Abstract: Mechanisms are provided for generating a resource allocation in an omnichannel distribution network. Demand forecast data and current inventory data related to a resource and the omnichannel distribution network are obtained and an ally-adversary bimodal inventory optimization (BIO) computer model is instantiated that includes an adversary component that simulates, through a computer simulation, a worst-case scenario of resource demand and resource availability, and an ally component that limits the adversary component based on a simulation of a limited best-case scenario of resource demand and resource availability. The BIO computer model is applied to the demand forecast data and current inventory data, to generate a predicted consumption for the resource. A resource allocation recommendation is generated for allocating the resource to locations of the omnichannel distribution network based on the predicted consumption, which is output to a downstream computing system for further processing.

Abstract: An example operation may include one or more of storing a hierarchical data set, receiving a plurality of predicted outputs from a plurality of nodes in a distributed computing environment, respectively, wherein each predicted output is generated by a different node via execution of a time-series forecasting model on a different subset of lowest level data in the hierarchical data set, combining the plurality of predicted outputs via bottom-up aggregation to generate one or more additional predicted outputs for the time-series forecasting model based on one or more levels above the lowest level in the hierarchical time-series data set, determining error values for the time-series forecasting model at each level of the hierarchical data set based on the received and the one or more additional generated predicted outputs, and modifying a parameter of the time-series forecasting model based on the determined error values.

Abstract: An example operation may include one or more of storing a hierarchical time-series data set in memory, initially training a first time-series forecasting model based on a lower level of time-series data in the hierarchical data set, training a second time-series teaching forecasting model based on an upper level of time-series data from the hierarchical data set which includes an additional level of aggregation with respect to the lower level of time-series data, optimizing one or more parameters of the initially trained first time-series forecasting model based on predicted outputs from the trained second time-series forecasting model in comparison to predicted outputs from the initially trained first time-series forecasting model, and storing the modified first time-series forecasting model in the memory.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to classifying accuracy of analytical model, such as a neural network. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an accessing component that accesses an analytical model, a deviation component that generates combined results of the analytical model in response to a set of inputs that vary in degree of perturbation of a set of test data, and an analysis component that compares a range of the combined results to a range of the ideal results.

Abstract: A processor may estimate uncensored demand from historical supply chain data. The processor may ingest historical data. The processor may convert the historical data to a dataset of multiple time series corresponding to sales for different products and locations and channels across multiple time points that is usable by an uncensored demand estimation machine learning model. The processor may train the uncensored demand estimation machine learning model by applying optimization solver techniques for deep learning.

Abstract: A processor in an omnichannel environment, over a specific network with transaction level operations, may receive one or more input configurations. The processor may identify, based on the one or more input configurations, one or more articles. The processor may identify one or more key performance indicators (KPIs) associated with the one or more articles. The processor may compute, based on an uncensored demand trajectory, an impact on the KPIs over a specified period in the omnichannel environment. The processor may provide the impact to a user.

Abstract: In an approach to improve order management by performing sustainable order fulfillment optimization through computer analysis, embodiments receive an order from a user through the order management system for performing sustainable order fulfillment. Further, embodiments estimate carbon emissions and economic costs of fulfilling the order from a plurality of nodes, and output an optimal sustainable order fulfillment. Additionally, responsive to receiving confirmation to implement the output optimal sustainable order fulfillment, embodiments place the optimal sustainable order fulfillment for the received order.

Abstract: In an aspect, input data can be received, including at least time series data associated with purchases of at least one product and causal influencer data associated with the purchases. The causal influencer data can include at least non-stationary data, where lost shares associated with said at least one product are unobserved. An artificial neural network can be trained based on the received input data to predict a future global demand associated with at least one product and individual market shares associated with at least one product. The artificial neural network can include at least a first temporal network to predict the global demand and a second temporal network to predict each of the individual market shares. The first temporal network and the second temporal network can be trained simultaneously.

Abstract: A computer implemented method of improving parameters of a critic approximator module includes receiving, by a mixed integer program (MIP) actor, (i) a current state and (ii) a predicted performance of an environment from the critic approximator module. The MIP actor solves a mixed integer mathematical problem based on the received current state and the predicted performance of the environment. The MIP actor selects an action a and applies the action to the environment based on the solved mixed integer mathematical problem. A long-term reward is determined and compared to the predicted performance of the environment by the critic approximator module. The parameters of the critic approximator module are iteratively updated based on an error between the determined long-term reward and the predicted performance.

Abstract: Embodiments relate to a system, program product, and method for inducing creativity in an artificial neural network (ANN) having an encoder and decoder. Neurons are automatically selected and manipulated from one or more layers of the encoder. An encoded vector is sampled for an encoded image. Decoder neurons and a corresponding activation pattern are evaluated with respect to the encoded image. The decoder neurons that correspond to the activation pattern are selected, and an activation setting of the selected decoder neurons is changed. One or more novel data instances are automatically generated from an original latent space of the selectively changed decoder neurons.

