How does a route optimisation engine work?

Organising a sales route requires taking into account numerous factors: the customers to visit, their opening hours, appointments that have already been confirmed, the distances to travel, or even the time available during the day.

When this information multiplies, manually building a coherent schedule quickly becomes time-consuming.

This is precisely the role of a route optimisation engine: to analyse the establishments to visit and the sales rep's constraints to propose an organisation of travel that is as efficient as possible.

But how does this engine actually work? What information does it rely on? And why might it sometimes suggest a visit order that differs from the one a sales rep would have chosen spontaneously?

What is a route optimisation engine?

A route optimisation engine is a system that allows an organisation of visits to be calculated based on a list of destinations and planning constraints.

Its objective is to limit travel while making the best use of available time to carry out sales visits.

Take the example of a Field Sales Representative who needs to visit twelve stores spread across several municipalities.

To build their route manually, they must check addresses, compare distances, verify opening hours, and distribute the visits throughout their calendar.

An optimisation engine performs these checks and searches for a coherent organisation based on the entered information.

It therefore does not simply indicate the path between two addresses: it helps determine which establishments to visit, in what order, and at what time.

What is the difference between route calculation and route optimisation?

A route calculation tool primarily answers the question: "How do I get from point A to point B?"

A route optimisation engine answers a broader question: "How do I organise multiple visits over one or more days, taking into account travel and the constraints of each appointment?"

This difference is essential for field sales reps.

The shortest journey is not always the most suitable when opening hours, visit durations, or already scheduled appointments must be respected.

This issue is notably linked to the Travelling Salesperson Problem (TSP), which involves finding the shortest circuit between several destinations.

However, sales route engines go further: they incorporate additional constraints to build achievable schedules.

What information is needed to optimise a route?

To propose a relevant route, the engine needs several pieces of information.

The quality of the result depends directly on the data used: an incorrect address, a missing opening time, or a poorly estimated visit duration can alter the proposed organisation.

Establishments to visit

The first step is to determine the customers or prospects to include in the route.

This may be an entire sales portfolio or a more targeted selection: stores in a specific geographical area, customers not visited recently, prospects to meet, or priority establishments.

This selection is important because the engine organises visits based on the establishments provided to it.

It does not replace commercial decisions regarding which customers to prioritise.

Addresses and travel times

The engine needs to know the location of establishments to evaluate travel between different visits.

The mode of transport can also influence journey times. Travelling by car does not take the same amount of time as walking.

This information makes it possible to seek a coherent order of visits and avoid unnecessary back-and-forth travel as much as possible.

Schedules and visit durations

A route must also respect the time available to the sales rep.

Several parameters may come into play:

• Working hours, to define the start and end of the day.

• Visit duration, to allocate sufficient time for each appointment.

• Establishment opening hours, to avoid scheduling a visit when the customer is closed.

• Lunch breaks, when they must be taken into account.

• The maximum number of daily visits, to cap the number of appointments.

• Working days, to distribute visits across available days.

These constraints make it possible to transition from a simple geographical itinerary to a genuine sales schedule.

Already confirmed appointments

A sales rep does not always plan their route from an empty calendar.

They may already have several confirmed appointments to which they wish to add complementary visits.

The engine must then build its organisation around these commitments.

For example, if a sales rep has three fixed appointments in their day, optimisation can look for other establishments to visit between these appointments or during remaining available slots.

How does an optimisation engine calculate a route?

The operation of an optimisation engine can be summarised in four steps.

1. It gathers establishments and constraints

The engine begins by factoring in the selected points of sale, their addresses, and planning parameters.

It identifies available days, working hours, visit durations, and any appointments already set.

This information serves as the starting point for the calculation.

2. It searches for visits that can be organised

The engine then assesses planning possibilities.

In particular, it must determine whether a visit can be carried out within the available time, taking into account necessary travel and other appointments.

Let's take an example.

A sales rep finishes a visit at 11:00 am. They have a confirmed appointment at 12:30 pm in another town.

Even if a store is located in the immediate vicinity of their first customer, it is not necessarily possible to add it to the schedule.

Its visit duration and the time required to reach the next appointment still have to be factored in.

The engine must therefore look for combinations compatible with the organisation of the day.

3. It compares different possible organisations

Multiple routes can meet the same constraints.

The engine then seeks an organisation that allows the maximum number of visits to be carried out while limiting the miles travelled.

This search involves trade-offs.

Should an additional establishment be added at the cost of a detour? Is it better to schedule two nearby stores on the same day? Can a visit be moved to another day to reduce travel?

The engine reviews different options to construct a proposed schedule.

It does not necessarily guarantee finding the single best mathematical solution, but it searches for an efficient organisation in light of the available parameters.

4. It proposes a visit schedule

Once the calculation is complete, the sales rep has a proposed route.

They can visualise the organised visits in their calendar and check their geographical distribution on the map.

The schedule provides a working basis: it enables the sales rep to prepare their journeys and ensure that the proposed organisation aligns with their priorities.

Why use a calculation engine rather than a fixed-rules system?

