FAQ

1. How long does it take to optimise a schedule?

Back to Top Route optimisation calculations grow in complexity very quickly according to the number of stops and vehicles included. As a result larger schedules take longer to complete than smaller ones. For a schedule with a small fleet of vehicles and around 100 stops, Optergon will take one to two minutes to produce a solution. For large schedules with 200+ locations there'll be time for a quick cup of coffee before the calculation completes.

2. My fleet consists of different vehicles carrying unusual loads, can Optergon cater for this?

Back to Top Yes. Optergon is designed to be completely flexible when it comes to configuring your vehicles, their capacities and location constraints. If you are transporting giraffes via a system of solar land yachts, we can handle it.

3. Can Optergon cater to companies that operate in multiple cities?

Back to Top Yes. Optergon accounts allow for multiple users who can independently manage their own fleets, schedules and depots. It's best to optimise schedules for each city individually as opposed to throwing all fleets across all locales into one large calculation (lots of smaller optimisations works out cheaper than fewer large optimisations).

4. Can I create schedules that leave my vehicles at different depots?

Back to Top Yes. You can specify a start and end depot for each vehicle in a schedule. The end depot does not have to be the same as the start depot.

5. My company handles thousands of deliveries per day, can Optergon cater to this?

Back to Top Yes. Optergon can handle very large optimisations in one go, but, the larger the problem the larger the burden on our computing resources. To keep your costs down it makes sense to break up your schedules into defined regions (i.e. if you operate in London and Paris, create and optimise schedules for each city individually). Ideally, you would want to keep individual schedules under 250 stops (although we technically have no size limitations and can cater for larger problems).