This guide walks users through importing data, managing planning inputs, running optimisation, and understanding results in CloudOps Send Planner.
Follow the steps below to prepare your data, apply planning rules, run optimisation, and review results.
CloudOps Send Planner helps users prepare pupil and vehicle data, apply planning rules, run optimisation, and review route outputs. The platform is designed to support operational planning with clearer structure and easier review at each stage.
Upload pupil and planning records in the expected CSV structure.
Check that addresses, schools, and transport needs are complete and accurate.
Make sure your available vehicles reflect real operational limits.
Review journey limits, grouping rules, and transport restrictions before you run.
Generate route outputs using the current planning data and active rules.
Check route suitability before using outputs operationally or exporting them.
Check data quality before running optimisation. Clear input data makes route outputs easier to trust, review, and explain.
Importing clean, structured data is essential for successful planning. The platform expects CSV input in the correct structure so records can be validated and used in optimisation.
| Field | How It Is Used |
|---|---|
| Pupil identifier | Used to uniquely track each pupil through planning and outputs. |
| Pupil name | Displayed in planning workflows where permissions allow. |
| Address or location fields | Supports pickup location setup and validation. |
| Coordinates | Improves route quality and may be essential for successful optimisation. |
| School or destination | Defines where the journey is heading and helps with grouping logic. |
| Vehicle or mobility requirements | Used for compatibility checks during assignment. |
| Planning flags | Supports rule-based decisions such as sharing or specialist transport needs. |
Some fields may improve planning quality but are not always mandatory. These can still help with visibility and route review.
The system may reject or flag rows with missing required values, invalid formatting, duplicate records, or incomplete coordinate information.
Pupil data is the foundation of route planning and should be kept accurate and complete. Once data has been imported, users should review pupil records before running optimisation.
Mobility support, supervision requirements, route suitability, and specialist transport needs can affect compatibility, grouping, and whether a route is feasible.
Poor or missing coordinates can lead to failed optimisation or inaccurate route planning, so location quality should always be checked.
Vehicle settings help the optimiser determine which journeys are possible and how pupils can be grouped. The data used here should reflect real operational limits, not theoretical maximums.
Fleets may include standard vehicles and specialist vehicles depending on route and pupil requirements.
WAV settings help the platform match pupils with vehicles that can support wheelchair-accessible travel where required.
Planning rules guide the optimiser and help ensure routes remain practical and compliant with operational requirements.
Tighter rules can reduce optimisation flexibility and may result in fewer or no valid route options. This is an important expectation to keep in mind before running a new scenario.
Optimisation uses the current planning data, vehicle settings, and rules to generate the best possible route plan within the active constraints.
Optimisation results depend on the data quality, vehicle capacity, and planning rules currently configured.
data quality and missing coordinates
strict constraints and insufficient vehicle capacity
Review results carefully before using them operationally.
Optimisation results should be reviewed for practicality, data accuracy, and operational suitability.
Optimisation supports planning decisions, but users should still apply operational judgement before accepting outputs.
Routes, groupings, and vehicle assignments should always be reviewed in context.
This section covers common problems and practical next steps. Use it to quickly narrow down likely causes before trying another run.
Possible causes include incorrect column headers, missing required fields, invalid formatting, or corrupted CSV structure.
Possible causes include blank fields, import validation errors, duplicate rows, or records filtered out because values were invalid.
Routes may fail or produce poor results without valid coordinates, especially where location accuracy is essential for pickup planning.
Possible causes include insufficient vehicle capacity, overly restrictive planning rules, incompatible transport requirements, or missing or invalid location data.
This usually means there is no feasible solution under the current rules, there are not enough valid vehicles, or the planning data is incomplete.
Unexpected results are often caused by outdated pupil or vehicle data, coordinate issues, incorrect rule setup, or unrealistic capacity settings.
Use CSV in the expected structure so required fields can be validated correctly.
Required columns typically include pupil identifier, pupil name, location details, destination, and planning or transport requirement fields where relevant.
Coordinates are strongly recommended because missing or inaccurate coordinates can affect route quality and may prevent successful optimisation.
This usually means the current planning rules, vehicle availability, or input data do not allow a feasible route plan.
The system may fail to assign all pupils or may produce no feasible solution under the current constraints.
Yes. Review and adjust planning rules before re-running if the previous output was too restrictive or unsuitable.
Yes. Results should always be reviewed for practicality and operational suitability before use.
Use the Support page for the next step if the guidance here does not resolve the issue.
If the Help Guide does not resolve your issue, visit the Support page.
Go to Support