Abstract: Certain aspects of the present disclosure provide techniques for generating multivariate time series data utilizing a variational auto-encoder (VAE) having an architecture for injecting custom temporal structures into the generated multivariate time series data. A method for generating multivariate time series data includes sampling a multivariate distribution forming a latent space vector, processing the latent space vector with an interpretable decoder of a variational auto-encoder, an architecture of the interpretable decoder comprising a plurality of blocks including one or more blocks configured to inject one or more temporal structures into multivariate time series data, and outputting, from the interpretable decoder, generated multivariate time series data comprising one or more temporal structures defined by the injected one or more temporal structures.

Abstract: Systems and methods of workforce scheduling are disclosed. One example embodiment, among others, comprises a computer-implemented method of scheduling workers. Each worker is associated with one of a set of flexibility classifications, which include non-flex-time and at least one flex-time. The method includes generating a set of shift instances to cover forecasted demand over a planning period, and assigning the shift instances to the set of workers by iterating through the each of the workers to assign at least a portion of the shift instances to a selected one of the workers. The assigning is such that total hours assigned to the selected worker depends on a number associated with the classification of the selected worker.

Abstract: Systems and methods of workforce scheduling are disclosed. One example embodiment, among others, comprises a computer-implemented method of scheduling workers. Each worker is associated with one of a set of flexibility classifications, which include non-flex-time and at least one flex-time. The method includes generating a set of shift instances to cover forecasted demand over a planning period, and assigning the shift instances to the set of workers by iterating through the each of the workers to assign at least a portion of the shift instances to a selected one of the workers. The assigning is such that total hours assigned to the selected worker depends on a number associated with the classification of the selected worker.

Abstract: Systems and methods of workforce scheduling are disclosed. One example embodiment, among others, comprises a computer-implemented method of scheduling workers. Each worker is associated with one of a set of flexibility classifications, which include non-flex-time and at least one flex-time. The method includes generating a set of shift instances to cover forecasted demand over a planning period, and assigning the shift instances to the set of workers by iterating through the each of the workers to assign at least a portion of the shift instances to a selected one of the workers. The assigning is such that total hours assigned to the selected worker depends on a number associated with the classification of the selected worker.