Time Windows & Delivery Constraints, Explained
A time window is the constraint most likely to quietly break a route if the underlying data is off, and it's also the one businesses have the most control over improving. This is less about the theory of constraint optimization - covered in more depth elsewhere - and more about the practical side: what actually goes wrong with time windows in real operations, and how to set them so they help rather than hurt.
What a time window actually constrains
A time window sets the earliest and latest a stop can be arrived at - it doesn't remove the service time needed once the driver's there. A ten-minute-wide window with a fifteen-minute service time is a contradiction the optimizer has to work around, often by pushing the stop's neighbors later than they'd otherwise need to be.
Common mistake: windows that don't reflect reality
Customers often give narrow windows out of habit rather than genuine need - "anytime before noon" becomes "between 10 and 10:15" because that's what a form asked for. Artificially tight windows shrink the set of feasible routes for no real benefit, and the cost shows up as a longer, less efficient overall route for every stop, not just the one with the unnecessarily narrow window.
Common mistake: ignoring service time
A time window paired with an unrealistic (usually zero or default) service-time estimate looks fine when the route is built and falls apart in execution, because every stop after it inherits the delay. This is the same failure mode as underestimating service time in field service dispatch - it just tends to be more visible in delivery routing because the knock-on effect compounds visibly across the rest of the day.
How tight windows trade off against total efficiency
More time windows - and narrower ones - give the optimizer less freedom to find a short overall route, and that's a real, expected trade-off rather than a flaw. A route with heavy time-window constraints will usually be longer in total distance than the same stops with no windows at all. That's the constraint doing its job, not the optimizer failing.
Setting windows that actually help
A few practical habits make the biggest difference:
- Ask for realistic ranges, not a customer's first instinct - most people will accept a wider window than they initially offer if asked
- Pad service-time estimates honestly, especially for stop types that vary (installs, multi-item drop-offs)
- If your tool distinguishes hard windows (must be met) from soft ones (preferred), use that distinction rather than marking everything hard by default