prioritize matching the target workload splits, potentially at the cost of other efficiencies (e.g., increased travel).
(N.B. - These preferences are balanced against each other and other factors like minimizing travel. The model aims for the best overall schedule based on your combined preferences.)