```json
{
    "title": "Legacy – 02 Report Route optimization theory vs practice EN",
    "url": "https://kardinal.ai/report-route-optimization-when-theory-meets-reality/",
    "datePublished": "2021-10-07",
    "dateModified": "2026-08-24",
    "language": "en-GB",
    "description": "Route optimization: when theory meets reality. Route optimization brings considerable benefits to planners.",
    "author": "Kardinal",
    "publisher": "Kardinal"
}
```

# Legacy – 02 Report Route optimization theory vs practice EN

Report – Route optimization: when theory meets reality

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Report · Route optimization
 Route optimization: when theory meets reality
 Optimization algorithms can compute tours that are close to optimal. Yet very few companies make full use of them. This report examines the gap between the mathematical theory of the problem and the operational reality of the field, and how to close it.

 [↓ Download the report (PDF)](https://kardinal.ai/wp-content/uploads/2026/07/Report-Route-optimization-when-theory-meets-reality%E2%80%8B.pdf)
 [Read the preview →](#guide)

 Why this report

 Why so few companies truly optimize their routes
 Building tours quickly becomes a headache for planners, and algorithms have proven themselves on that complexity. But when data is missing or changing, the optimization computed upstream is already unrealistic by the time drivers hit the road. Planners end up setting the tool aside and returning to their manual practices.

 💡
 **The goal:** identify the four practical obstacles that make optimization projects fail, and the concrete mechanisms that reduce them.

What you will find inside

 01The complexity of the problemWhy route planning is a mathematical problem of dizzying complexity.

 02The promise of algorithmsUp to 30% less on operating costs and CO2 emissions, hundreds of planning hours saved each year.

 03The four practical obstaclesIncomplete data, misleading averages, field hazards, business constraints too specific to model.

 04The Kardinal answerContinuous optimization, Machine Learning on times actually observed, a proprietary solver.

 The four practical obstacles
 So what's wrong with route optimization solutions?
 01Incomplete dataCustomer preferences or the specifics of access to a site do not exist in the database: only the operational team knows them.

 02Data far from the fieldA delivery time averaged at 5 minutes when it actually varies from 3 to 20 cascades into delays.

 03Changing environmentTraffic jams, missing customers, parking difficulties: the last mile concentrates the hazards no calculation can foresee.

 04Very specific constraints"This piece of furniture requires two deliverymen": a business rule generic solvers rarely model.

**What they have in common:** a tour calculated without taking these realities into account is not simply less good, it is often plainly not achievable in the field.

 Inside the document
 A few pages from the report

 [↓ Download the full report](https://kardinal.ai/wp-content/uploads/2026/07/Report-Route-optimization-when-theory-meets-reality%E2%80%8B.pdf)

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