Contextualización de la empresa ASSUKKAR S.A

When a drone’s primary concern is the priority of the specimens, it does not weigh up the consequences of travelling a far distance or of collecting minimal samples. Similarly when a drone’s objective is to minimise distance or maximise specimen collection, the priority of the specimens may be neglected.

For this reason, a penalty must be issued on the drone whenever a compromise is made. The penalty value takes into account the above mentioned as well as the request points that were not visited as a result of the drone’s decision.

A penalty function is introduced in order to validate the optimisation algorithm. This will allow the simulation results to be compared with other policies. The total penalty value is the sum of the penalties accrued by a drone throughout the simulation. This can be seen in equation 6.9. The penalty equation for priority of request points over distance and number of specimens can be seen in equation 6.6.

Chapter 6. SIMULATION DEVELOPMENT 49 PP = T2 Pn i=1Pi (6.6)

PP = Penalty based on priority.

T = Delivery Time.

n = Number of visited delivery points.

The penalty equation for maximum number of specimens over distance and specimen priority can be seen in equation 6.7.

PS=

T2 Pn

i=1Si

(6.7)

PS = Penalty based on number of specimens.

The penalty equation for minimising the distance between the drone and request point over priority of specimens and number of specimens can be seen in equation 6.8.

PD =

Pn

i=1di

T2 (6.8)

PD = Penalty based on distance.

PT = PP + PD+ PS (6.9)

The penalty values are based heavily on the reward function. A penalty is issued if a possible reward was not obtained. For example, in response to the drone not selecting the shorter travel distance, the penalty equation 6.2 would result in a higher value. This penalty value is then used to gauge the efficiency of the optimisation method.

A flow diagram illustrating the reward based inspection algorithm for the drone delivery system can be seen in figure 6.2.

6.6

DATA COLLECTION AND INSTRUMENTATION

A scenario is compiled where an initial ‘N’ number of requests are defined and allocated a properties matrix. This matrix consists of the coordinates of the location, the number of specimens per location and a priority of the specimens. The coordinates lie within the radius defined on start up. The number of specimens and priority of the specimens is defined between the values of 1 and 10. The priority scale is descending where 10 is high and 1 is low.

The number of drones, ‘K’, is defined and the drone criteria matrix is generated. Each drone in the scenario is given an initial position within the defined radius. The drone’s states are set at zero. The maximum distance the drone can travel is 22.5 km. In order for the battery to never be fully depleted this value is reduced to 22 km. The generated drones are given their initial range of 22 km and an initial payload of 0 %. If the drone is unable to reach the destination based on its range it must locate the nearest charge point which may be the nearest clinic. If the drone has no clinics or charge points within the range it is considered to be out of commission.

Once the drone is assigned its respective request, it must make its way to the location to collect the samples. Upon reaching the location the drones range is considered to be reduced by the distance between the request point and the drone’s initial position. The battery is then replenished as a battery change is scheduled and the range of the drone is now set to its maximum of 22 km or a flight time of just under 30 minutes. However, upon collection of the specimens, the drone’s payload is increased as it has now collected a number of specimens. This affects the next possible route as the drone must complete its delivery before the payload capacity limit is reached.

Once the drone reaches the request point it ‘loads’ up the specimens by adding the number of specimens at the request point to the drone’s payload capacity value. If the drone capacity is verging on maximum with no request points within range containing low numbers of specimens i.e. no possible routes, the drone must travel to its delivery point and offload. When the drone offloads at the delivery point the payload is reset to 0 % and the range is reset to 22 km.

The initial scenario is plotted on a graph with red stars indicating the initial request locations and blue X’s defining the drone’s initial position. The properties matrices are assigned and the requests as well as drones are given a defining value from 1 to N and 1

Chapter 6. SIMULATION DEVELOPMENT 51

to K respectively for reference purposes. The drone’s velocities are constant at a rate of 1 km per time period which is defined at start up as 1 minute and 20 seconds equating to a speed of 45 km/h. All points plotted on the graph are overlaid with text labelling their properties. The object properties are listed in table 6.1.

Table 6.1: Object properties.

Object Assigned Properties

Requests Location, Number of Specimens, Urgency Rating Drones Position, Range , Payload Capacity

Region Radius, Specified number of Drones, Specified number of initial request points

Chapter 7

SIMULATION OPTIMISATION

7.1

SIMULATION

The following scenarios are used to assess the simulation algorithm. The system’s ability to produce accurate results are evaluated based on the input variables reflecting real life scenarios.

The variables can be configured according to the criteria of a region under assessment. The input variables are based on the known values of the region under question. The sim- ulation automates the properties of the drones and request points while running through each iteration. Once the simulation is complete, the output variables are presented on a graph depicting as a map of the region as well as a log file of the output variables. The simulation variables can be found in table 7.1.

Table 7.1: Key Variables.

Input Variables Automated Values Output Variables

No. of Request Points Initial Payload Capacity No. of Specimens Collected No. of Drones Initial Flight Range No. of Points Visited Radius of Search Space Request Rewards Total Distance covered No. of Iterations Penalty Values Total Flight Time No. Specimens per location

Specimen Urgency

An example of the log output, found in the accompanying disk, labelled “Log Files.PDF”, is as follows:

“RequestXY = (23,6) DroneXY = (27,6) Coordinate = 10 Drone = 1 Distance = 4.00 No.Specimens = 2 Req.Priority = 3 Drone Range = 22 Drone Capacity = 85 Route Possible = 1 Reward = 37.72 ”

“Accumulated Distance Travelled = 103.53 Kilometers , Time Elapsed = 139.21 minutes, No.Point Visited = 19 No.Specimens Collected = 87”