Selecting pickers for service requests based on output of computer model trained to predict acceptances

A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:

• access a first machine learning model trained to predict a likelihood that a picker will accept a service request for an order placed with an online concierge system;
• apply the first machine learning model to predict the likelihood that each picker of a plurality of pickers will accept a first service request for a first order;
• access a distribution of timespans between a sending and an acceptance of a service request for one or more previous orders for one or more pickers of the plurality of pickers;
• identify a first plurality of sets of pickers from the plurality of pickers based at least in part on a first retailer location associated with the first order, wherein each set of pickers of the first plurality of sets of pickers comprises a first number of pickers associated with a set of locations within a first radius of the first retailer location and the first number of pickers is proportional to the first radius;
• generate a simulated response of each set of pickers of the first plurality of sets of pickers to the first service request based at least in part on the likelihood that each picker of the plurality of pickers will accept the first service request for the first order and the distribution of timespans;
• train a second machine learning model to predict a response of a set of pickers to a service request based at least in part on a set of attributes of the first order, the simulated response generated for each set of pickers of the first plurality of sets of pickers, and a corresponding first number of pickers and first radius;
• receive a new order placed with the online concierge system;
• identify a second plurality of sets of pickers from the plurality of pickers based at least in part on a second retailer location associated with the new order, wherein each set of pickers of the second plurality of sets of pickers comprises a second number of pickers associated with a set of locations within a second radius of the second retailer location and the second number of pickers is proportional to the second radius;
• for each set of pickers of the second plurality of sets of pickers, apply the second machine learning model to predict the response of a corresponding set of pickers to a second service request for the new order based at least in part on the set of attributes of the new order and a corresponding second number of pickers and second radius;
• and
• determine a minimum number of pickers to send the second service request for the new order based at least in part on the response predicted for each set of pickers of the second plurality of sets of pickers and a delivery time associated with the new order.

A computer system comprising:

• a processor;
• and
• a non-transitory computer readable storage medium storing instructions that, when executed by the processor, cause the processor to perform actions comprising:
• accessing a first machine learning model trained to predict a likelihood that a picker will accept a service request for an order placed with an online concierge system;
• applying the first machine learning model to predict the likelihood that each picker of a plurality of pickers will accept a first service request for a first order;
• accessing a distribution of timespans between a sending and an acceptance of a service request for one or more previous orders for one or more pickers of the plurality of pickers;
• identifying a first plurality of sets of pickers from the plurality of pickers based at least in part on a first retailer location associated with the first order, wherein each set of pickers of the first plurality of sets of pickers comprises a first number of pickers associated with a set of locations within a first radius of the first retailer location and the first number of pickers is proportional to the first radius;
• generating a simulated response of each set of pickers of the first plurality of sets of pickers to the first service request based at least in part on the likelihood that each picker of the plurality of pickers will accept the first service request for the first order and the distribution of timespans;
• training a second machine learning model to predict a response of a set of pickers to a service request based at least in part on a set of attributes of the first order, the simulated response generated for each set of pickers of the first plurality of sets of pickers, and a corresponding first number of pickers and first radius;
• receiving a new order placed with the online concierge system;
• identifying a second plurality of sets of pickers from the plurality of pickers based at least in part on a second retailer location associated with the new order, wherein each set of pickers of the second plurality of sets of pickers comprises a second number of pickers associated with a set of locations within a second radius of the second retailer location and the second number of pickers is proportional to the second radius;
• for each set of pickers of the second plurality of sets of pickers, applying the second machine learning model to predict the response of a corresponding set of pickers to a second service request for the new order based at least in part on the set of attributes of the new order and a corresponding second number of pickers and second radius;
• and
• determining a minimum number of pickers to send the second service request for the new order based at least in part on the response predicted for each set of pickers of the second plurality of sets of pickers and a delivery time associated with the new order.