{"id":991,"date":"2026-08-25T10:33:55","date_gmt":"2026-08-25T10:33:55","guid":{"rendered":"https:\/\/trendroute.ai\/?p=991"},"modified":"2026-08-25T10:50:12","modified_gmt":"2026-08-25T10:50:12","slug":"the-trade-off-between-minimising-route-distance-and-reducing-the-risk-of-delayed-parcels","status":"publish","type":"post","link":"https:\/\/trendroute.ai\/en\/uncategorized\/the-trade-off-between-minimising-route-distance-and-reducing-the-risk-of-delayed-parcels\/","title":{"rendered":"The trade-off between minimising route distance and reducing the risk of delayed parcels!"},"content":{"rendered":"
\n

<\/p>\n

\n
<\/div>\n
<\/div>\n
ROUTE OPTIMISATION \u00b7 OPERATIONAL RESILIENCE<\/div>\n

The hidden trade-off between route efficiency and operational resilience<\/h1>\n

Minimising distance and travel time matters. But for long delivery routes,
\nanother question can become just as important:
\nwhich deliveries should we complete first?<\/strong><\/p>\n<\/div>\n

<\/p>\n

\n
01 \u2014 THE TRADITIONAL OPTIMISATION PROBLEM<\/div>\n

Most optimisation starts with three questions<\/h2>\n

In route optimisation, we usually ask questions such as:<\/p>\n

\n
\n
01<\/div>\n
How can we minimise total distance?<\/div>\n<\/div>\n
\n
02<\/div>\n
How can we minimise travel time?<\/div>\n<\/div>\n
\n
03<\/div>\n
How can we complete the route as efficiently as possible?<\/div>\n<\/div>\n<\/div>\n

These are fundamental objectives in solving TSP, VRP and other multi-stop
\nrouting problems.<\/p>\n

But in real logistics operations, there is another consideration that becomes
\nincreasingly important as routes become longer:<\/p>\n

\n
THE OTHER QUESTION<\/div>\n
Which deliveries should we complete first?<\/div>\n<\/div>\n

That is particularly important for long last-mile routes, where a small
\nrouting decision early in the shift can determine how many customers are
\naffected several hours later.<\/p>\n

For a route starting from a depot or terminal, it may feel natural to serve
\nnearby stops first and progressively move towards more distant areas.<\/p>\n

Interestingly, that sequence is not always the mathematically shortest or
\nfastest route.<\/p>\n

In some cases, an optimiser can reduce total distance by travelling towards
\na more distant cluster early, returning through another area later, or
\nfollowing a sequence that does not simply progress from the closest deliveries
\nto the farthest ones.<\/p>\n

Mathematically, that may be the better solution.<\/div>\n

Operationally, however, there is another side to the problem.<\/p>\n<\/div>\n

<\/p>\n

\n
\n

\"What<\/p>\n<\/div>\n

02 \u2014 REAL-WORLD UNCERTAINTY<\/div>\n

What happens when the day does not go according to plan?<\/h2>\n

A route plan assumes that the operation will progress approximately as expected.
\nBut real delivery operations contain uncertainty.<\/p>\n

\n
\ud83d\udea6 A serious traffic jam may appear.<\/div>\n
\u26d4 A road may suddenly close.<\/div>\n
\ud83d\ude9a A vehicle may have a problem.<\/div>\n
\u26a0\ufe0f An accident may delay the driver.<\/div>\n
\ud83c\udf27\ufe0f Weather conditions may deteriorate.<\/div>\n
\u23f1\ufe0f Service times may become longer than expected.<\/div>\n<\/div>\n

When something unexpected happens late in a long route, the important question
\nis not only:<\/p>\n

\n
TRADITIONAL KPI<\/div>\n
How many kilometres have we driven?<\/div>\n<\/div>\n
\n
OPERATIONAL EXPOSURE<\/div>\n
How many customer orders are still waiting to be delivered?<\/div>\n<\/div>\n

This creates an interesting trade-off in route optimisation.<\/p>\n

A route that prioritises nearby stops earlier may be slightly longer overall,
\nbut it can complete a larger number of deliveries earlier in the shift.<\/p>\n

