Abstract: Technologies for computing, within a real-time time interval, of a digital plan for a vehicle in response to receipt by an online system of a volume of short-route pickup-and-delivery requests that increases over an operational period, include: receiving, by a hybrid optimization model, a set of digital inputs; where the set of digital inputs includes (i) pairs of geo-location data associated with pickup-and-delivery requests and (ii) geo-location data associated with the vehicle and (iii) precedence data that indicate an order that geo-locations in a pair of geo-location data are to be visited; using the hybrid optimization model to generate, in an execution time that does not exceed the real-time time interval, a pickup and delivery plan that conforms to the precedence data as digital output in response to the set of digital inputs; where the hybrid optimization model includes a constraint programming-based solver that operates in coordination with at least one other solver; creating, digitally storing, tra

Abstract: A computer-implemented method comprises receiving training data corresponding to a plurality of trips, the training data including at least a value for a set of attributes for each of the plurality of trips, the set of attributes including an indication of when in a week a trip is taken or in which of a plurality of meal-based time zones in a day the trip is taken, the training data including an actual travel time for each of the plurality of trips. The method further comprises creating and storing, in computer memory, a digital model that is configured to predict a travel time for a future trip based on the training data, the digital model including a set of parameters corresponding to the set of attributes, the digital model including a plurality of sets of values for the set of parameters.

Abstract: Technologies for creating and executing a runnable that includes a decision diagram (DD)-based optimization function are capable of configuring the runnable based on runnable options data passed by the client program to a runner function, creating an executable version of the configured runnable based on context data associated with the client program, using input data read from the data source, creating a decision model and executing the executable version of the configured runnable using the decision model, and writing output produced by the executing of the executable version of the configured runnable to the data source.

Abstract: Technologies for creating and executing a runnable that includes a decision diagram (DD)-based optimization function are capable of configuring the runnable based on runnable options data passed by the client program to a runner function, creating an executable version of the configured runnable based on context data associated with the client program, using input data read from the data source, creating a decision model and executing the executable version of the configured runnable using the decision model, and writing output produced by the executing of the executable version of the configured runnable to the data source.