To organise a route, an initial approach is to apply simple rules.

For example, grouping stores located in the same area, limiting travel to a certain distance, or distributing a defined number of visits per day.

These rules can help build a schedule, but they have a limitation: they do not always find the best compromise when multiple constraints combine.

Imagine a sales rep who needs to visit eight establishments.

Some are geographically close, but have different opening hours. Others are further away, but can be visited between two already confirmed appointments.

An organisation based solely on geographical proximity risks missing out on worthwhile opportunities.

A more advanced calculation engine can examine different distributions and visit orders to find a coherent solution.

This is the value of a solver—in other words, a system responsible for finding a solution to a problem with multiple constraints.

For the sales rep, the process remains simple: they select their establishments, enter their parameters, and run the optimisation.

It is the engine that handles searching for an appropriate organisation.

How does Sidely optimise sales routes?

Sidely integrates a route optimisation engine directly into its field CRM.

It allows sales reps to build their schedules from the establishments registered in their portfolio, without having to manually organise every journey.

1. Select points of sale from the map or calendar

Optimisation can be launched from two places.

From the map, the sales rep can select a geographical area using the selection tool and identify the establishments to include in their route.

This approach is particularly useful for organising a prospecting day or covering a specific geographical area.

From the calendar, they can launch optimisation based on their existing schedule.

This second method is especially suited to sales reps who already have several confirmed appointments and wish to fill out their days with further visits.

2. Define route parameters

Once the establishments are selected, the sales rep enters the parameters required for the calculation.

With Sidely, they can notably define:

• Their starting address, retrieved from their profile.

• The maximum number of events per day.

• Their working hours, set by default from 8:00 am to 5:00 pm.

• An optional lunch break.

• The planned duration for each visit, based on a default duration.

• The number of working days and excluded days.

• The period over which they wish to organise their visits.

• The opening hours of points of sale, when recorded in the CRM.

These parameters help tailor planning to the sales rep's working habits and the characteristics of their portfolio.

3. Organise visits around existing appointments

One of the strengths of the Sidely optimiser is its ability to take already scheduled appointments into account.

These events are treated as priority commitments: the engine organises complementary visits around them.

Take the example of a sales rep with three confirmed appointments during the day.

They wish to make the most of their travel to meet other customers, but do not want to alter appointments already made.

By launching the optimisation from their calendar, they can search for an organisation of supplementary visits that factors in their commitments.

This approach allows an existing schedule to be completed rather than rebuilding the entire day manually.

4. Generate and visualise the route

Based on the selected establishments and defined constraints, Sidely calculates a proposed route.

The aim is to maximise feasible visits while limiting travel.

The sales rep can then review the proposed organisation in their calendar and visualise their journeys on the map.

They thus have an overall view of their visits and their geographical distribution.

Discover route optimisation with Sidely.

Why doesn't an optimiser always select the closest store?

An automatically calculated route can sometimes seem surprising.

Imagine a sales rep visiting a store and passing close to a second establishment, located just a few hundred metres away.

Yet this second store does not appear in their schedule for the day.

Why?

Because geographical proximity is only one criterion among others.

The engine may skip this store if its visit duration exceeds the available time, if its opening hours do not match the time slot, or if a confirmed appointment prevents it from being fitted into the day.

It may also be impossible to include all selected establishments within the defined period.

The role of the optimiser is not to systematically schedule the closest stores, but to seek an organisation consistent with all entered constraints.

What should you do when a route does not meet expectations?

When an establishment is not included in the route, it is helpful to check the parameters used.

Are the working hours too restrictive? Is the average visit duration appropriate? Is the maximum number of daily appointments high enough?

It is also important to verify the addresses and opening hours recorded in the CRM.

On Sidely, the sales rep can adjust certain parameters or manually alter their schedule, then rerun the optimisation as their organisation evolves.

A surprising result does not necessarily mean the calculation is incorrect: it can also highlight constraints incompatible with the desired visits.

Can a route be re-optimised after a schedule change?

A sales route is never completely set in stone.

A customer may request a time change, an appointment may be cancelled, or a priority visit may be added at the last minute.

In these situations, it may be necessary to reconsider the organisation of travel.

With Sidely, a sales rep can view their route from the calendar, adjust their visits, and rerun the optimisation to obtain a new proposed itinerary.

For example, if an appointment is added in an area already covered, it may be worthwhile reviewing the order of other visits to limit additional travel.

Optimisation thus becomes a tool for planning assistance, as well as for adapting when days evolve.

Key takeaways

A route optimisation engine does not merely calculate the shortest journey between several establishments.

It analyses destinations to visit, travel times, working hours, visit durations, and existing appointments to find a coherent organisation.

Its value lies in its ability to process multiple constraints simultaneously, which would otherwise be long and complex to compare manually.

For field sales reps, the goal is simple: spend less time organising their travel and have a schedule that allows them to dedicate more time to their customers.

By integrating optimisation directly into the CRM, Sidely makes it easy to move from selecting establishments to the concrete preparation of days in the field.