Machine-learned model for optmizing selection sequence for items in a warehouse

A method comprising:

• accessing a plurality of training examples, each training example comprising a list of candidate items;
• initializing a plurality of weights for a machine-learning model;
• training the machine-learning model based on each of the plurality of training examples, and wherein training the machine-learning model on a training example comprises:
• transmitting the list of candidate items of the training example to a picker device associated with a picker of the plurality of pickers;
• receiving, from the picker device, a picking sequence for the list of candidate items, wherein the picking sequence indicates a sequence in which the picker picked the list of candidate items;
• retraining the machine-learning model based on the picking sequence by, for a subject item in the picking sequence:
• inputting, to the machine-learning model, a list of previously-picked items in the picking sequence and an embedding for each unpicked item in the picking sequence to generate a prediction score for each unpicked item in the picking sequence, wherein a prediction score for each unpicked item represents a likelihood that the corresponding unpicked item is picked next by the picker;
• comparing the prediction score for each unpicked item to the subject item in the picking sequence;
• and
• updating the plurality of weights for the machine-learning model based on the comparison;
• and
• storing a final plurality of weights for the machine-learning model to a non-transitory computer-readable medium.

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising:

• accessing a plurality of training examples, each training example comprising a list of candidate items and a picking sequence in which the items in the list of candidate items were picked;
• initializing a plurality of weights for a machine-learning model;
• training the machine-learning model based on each of the plurality of training examples, and wherein training the machine-learning model on a training example comprises:
• transmitting the list of candidate items of the set of training data to a picker device associated with a picker of the plurality of pickers;
• receiving, from the picker device, a picking sequence for the list of candidate items, wherein the picking sequence indicates a sequence in which the picker picked the list of candidate items;
• retraining the machine-learning model based on the picking sequence by, for a subject item in the picking sequence:
• inputting, to the machine-learning model, a list of previously-picked items in the picking sequence and an embedding for each unpicked item in the picking sequence to generate a prediction score for each unpicked item in the picking sequence, wherein a prediction score for each unpicked item represents a likelihood that the corresponding unpicked item is picked next by the picker;
• comparing the prediction score for each unpicked item to the subject item in the picking sequence;
• and
• updating the plurality of weights for the machine-learning model based on the comparison;
• and
• storing a final plurality of weights for the machine-learning model to a non-transitory computer-readable medium.