Slot-based & Time Window Planning
Every delivery in the right window - planned automatically, every time.
Mojro models every customer time window as a hard constraint in the optimization run. It balances slot capacity across the fleet, sequences routes around committed windows, and pushes real-time ETAs to customers as routes progress.
The Problem
Slot planning without right
optimization isn't scheduling.
It's guessing at scale.
Slots get assigned manually by a dispatcher. It’s time-consuming, error-prone, and impossible to scale as order volumes grow.
Route sequences gets planned after slot assignment. That's two sequential decisions, each making the other worse.
Slot capacity is managed in a spreadsheet. Manual over and under booking happen regularly.
A window breach gets discovered at the point of delivery. The customer complaint is the first signal your team receives.
Capabilities
Every time window modeled. Every slot balanced. Zero manual assignment.
01 Time window as hard constraint
Time window compliance built into the plan, not just nice-to-have.
Customer preferred delivery windows are treated as hard constraints. The optimizer never produces a route that violates a committed window.
02 Slot capacity balancing
No slot overbooked, no vehicle underutlized.
Total deliveries per slot are balanced across vehicles as a network-level constraint and not managed slot by slot with no more over-and-underbooking.
03 Batch and real-time planning modes
Plan all slots at once or absorb new orders as they arrive.
Batch mode assigns all orders to slots at the start of each planning cycle. Real-time mode injects new orders into open slots as they arrive, with live re-optimization to absorb them.
04 ETA computation and update
Customers know exactly when their delivery is arriving.
Precise ETAs are computed and automatically advised based on route sequence, traffic, and driver progress.
05 Preferred window optimization
Preferred window first, acceptable window second.
When customers provide both options, Mojro optimizes for preferred first. It will fall back to acceptable only when preferred creates unacceptable network cost.
06 Appointment and dock scheduling
Fixed appointments modeled as absolute constraints, not just a preference.
Hospital deliveries, retail dock schedules, and office building hours are modeled distinctly from preferences.
What actually changes when every time window is modeled?
100%
time window adherenace
0
Delivery missed
5 - 10%
reduction in returns
The right delivery in the right time window - planned, not assigned.
