delivery route types how to optimize

Each delivery run has its own flavour depending on the purpose of the run. A very simple fixed delivery run varies significantly from a daily dynamic pick up and drop off run with multiple stops at a depot. The truck type, terrain, schedule, cargo, and stop structure all introduce variations that mean each route needs to be optimized on an individual basis – there is no one size fits all optimization.

Delivery route types

Before we look at how to optimize routes, let’s see how they can be categorized:

By stop structure

Milk run

A milk run is a fixed, recurring route visiting the same stops in the same sequence, typically on a set schedule. This run type is common in manufacturing supply chains and regular replenishment runs.

Point-to-point (P2P)

A run from a single origin to a single destination, common in same-day courier and urgent freight logistics.

Multi-drop

Starts from one origin with multiple delivery stops and no pickups, basically the classic "delivery run."

Pickup and drop-off (PUDO)

PUDO mixes collections and deliveries on the same route. It is significantly more complex to optimize due to load sequencing constraints; however, a dynamic route can respond to changes in conditions (such as a missed delivery) and incorporate an extra stop in an ad hoc way that gains an optimization during the re-delivery.

Hub-and-spoke last mile

Named after the design of a wheel, cargo is consolidated at a depot or micro-fulfilment centre (the hub) before being distributed outward on radial routes (the spokes.)

Cross-dock run

Cargo is transferred between vehicles at a transfer point without storage. It is often time-critical and sequencing-sensitive.

By scheduling type

Fixed/set runs

A pre-planned route with locked stops and sequence. This is a stable route but can become inefficient over time if not reviewed. See an example: StarTrack Courier found runs unchanged for 10 years before re-evaluating them with Adiona.

Dynamic or on-demand route

Stops are added to a route in real time as orders come in. With this method, volumes can fluctuate dramatically especially during peak times. Being able to respond to this fluctuation with dynamic route optimization becomes your superpower.

Pre-planned with ad-hoc additions

A hybrid of dynamic and a milk run, where a base route is planned in advance but exceptions and urgent stops are inserted throughout the day.

By cargo and service level

Time-window delivery

Multiple factors can introduce strict delivery time windows, such as perishable cargo or busy loading docks that need to efficiently receive orders from multiple trucks. This is extremely common in B2B restocking (for example: retail, pharmacy, FMCG.)

Appointment-based delivery

Common for bulky freight and healthcare, the recipient agrees on a specific delivery time, with high consequences if the delivery is missed.

Same-day delivery

Orders are placed and delivered within the same day, requiring dynamic routing and tight ETA accuracy.

Time-critical and emergency runs

Delivering life-critical cargo such as blood, organs, or medical equipment with zero tolerance for latency or re-routing requires dedicated algorithms that prioritise time over cost.

Temperature-controlled runs

Refrigerated or frozen cargo often has dwell-time constraints at each stop. Route sequencing should account for how long or how frequently doors are opened and the extra energy consumption of the vehicle.

Hazmat or restricted cargo runs

Routes for dangerous goods may be constrained by road classifications, tunnel restrictions, or permit zones.

By fleet and vehicle type

Full truckload (FTL) last mile

Routes are serviced by large vehicles completing a single delivery or very few stops, typically to a DC or large retail customer.

Less-than-truckload (LTL) consolidation runs

Multiple consignments from different shippers are consolidated onto one vehicle.

Parcel or courier runs

These runs have a high stop count with a low dwell time per stop. Optimization is heavily focused on sequencing and territory clustering.

Bulky goods runs

These runs have a lower stop count and longer dwell time, often requiring two-person crews to move cargo in and out of the vehicle. Vehicle load sequencing (last-in-first-out) is critical and adds an extra dimension to route planning.

EV-optimized runs

Routes are designed around battery range, charging infrastructure, payload weight, altitude, and ambient temperature, among other EV-specific factors.

By territory structure

Zone-based and territory runs

Each driver is assigned a geographic zone and owns all deliveries within it, creating consistency but can also create inefficiencies at zone boundaries or become out of date as soon as a new customer is added.

Interleaved or pooled territory

Stops are assigned dynamically across zones to maximise vehicle utilisation, especially during volume spikes.

Radial run

Routes emanate from a central point (depot) outward, typically structured to minimise backtracking.

Loop run

The route forms a closed circuit returning to the depot, optimized for total distance rather than individual stop sequence.

Corridor run

A linear route follows a geographic corridor (e.g., a highway strip or main arterial), common in regional or long geographies or territories.

By driver or operational model

Owner-driver run

Where a fleet uses independent contractors with a fixed territory; the route optimization must respect contractor agreements and earnings expectations.

Crowdsourced or gig delivery

Routes are assigned dynamically to casual drivers, with a heavy reliance on real-time optimization.

Multi-shift runs

The same route or territory is covered across two or more driver shifts, requiring handover planning and depot return sequencing.

Driver redeployment run

After completing a primary run early, a driver is reassigned to a secondary run. Requires real-time optimization capability to identify and assign the next best task.

By network complexity

Single depot, single shift

The simplest configuration and the most common starting point for optimization.

Multi-depot

Routes can originate from or return to different depots, with the optimizer selecting the best depot per route or stop.

Micro-fulfilment centre (MFC) routes

Last-mile routes originate from a small urban fulfillment node rather than a traditional depot, enabling faster same-day coverage in dense urban areas.

Relay or linehaul with last mile

Long-haul leg transfers to a local delivery vehicle at a transfer point and the last-mile leg is then optimized separately.

