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TO Capacitated Transportation Optimization with Time Windows

In this example, we will learn how to define time windows (operating hours) for the customers, and create a product delivery network with the customers visited in a certain order, considering customer demand and vehicle capacity.

Problem definition

We consider the existing beer supply chain in Germany with the following data:

  • One distribution center in Berlin
  • 10 customers in the largest cities of Germany
  • Delivery time is limited to the operating hours of the customers
  • Vehicle with a capacity of 30 m3
  • Inbound shipment processing time is 30 min per m3

Demand is aggregated for each week and varies from week to week. Considering the demand variations, we want to have 5 shipments in October and get a set of routes for each of them.

We want each customer to have product delivery time or operating hours. The Time Windows table allows us to define the start and end times of the receiving period.


Find an optimal set of routes to fulfill the demand of all the customers and reduce logistics costs taking into consideration that the maximum distance between objects should not exceed 400 km.


Before running the experiment we must make sure that the Use time windows parameter is enabled in the experiment's settings.

The result of the experiment contains the optimal sets of routes that are obtained for each shipment considering all the defined restrictions.

By default, the results of the experiment are available in the Optimization Results tab. The data for the generated routes, transportation costs, and the covered distance is given in the corresponding tables:

  • Generated Paths — shows details on the routes that were generated by the experiment:
    • Site — the initial point of the route the vehicle sets off from
    • Vehicle Type — the vehicle type that is used to deliver the products on this route
    • Destinations — the customers the vehicle will visit
    • Cost — the cost of transportation between the two points (Site and Destination)
    • Shipment — the period within which the shipment was made
  • Generated Path Segments — shows additional statistics on the segments of the generated routes that were created by the experiment:
    • Distance — the distance between the two points (Site and Destination)
    • Cost — the cost of transportation between the two points (Site and Destination)
    • Arrival time — shows the time when the shipment was delivered
    • Amount — the amount of product(s) the vehicle carried on this segment
  • Skipped Customers — shows details on the customers that were not included in the generated route(s).
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