Phase 1. Import Scenario and Run the Experiment
We shall start with obtaining the data that will serve as the
basis for our experiment. You may either provide your own data or you
can instantly import the scenario.
Either way, you should have a supply
chain scenario containing
- Distribution center(s)
located in the optimal places
- Properly configured sourcing and paths
- Vehicle type(s)
Download and import the
- Download the scenario.
No internet connection is required, since the scenario is supplied with
the downloaded scenario. The GIS map will appear showing the content of
the imported scenario.
- Explore the connections by clicking the Show filters
button to open the filter options, and then clicking
Show connections button.
The imported scenario contains a supply chain with 32 Lidl
stores and one distribution center on the territory of
Let us observe the data the experiment will work with.
Primarily we are interested in the Paths
and the Sourcing
tables, and Vehicle Types.
Observe the data of the
- Navigate to the Paths
table. It contains 1
record, allowing all connections between the objects of our supply
chain. If you need to edit this record, make sure that the resulting
record(s) (with Fixed
delivery cost or Distance
based cost calculation formulas only) will allow a vehicle
to go from:
- Site to Customer
- Customer to Customer
- Customer to Site
- Now switch to the Sourcing
table, which defines the sourcing of products within this supply
chain. The Sources
column contains the Gabrovo DC
which will be servicing customers that are defined by the Lidl Stores
in the Delivery Destination
open the Vehicle Types table. You will see the vehicle that we will be
using. It is a standard 40m3 truck, that is specified in the Paths table, and in the experiment parameters.
Configure parameters of
the CTO experiment
- Navigate to the experiments
section and click CTO experiment.
You will be taken to the experiment's view
with its settings, where:
- Number of shipments
- the number of shipments you would
like to send within the Experiment
duration period. We will have 8 shipments within
2 months, i.e. 1 shipment a week.
- Vehicle types
- the type of vehicle delivering the products. The selected
vehicle type(s) must correspond to the vehicles defined in the Paths table.
- Travel segment limit
- the maximum remoteness of the network objects from each other in the
specified Distance unit.
- Returning segment limit
- the maximum remoteness of the last customer of the route from the DC
that the vehicle set off.
- Min vehicle load ratio
for direct shipments
- specifies the minimum amount of products a vehicle can carry as a
direct shipment (shipment made to one customer only). In our case, a
customer must order 28 m3 of products or
more to have a direct shipment. The value is calculated as vehicle capacity (40m3) multiplied by the parameter's value (0.7).
Run in the toolbar of the CTO
The results will be available in the Result
sub-item of the CTO experiment
Let us analyze the received
the received results
- Navigate to the Optimization
tab below the experiment view. The results contain all the possible
routes with the lowest expenses. There are 8 sets of routes, one for
each shipment. Each set includes only the routes that let a vehicle
every customer by the specified vehicle.
- Expand the borders
of the Destinations column
to observe the order in which customers are visited within each route.
- Filter the results per
shipment. Type 01-07
(the date of the first
shipment) into the filter
field below the Aggregation Period
Scenario duration period is 2 months. The Number
of shipments parameter is set to 8 shipments, which
results in weekly shipments, hence the date of the first shipment in
- Now click the first record.
GIS map will appear, showing a route comprising 8
customers: Svishtov, Ruse, Silistra, Dobrich, Varna, Shumen,
experiment considers actual roads. The GIS
map, however, depicts the results with straight lines
- In the same way you
may observe the rest of the offered routes on the GIS map.
- Now switch to the Solution
Type column. It offers 2 types of solution for routes of
our supply chain:
- optimal solution in terms of costs and the provided
- the shipment made directly to a customer
whose weekly demand exceeds the capacity of our vehicle
(80m3). The rest
customer's demand is used by the experiment to design the
- Navigate to the Generated Paths tab of the
experiment results to observe transportation cost per route.
The table contains dozens of records, showing routes per each shipment.
- Group the records by
shipment by clicking the Shipment
column title and dragging it to the Grouping
The first line contains summarized data on all
the routes from the first shipment, lines 2-7 show
data per each route
- Check the Solution Type
you can see, we have 2 direct shipments and 4 optimal routes within
the first period.
- Calculate the sum of all the routes. In our case the total
cost of the offered routes constitutes $935.526.
- For more details you can
navigate to the Generated Path Segments
tab, which contains details on each segment of every route.
That's it we
have completed the first phase of this tutorial. We imported the
scenario, created cost-efficient routes, and analyzed the results. In
the next phase we will try to decrease the cost of running the
supply chain by adjusting the experiment parameters.
experiment Tutorial | Phase
2. Adjust Parameters and Compare Results