Worker assignment for a product servicing and return back

numpy
pandas
machine_learning

#1

Hi friends,

I have a dataset which contains a list of works, a list of customer and product room locations. These workers have to go and pick the products from the customer location to the product room, where the item will undergo servicing. After this, the product has to be return back to the customer location.

Both worker and customer location is known in(latitude, longitude). How can we assign the tasks (say 10:30 am -11:30 am) efficiently?

At a time only one item picking and giving to product room or back from product room to customer.
i tried grouping in may way with respect to minimum distance but the same worker is getting more than one job at same time.


#2

@Allaen can you provide a sample dataset and the exact problem statement.

Also, are there any constraints - like working hours per worker, how much distance can a worker cover, what speed can they walk / drive at?

Regards,
Kunal


#3

Hi Kunal,
my data set attributes are
workerid , lat, longt,availabilityworkerlog.csv (2.4 KB)

custmerId, Address, customer lat, customer longt, date, timeslot

there is no working hours limit. if the worker is available, we get availability as 1, else 0.
Distance of travel is maximum 10KM, (straight line distance). Speed is not considered in this assignment.