Delivery management system with integrated driver management and scheduling

A method for a delivery management system, the method comprising:

• obtaining a plurality of driver profiles, wherein each driver profile indicates at least one capability of a corresponding driver and an availability of the corresponding driver;
• generating, based on the plurality of driver profiles, a schedule assigning each of the drivers to a time period;
• receiving a first delivery task from a first customer of a first merchant, wherein the first delivery task defines a set of delivery task requirements;
• determining an availability status of each driver in the plurality of drivers based on the schedule;
• determining a plurality of available drivers in the plurality of drivers;
• identifying at least one driver from the plurality of available drivers whose driver profile satisfies the set of delivery task requirements;
• allocating the first delivery task to a first driver from the plurality of available deliverers based on either a first input from a user or an output of a delivery management engine;
• receive an acceptance of the first delivery task from the first driver;
• monitor an execution of the first delivery task;
• and
• generate a delivery management report providing an assessment of at least one of the delivery task or the first driver.

One or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:

• receive a digital schedule, the digital schedule including one or more shifts, each of the one or more shifts including at least one opening;
• execute a machine learning model using the digital schedule to generate demand characteristics associated with the digital schedule, the demand characteristics including (i) a number of drivers and (ii) driver characteristics associated with the number of drivers;
• parse a database including a plurality of digital representations of drivers to identify a selection of drivers having the driver characteristics associated with the number of drivers;
• automatically assign the selection of drivers to the one or more shifts of the digital schedule according to the demand characteristics generated by the machine learning model;
• monitor completion of at least one shift of the one or more shifts to measure a key delivery parameter associated with each delivery within the at least one shift;
• and
• update the machine learning model based on monitoring the at least one key delivery parameter.