If a major disruption then occurs, fewer customer orders remain exposed to
\nthe delay.<\/p>\n<\/div>\n

<\/p>\n

\n
03 \u2014 A SIMPLE EXAMPLE<\/div>\n

60 stops. Two valid routes. Two very different outcomes.<\/h2>\n

\nConsider an illustrative route containing 60 delivery stops.
\n<\/strong><\/p>\n
Most of the stops are relatively close to the terminal, while a smaller group
\nis located farther away.<\/div>\n<\/div>\n
\n

<\/p>\n

\n
<\/div>\n
\n
ROUTE A<\/div>\n

Minimum-distance solution<\/h3>\n

The optimiser finds that the shortest overall sequence is to move towards one
\nof the more distant areas relatively early, connect several geographical clusters
\nefficiently, and eventually return through some of the areas closer to the terminal.<\/p>\n

\n
Total distance<\/span>
\n72 km<\/strong><\/div>\n
Delivered halfway<\/span>
\n32 \/ 60<\/strong><\/div>\n<\/div>\n<\/div>\n<\/div>\n

<\/p>\n

\n
<\/div>\n
\n
ROUTE B<\/div>\n

Progressive service solution<\/h3>\n

The second route deliberately gives more preference to serving nearby and dense
\ngroups of deliveries first, before gradually progressing towards the more distant stops.<\/p>\n

\n
Total distance<\/span>
\n74 km<\/strong><\/div>\n
Delivered halfway<\/span>
\n42 \/ 60<\/strong><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

<\/p>\n

\n
Deliveries completed after half of the shift<\/div>\n
\n
Route A<\/strong>
\n32 of 60<\/div>\n
\n
<\/div>\n<\/div>\n<\/div>\n
\n
Route B<\/strong>
\n42 of 60<\/div>\n
\n
<\/div>\n<\/div>\n<\/div>\n<\/div>\n

<\/p>\n

\n
NOW INTRODUCE A DISRUPTION<\/div>\n
A major accident causes significant traffic disruption during the second half
\nof the shift.<\/div>\n
\n
\n
ROUTE A<\/div>\n
28<\/div>\n
orders still waiting<\/div>\n<\/div>\n
\n
ROUTE B<\/div>\n
18<\/div>\n
orders still waiting<\/div>\n<\/div>\n<\/div>\n<\/div>\n

<\/p>\n

\n
35%<\/div>\n
\n

fewer parcels remain exposed to the disruption in this illustrative scenario.<\/p>\n

28 pending orders \u2192 18 pending orders<\/div>\n<\/div>\n<\/div>\n

The second route travelled slightly farther, but significantly fewer customers
\nare exposed to the disruption.<\/p>\n

The numbers above are only an illustrative example, but the operational
\ntrade-off is very real.<\/p>\n<\/div>\n

<\/p>\n

\n
\n
\"Then
Then how to solve this multi-objective complex problem efficiently?<\/figcaption><\/figure>\n<\/div>\n
04 \u2014 MULTI-OBJECTIVE OPTIMISATION<\/div>\n

So what is actually the \u201cbest\u201d route?<\/h2>\n

This is why defining the ”best route” is more difficult than simply minimising
\ndistance or duration.<\/p>\n

The optimiser may need to balance two objectives:<\/p>\n

\n
\n
01<\/div>\n
\n
EFFICIENCY<\/div>\n
Minimise total route distance and duration<\/div>\n<\/div>\n<\/div>\n
BALANCED AGAINST<\/div>\n
\n
02<\/div>\n
\n
ROBUSTNESS<\/div>\n
Maximise the number of orders completed earlier in the route<\/div>\n<\/div>\n<\/div>\n<\/div>\n

These objectives can sometimes conflict.<\/p>\n

Giving too much weight to early completion could create inefficient routes with
\nunnecessary additional kilometres.<\/p>\n

Giving no weight to it at all may create mathematically excellent routes that
\nexpose a larger number of deliveries to late-shift operational uncertainty.<\/p>\n

The right answer therefore does not have to be one extreme or the other.<\/div>\n<\/div>\n