How each delivery route type is optimized

Given we now understand just how unique each route type is, it’s safe to say that optimizing each type requires its own approach. In many cases, logistics businesses mix route types, network types, driver types, and vehicle types in one big, complicated mix.

Many routing tools can’t handle this complexity and apply a one-size-fits-all approach, forcing route planners to manually optimize and constantly tweak routes until they’re ‘good enough’.

Let’s take a look at the factors that get considered in each route type when optimizing the runs.

Pickup and Drop-Off (PUDO) Routes

Unlike a pure delivery run where every stop is a drop, PUDO routes require a vehicle to collect items from some locations and deliver them to others, often within the same shift and sometimes within tight time windows.

The challenge is created by the fluctuation in the vehicle’s capacity; a driver can't deliver something they haven't yet picked up, which means the optimization engine has to respect a web of precedence constraints — stop B must come after stop A, stop D after stop C, and so on.

This gets particularly complicated when you have multiple pickup-delivery pairs interleaved across the same route. A naive routing engine will struggle here, defaulting to geographically close clusters that inadvertently violate the pickup-before-delivery ordering. A good optimization engine handles PUDO with a dedicated algorithm built specifically for this constraint.

StarTrack Courier — Australia Post's same-day courier arm — is a strong example of a PUDO-heavy operation. Their fleet handles everything from e-commerce returns to medical specimens, all with tight sequencing requirements.

Milk runs

Milk runs are often seen as "set and forget" from a planning perspective. The route was optimized once, years ago, and has been running on autopilot ever since. This is exactly the thinking that quietly erodes fleet efficiency over time.

The opportunity in milk run optimization is in ongoing route reviews. Routing teams at StarTrack Courier discovered exactly this when they started running existing routes through Adiona's platform. The results consistently revealed savings that had been invisible under the old approach.

For milk run optimization, the key inputs are historical volume data, realistic dwell times at each stop, and current traffic patterns.

High-density metropolitan runs vs. low-density suburban and rural runs

These two route types require fundamentally different optimization strategies:

High-density metropolitan routes are dominated by dwell time and traffic, not distance.

Low-density suburban and rural routes flip this equation. Here, drive time between stops dominates the shift, and the cost of a suboptimal sequence is measured in kilometres rather than minutes of parking.

The practical implication for fleet managers is that a single optimization approach doesn't work well across both contexts.

Maintenance and service vehicle runs

Maintenance vehicles often return to depot multiple times throughout a day to collect parts, drop off recovered equipment, or re-stock before the next job.

This creates a multi-leg routing problem. Each "trip" from the depot is effectively a mini-route, and the sequencing of jobs across trips has to account for parts availability, job duration uncertainty, and the cost of each return journey.

Technician skill sets add another layer of complexity. Not every technician can complete every job, so routing has to respect job-to-technician matching alongside geography and timing.

Electric vehicle routes

EV fleet routing is a category that is growing rapidly, and it introduces constraints that conventional routing engines weren't designed to handle.

For fleets transitioning from ICE vehicles to EVs, there's an additional planning challenge: which routes are suitable for which EV models? Running these scenarios against real data before committing to a vehicle purchase is where simulation tools become invaluable.

Refrigerated and temperature-controlled routes

Cold chain delivery adds another factor to account for – keeping the cargo at the right temperature. The optimization implication is that it's not just about the most efficient sequence of stops. It's about the sequence that minimizes how many times the truck needs to be opened up.

Third-party logistics fleets vs. dedicated fleets

The operational dynamics of a 3PL fleet and a dedicated fleet are quite different; a dedicated fleet operates for a single customer, while a 3PL fleet carries multiple customers' freight simultaneously.

Hub-and-spoke networks

Hub-and-spoke is a network design model rather than a single route type, but it has significant implications for last-mile route planning. The relevant challenge is at the spoke end. How do you design and optimize the delivery routes that radiate out from each hub to serve the surrounding catchment?

Return to depot vs. finish at home

The question of where a driver's day ends is more consequential for route optimization than it might first appear.

In a return-to-depot model, the driver finishes their shift back at the starting location. In a finish-at-home model, the driver finishes their last delivery and goes directly home, opening up genuinely different optimization possibilities.

Daily dynamic routing vs. fixed routing

This is perhaps the most fundamental choice in route planning strategy. Fixed routing assigns specific customers to specific routes and drivers on a predictable schedule. Dynamic routing generates route plans every day based on that day's actual orders.

Parcel delivery routes

High-volume parcel delivery is one of the most studied last-mile problems in logistics, and for good reason. The defining characteristic of parcel routes is volume. Capacity utilization matters here too.

One side of the road at a time

For route types like waste collection, the routing must complete one side of a street at a time. This is a variant of the classic "Route Inspection Problem" in route planning.

Big and bulky deliveries with multi-person crews

Furniture, white goods, and other oversized items introduce crew-level constraints that standard parcel routing ignores entirely. The first is simply time.

Micro-fulfilment vs. large depot runs

Where freight starts its last-mile journey has a significant bearing on how the route should be planned. Large depot runs originate from a regional distribution centre, while micro-fulfilment relocates inventory much closer to the point of demand.

Real constraints require real optimization

Reading through these route types, a pattern emerges. Every single one of them has constraints that a generic routing approach handles poorly.

Good last-mile optimization isn't about applying a single algorithm to every problem. It's about having an optimization platform flexible enough to model the specific constraints of each route type.