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REVOLUTIONISING OPERATIONS: AI VS ANTIQUATED SYSTEMS
A R T I C L E S
REVOLUTIONISING OPERATIONS: AI VS ANTIQUATED SYSTEMS
s urban environments grow more complex, the tools we use to plan and optimise logistics must evolve accordingly In this edition we re diving into how AI-based optimisation must adapt when moving from traditional delivery networks to the intricate world of waste collection and why the approach we ’ ve relied on for years in tools like My Transport Planner (MTP) simply doesn’t cut it in this new context
SECTION 1
THEOVERLAP-AND THEDIFFERENCES
n the surface waste collection and traditional delivery routes share some common traits: vehicle restrictions, time windows, and capacity constraints, to name a few These similarities initially made it seem like our established MTP algorithms might be transferable to waste logistics However the deeper we explored the clearer it became: waste collection isn’t just a variation of
delivery routing it’s a fundamentally different optimisation problem
SECTION 2
THECOREPROBLEM:DENSITYAND STRUCTURE
Traditional delivery routes are largely about going from point A to B to C in the most cost-effective way Our MTP algorithms do this by calculating the best sequence of stops based on time and distance without needing to understand the actual path between them The logic is simple: the fewer miles and minutes between stops, the better the route
But waste collection flips this model on its head
Waste vehicles must service every single bin on every relevant street sometimes over 10 000 stops in a single plan These aren’t neatly spaced deliveries; they’re densely packed, highly repetitive micro-stops, often on adjacent or interconnected streets Trying to optimise this by treating each bin as an individual stop leads to inefficiencies, confusion for collection staff and an algorithmic nightmare The crew doesn’t want to be told to collect bin 1, then 3, then 5 They want a route that mirrors how they operate in real life: drive down the street collect everything and move on
APARADIGMSHIFT:FROMPOINTSTO PATHS
That s where our new ARC algorithm comes in a ground-up redesign that rethinks wa collection planning through the lens of graph optimisation
In contrast to point-to-point planning graph-based optimisation models how streets ( and junctions (nodes) connect, focusing on traversing every required street rather than stopping at every bin It’s like solving the classic “draw the envelope without lifting you puzzle but at city scale
ARC doesn t care about how far apart the streets are in miles; it cares about how they r connected It calculates optimal routes by understanding how to traverse linked street efficiently, creating routes that are both computationally effective and operationally in for collection teams
SECTION 4
BEYONDTHEBASICS
Of course, this is just a high-level look Under the hood, ARC is balancing a host of additional constraints vehicle capacity, transfer station visits, time windows, and more But at its core ARC represents a necessary shift in thinking: from outdated one-size-fits-all optimisation tools to systems built specifically for the real-world challenges of waste collection
By blending our experience in delivery logistics with innovative new approaches to routing, we ’ re proud to be developing tools that don’t just fit the future they’re built for it
If you want to learn more about how our algorithms can enhance your operations in the Waste Sector and beyond, see our latest supplement with Motor Transport here