<\/p>\n

\n
05 \u2014 WHY ROUTE LENGTH MATTERS<\/div>\n

The longer the route, the more uncertainty matters<\/h2>\n

For a route with 10 or 15 stops, this consideration may have relatively little impact.<\/p>\n

But the situation changes when a courier is expected to visit 50, 60, 80 or more
\nstops during one shift.<\/p>\n

\n


\n10 stops
\n<\/span><\/p>\n


\n15 stops
\n<\/span><\/p>\n


\n50 stops
\n<\/span><\/p>\n


\n60 stops
\n<\/span><\/p>\n


\n80+ stops
\n<\/span><\/p>\n<\/div>\n

\n
\n
DISRUPTION AFTER<\/div>\n
1 hour<\/div>\n<\/div>\n
\n
DISRUPTION AFTER<\/div>\n
6 hours<\/div>\n<\/div>\n<\/div>\n

The longer the route, the greater the exposure to uncertainty throughout the day.<\/p>\n<\/div>\n

<\/p>\n

\n
06 \u2014 THE CONTROLLED TRADE-OFF<\/div>\n

Then how do we solve this efficiently?<\/h2>\n
\n
THE KEY PRINCIPLE<\/div>\n
Allow a small increase in theoretical route cost when it creates a meaningful
\nimprovement in operational robustness.<\/div>\n<\/div>\n

The goal is not to enforce a rigid rule saying that the closest stop must always
\nbe visited first or that the farthest stop must always be last.<\/p>\n

That would itself produce poor solutions in many routing problems.<\/p>\n

Instead, the optimisation process can consider the operational benefit of
\nprogressively completing deliveries while still protecting overall route efficiency.<\/p>\n<\/div>\n

<\/p>\n

\n
07 \u2014 TRENDROUTE.AI<\/div>\n

How we reflect this philosophy in our optimisation engine<\/h2>\n

The goal is not to replace mathematical optimisation with a collection of rigid
\noperational rules.<\/p>\n

Instead, this philosophy is reflected in several other aspects of TrendRoute.ai:<\/p>\n

\n
\n
\u25ce<\/div>\n
Route-shape quality<\/div>\n
Looking beyond distance alone to evaluate the quality of the route structure.<\/div>\n<\/div>\n
\n
\u219d<\/div>\n
Less zig-zagging<\/div>\n
Reducing unnecessary backtracking and repeated movement between areas.<\/div>\n<\/div>\n
\n
\u2192<\/div>\n
Geographical progression<\/div>\n
Considering how a route progresses naturally through delivery areas.<\/div>\n<\/div>\n
\n
\u25f7<\/div>\n
ETA prediction<\/div>\n
Combining geographical and operational intelligence around the mathematical optimiser.<\/div>\n<\/div>\n<\/div>\n<\/div>\n

<\/p>\n

\n
<\/div>\n
THE BROADER PRINCIPLE<\/div>\n
A strong route optimisation engine should not optimise only for the
\nbest route on paper.<\/span>
\nIt should optimise for a route that performs well when the real world does not
\nbehave exactly as planned.<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

ROUTE OPTIMISATION \u00b7 OPERATIONAL RESILIENCE The hidden trade-off between route efficiency and operational resilience Minimising distance and travel time matters. But for long delivery routes, another question can become just as important: which deliveries should we complete first? 01 \u2014 THE TRADITIONAL OPTIMISATION PROBLEM Most optimisation starts with three questions In route optimisation, we usually […]<\/p>\n","protected":false},"author":1,"featured_media":992,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-991","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"\nThe trade-off between minimising route distance and reducing the risk of delayed parcels! - TrendRoute<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/trendroute.ai\/en\/uncategorized\/the-trade-off-between-minimising-route-distance-and-reducing-the-risk-of-delayed-parcels\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The trade-off between minimising route distance and reducing the risk of delayed parcels! - TrendRoute\" \/>\n<meta property=\"og:description\" content=\"ROUTE OPTIMISATION \u00b7 OPERATIONAL RESILIENCE The hidden trade-off between route efficiency and operational resilience Minimising distance and travel time matters. 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