Parameters of our algorithms
– Mission formats (collection/intervention, delivery from a depot, pick-up and delivery with or without return trip)
– Date and time of the operation / time slot of the operation
– Availability time windows (for example: 9am-12pm then 2pm-5pm)
– Operation duration
– Operation can be shared or not
– Adress / operation geographical coordinates
– Number, dimensions and weights of transported goods
– Type of transported goods if necessary
– Operation type
– Business priority of the operation
– Type of vehicle (car, motorcycle, scooter, bicycle, pedestrian, truck)
– Carrying capacity
– Weight limit
– Container dimensions
– Costs per kilometre
– User costs
– Working hours and/or the maximum duration of a working day
– Possible break times
– Breaks duration
– Working time legal constraints
– Stakeholder skills
– Starting and finishing points of the tour
– Mandatory return to a given point at a given frequency (e.g. electric vehicle recharge or biological sampling depot)
– Use of predictive traffic in the case of static optimization
– Use of real-time traffic for dynamic use
Intelligent Appointment Taking Features
INTEGRATION OF EXISTING PLANNING
Citodi takes into account all the appointments already scheduled.
Thanks to the machine learning, Citodi algorithms are able to predict how to position your appointments taking into account those to come.
ASSISTANCE IN MAKING APPOINTMENTS
Citodi suggests the best meeting slots (those that best fit into existing tours) in order to maximize your number of interventions per tour.
DRIVER COMPETENCY MANAGEMENT
The notion of your stakeholders’ skills is included in the calculations (if they do not all have the same skills).
Static tour optimization features
PLANNING AND SIMULATION MODE
Plan your operations before launching your fleet in the field, or simulate past or future days by changing settings to improve your organization.
CHOICE OF YOUR OPTIMIZATION KPI
Citodi allows you to choose the criteria to be optimized beforehand (distance covered, tour duration, fleet size, CO2 emissions, OPEX costs).
Real-time optimization and automatic dispatch features
SCHEDULE CHANGE FOR AN OPERATION
The algorithm integrates this modification, dynamically adapts the tour to this new information.
CHANGE OF ADDRESS FOR AN OPERATION
The algorithm integrates this modification by recalculating the distances involved and adapts the tour dynamically.
ADDITION OF NEW MISSIONS
The algorithm automatically and optimally inserts the new mission into existing tours while they are running?
If a driver is late, the algorithm detects him and the tour is automatically readjusted.
The algorithm automatically dispatches the missions that were assigned to the cancelled tour to the other drivers.
The algorithm removes the mission from the tour in question and dynamically adapts it to exploit the time freed by the cancellation.
If a driver breaks down with packages to be delivered, the algorithm automatically considers him as a pickup point for other drivers who are redirected to the broken down vehicle to pick up the packages and deliver them on time.
MANAGEMENT OF FLEET REPOSITIONING
In contexts where orders arrive on the fly, the algorithm takes advantage of idle times to reposition the fleet in anticipation of future demands.
In this very dynamic mode, drivers only know their next mission/action (go to, wait).