Abstract: A method and system for time series forecasting on a big data set are provided. The method includes receiving a plurality of time series, each of the time series representing a historical demand pattern for an item, performing a domain-based segmentation to identify a plurality of statistically forecastable time series from the plurality of time series, grouping the plurality of statistically forecastable time series into one or more clusters, for each cluster, generating an aggregate time series based on time series included in the cluster, performing a future demand forecast at a cluster level by performing time series forecasting of the aggregate time series for each cluster, and determining a future demand forecast for each item based on the time series forecasting of the aggregate time series.

Abstract: A method and system are provided where a module employing reinforcement learning (RL) can learn to solve the vehicle selection and space allocation problems for the transportation and/or storage of goods. In one embodiment, a method includes: (a) obtaining a specification of a load that includes enclosures of one or more enclosure types, and the specification includes, for each enclosure type: (i) dimensions of an enclosure of the enclosure type, and (ii) a number of enclosures of the enclosure type. The method also includes (b) obtaining a specification of vehicles of one or more vehicle types, where the specification includes, for each vehicle type: (i) dimensions of space available within a vehicle of the vehicle type, and (ii) a number of vehicles of the vehicle type that are available for transportation. The method further includes (c) providing a simulation environment for simulating loading of a vehicle.