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Business Automation

Automating Cleaning Schedules Around Building Access

Rotas fail on constraints rather than on hours. Access windows, key holding, lone worker rules and skills all bind before labour does.

Updated 2 min readBy SpiderHunts Technologies

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Quick answer — TL;DR

Scheduling is a constraint problem, not a headcount problem. Access windows, key holding, required certifications and lone worker rules usually bind before available hours do, and a rota that ignores them fails in the first week.

The short answer

Most cleaning rotas break on constraints: a site that can only be accessed between certain hours, a key that one person holds, a task needing a certification, a rule that nobody works alone after a time.

Encode the constraints first. A schedule that respects them and is slightly less efficient beats an optimal one that cannot be worked.

The constraints that actually bind

  • Access windows, which differ per site and sometimes per day
  • Key holding and alarm codes, held by specific people
  • Certifications for specialist tasks
  • Lone working rules, particularly out of hours
  • Travel time between sites, on real routes
  • Contracted hours and rest requirements

Travel time is the one most often estimated badly. Straight-line distance between two postcodes bears little relation to a van in traffic at seven in the morning.

Prediction feeds the optimiser

PredictionUsed by the schedule
How long this site actually takesSlot length
Travel time on this route at this hourSequencing
Likelihood of absenceCover requirements
Whether access will be availableOrdering and fallbacks

The first row is the largest single improvement in most operations. Specification times and actual times differ consistently per site, and using actuals fixes a rota that always runs late.

Build in the fallback

Schedules meet reality: someone is off, a site is locked, a job takes twice as long. A system that produces a perfect plan and no way to adjust it gets abandoned within a fortnight.

  1. Make manual adjustment easy and quick.
  2. Show the consequence of a change, such as which site now runs late.
  3. Keep a list of who can cover which site, with the constraints.
  4. Record why a change was made, so patterns become visible.
  5. Re-optimise from the current position rather than from the original plan.

Respect the people constraints

Staff have preferences, caring responsibilities and travel limits. A schedule that ignores them produces turnover, and turnover in this sector is expensive and already high.

Capturing preferences as soft constraints, honoured where possible, tends to improve retention more than small pay differences do.

FAQ

Frequently asked questions

The questions readers ask us after this guide.

Still have a question?

Ask us directly — a senior engineer will get back to you.

Ask about your project

Is scheduling a machine learning problem?

The scheduling itself is optimisation. Machine learning supplies the inputs, particularly how long sites actually take and realistic travel times.

Can schedules be fully automated?

They can be generated automatically, and they will need human adjustment. Aim for a good draft rather than a final answer.

What about lone working rules?

They are a hard constraint and belong in the optimiser, not in a supervisor's memory.

How do we get accurate site durations?

Record actual start and finish times, which most workforce systems already capture, and use those rather than specification times.

Keep reading

More on Business Automation

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