Systems and methods for generating graphical user interfaces for adaptive delivery scheduling

A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform operations for delivery scheduling comprising:

• receiving, from a remote system, an electronic request to order a product;
• determining information associated with the remote system;
• determining a fulfillment center associated with the information and the product;
• assigning a delivery wave estimate by:
• calculating an estimated arrival from the fulfillment center to a camp zone based on at least one of distance, historic trends, or delivery rotations;
• and
• assigning the delivery wave estimate to the first wave when determining the estimated arrival is before a first wave;
• generating, based on the information associated with the remote system, an electronic message comprising:
• the determined fulfillment center,
• the product,
• a time of day, and
• the delivery wave estimate;
• and
• forwarding, to the fulfillment center, the electronic message and instructions to generate a graphical user interface displaying the product and the delivery wave estimate,
• wherein determining the fulfillment center comprises:
• storing in a database a plurality of previous electronic requests and associated fulfillment centers;
• dividing the previous electronic requests in a training dataset and a validation dataset, the training dataset having more requests than the validation dataset;
• generating a predictive model based on the training data set associating request information and fulfillment centers;
• validating the predictive model using the validation dataset;
• and
• determining the fulfillment center by applying the predictive model to the electronic request.

A computer-implemented method for delivery scheduling, the method comprising:

• receiving, from a remote system, an electronic request to order a first product and a second product;
• determining information associated with the remote system;
• determining a first fulfillment center associated with the information and the first product and a second fulfillment center associated with the second product and the second fulfillment center;
• assigning a first delivery wave for the first product and a second delivery wave for the second product by:
• calculating an estimated arrival from the fulfillment center to a camp zone based on at least one of distance, historic trends, or delivery rotations;
• and
• assigning the delivery wave estimate to the first wave when determining the estimated arrival is before a first wave;
• generating, based on the information associated with the remote system,
• an electronic message comprising:
• the first and second fulfillment centers,
• the first and second products,
• a time of day, and
• the first and second delivery wave estimates;
• determining that the second delivery wave estimate is different from the first delivery wave;
• modifying at least one of the first or second delivery wave estimates to match the other delivery wave estimate;
• forwarding, to the first fulfillment center and the second fulfillment center, the electronic message and instructions to generate a graphical user interface displaying a list with the first and second products color-coded with corresponding delivery wave estimate;
• and
• sending an instruction to the first fulfillment center and the second fulfillment center to print shipping labels listing the first or second delivery wave estimates,
• wherein determining the first fulfillment center and the second fulfillment center comprises:
• storing in a database a plurality of previous electronic requests and associated fulfillment centers;
• dividing the previous electronic requests in a training dataset and a validation dataset, the training dataset having more requests than the validation dataset;
• generating a predictive model based on the training data set associating request information and fulfillment centers;
• validating the predictive model using the validation dataset;
• and
• determining the first fulfillment center and the second fulfillment center by applying the predictive model to the electronic request.