Abstract: A machine learning system that incorporates arbitrary constraints is provided. The machine learning system selects a set of domain-specific constraints from a plurality of sets of domain-specific constraints. The machine learning system selects a set of general functional relationships from a plurality of sets of general functional relationships. The machine learning system maps the selected set of general functional relationships and the selected set of domain-specific constraints to a set of learning transforms. The machine learning system modifies a machine learning specification according to the set of learning transforms, wherein the machine learning specification specifies a model construction, a model setup, and a training objective function. The machine learning system optimizes a machine learning model according to the modified machine learning specification.

Abstract: A machine learning system that uses a split net configuration to incorporate arbitrary constraints receives a set of input data and a set of functional constraints. The machine learning system jointly optimizes a deep learning model by using the set of input data and a wide learning model by using the set of constraints. The deep learning model includes an input layer, an output layer, and an intermediate layer between the input layer and the output layer. The wide learning model includes an input layer and an output layer but no intermediate layer. The machine learning system provides a machine learning model comprising the optimized deep learning model and the optimized wide learning model.

Abstract: A machine learning system that incorporates arbitrary constraints into deep learning model is provided. The machine learning system provides a set of penalty data points en a set of arbitrary constraints in addition to a set of original training data points. The machine learning system assigns a penalty to each penalty data point in the set of penalty data points. The machine learning system optimizes a machine learning model by solving an objective function based on an original loss function and a penalty loss function. The original loss function is evaluated over a set of original training data points and the penalty loss function is evaluated over the set of penalty data points. The machine learning system provides the optimized machine learning model based on a solution of the objective function.

Abstract: In various embodiments, a computing device, a non-transitory storage medium, and a computer implemented method of improving a computational efficiency of a computing platform in processing a time series data includes receiving the time series data and grouping it into a hierarchy of partitions of related time series. The hierarchy has different partition levels. A computation capability of a computing platform is determined. A partition level, from the different partition levels, is selected based on the determined computation capability. One or more modeling tasks are defined, each modeling task including a group of time series of the plurality of time series, based on the selected partition level. One or more modeling tasks are executed in parallel on the computing platform by, for each modeling task, training a model using all the time series in the group of time series of the corresponding modeling task.

Abstract: A computing device for time series modeling and forecasting includes a processor, and a memory coupled to the processor. The memory stores instructions to cause the processor to perform acts including encoding an input of a multivariate time series data, and performing a non-linear mapping of the encoded multivariate time series data to a lower-dimensional latent space. The next values in time of the encoded multivariate time series data in the lower dimensional latent space are predicted. The predicted next values and a random noise are mapped back to an input space to provide a predictive distribution sample for a next time points of the multivariate time series data. One or more time series forecasts based on the predictive distribution sample are output.

Abstract: Embodiments herein describe a return network simulation system that can simulate changes in a retailer's return network to determine the impact of those changes. Advantageously, being able to accurately simulate the retailer's return network means changes can be evaluated without first making those adjustments in the physical return network. Doing so avoids the cost of implementing the changes on the return network without first being able to predict whether the changes will have a net positive result (e.g., a positive result that offsets any negative results). A retailer can first simulate the change on the return network, review how the change affects one or more KPIs, and then decide whether to implement the change in the actual return network. As a result, the retailer has a reliable indicator whether the changes will result in a desired effect.

Abstract: A computer implemented method and system of setting values of parameters of nodes in an omnichannel distribution system, the method comprising is provided. Input parameters are received from a computing device. Historical data related to the network of nodes is received from a data repository. A synthetic scenario is determined based on the received input parameters and the historical data. Each node is clustered into a corresponding category. For each category of nodes, key parameters are identified. A range of each key parameter is determined based on the synthetic scenario. A number of simulations N to perform with data sampled from the synthetic scenario within the determined range of each key parameter is determined. For each of the N simulations, a multi-objective optimization is performed to determine a cost factor of the parameter settings. The parameter settings with a lowest cost factor are selected.

Abstract: A computer implemented method and system of calculating labor resources for a network of nodes in an omnichannel distribution system. Input parameters are received from a computing device of a user. Historical data related to a network of nodes is received, from a data repository. A synthetic scenario is determined based on the received input parameters and the historical data. For each node, key parameters are identified and set based on a multi-objective optimization, wherein the multi-objective optimization includes a synthetic inventory allocation to the node based on the synthetic scenario. A synthetic labor efficiency is determined for the node from the synthetic scenario. Labor resources are calculated based on the synthetic inventory allocation for the synthetic scenario. The labor resources of at least one node are displayed on a user interface of a user device.

Abstract: A computer-implemented method for a machine learning based design framework includes receiving input data, generating a design proposal based on the input data using a machine learning model, receiving feedback for the design proposal from a designated reviewer of the design proposal, updating a user preference profile associated with the designated reviewer using data generated by a different machine learning model based on the feedback for the design proposal, updating the design proposal to replace the candidate design with a new candidate design based on the user preference profile, and generating a final design based on the design proposal. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Techniques for facilitating estimation of node processing capacity values for order fulfillment are provided. In one example, a computer-implemented method can comprise: generating, by a system operatively coupled to a processor, a current processing capacity value for an entity; and determining, by the system, a future processing capacity value for the entity based on the current processing capacity value and by using a future capacity model that has been explicitly trained to infer respective processing capacity values for the entity. The computer-implemented method can also comprise fulfilling an order of an item, by the system, based on the future processing capacity value.