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: A computer-implemented machine learning method includes accessing a decision tree associated with a path-based machine learning model. The decision tree is split into a plurality of multiway decision trees in a path-based formulation, each of the plurality of decision trees having an attribute not occurring more than once in each of the plurality of decision trees. A problem associated with the machine learning model is solved using one or more of the plurality of decision trees in which one or more decision rules of the decision tree are mapped using a mixed-integer program (MIPS).

Abstract: A processor may receive input data. The processor may train based on the received input data, a machine learning model to estimate rate elasticity, attraction value, and a dissimilarity index associated with an object query and at least one object attribute. The processor may generate one or more object bundles. The processor may output the one or more object bundles to the user.

Abstract: A computer implemented method includes identifying, by one or more processors, a decision tree corresponding to an artificial intelligence model, detecting, by one or more processors, new data associated with an update to the identified decision tree, identifying, by one or more processors, counterfactual data corresponding to the new data, identifying, by one or more processors, one or more expected outcomes corresponding to the counterfactual data and the new data, and generating, by one or more processors, an updated decision tree based on the identified new data and the identified counterfactual data. A computer program product and computer system corresponding to the method are also disclosed.

Abstract: A method, a computer program product, and a system for optimized passenger rebooking including obtaining at least one travel disruption affecting at least one scheduled trip for a plurality of transported items. A demand valuation is calculated for each transported item of the plurality of transported items. A plurality of supply valuations is calculated for a plurality of alternative trips. An optimized alternative trip is selected from among the plurality of alternative trips for each transported item based on a comparison of the demand valuation and the plurality of supply valuations.

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 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 generating an Artificial Intelligence (AI) driven prescriptive policy and executing a function includes obtaining interdependent operational information about the function. A model is trained with the interdependent operational information about the function to dynamically generate a plurality of candidate decision paths from a group of all feasible decision paths for a plurality of interrule logical conditions and one or more dynamic constraints of the operational information. A prescriptive policy is generated from the plurality of candidate decision paths to execute the function that satisfies to a threshold degree of confidence the interrule logical conditions and the one or more dynamic constraints of the operational information. The function is executed based on the generated prescriptive policy.

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: One embodiment of the invention provides a method for integrated segmentation and prescriptive policies generation. The method comprises training a first artificial intelligence (AI) model and a second model based on training data. The first AI model comprises a teacher model trained to determine a likelihood of a desired outcome for a given action. The second model comprises a prescriptive tree trained for segmentation. The method further comprises determining, via the teacher model, a first policy that produces an optimal action. The optimal action provides a best expected outcome. The method further comprises applying, via the second model, a recursive segmentation algorithm to generate one or more interpretable prescriptive policies. Each interpretable prescriptive policy is less complex and more interpretable than the first policy. The method further comprises, for each interpretable prescriptive policy, determining, via the teacher model, an expected outcome for the interpretable prescriptive policy.

Abstract: A computer generates an optimized decision distribution vector for a plurality of related, demand-correlated products. The computer receives data indexed by product, with each entry including several entry attributes. The computer receives decision context data for the products. The computer determines a set of primary attributes and trains a first machine learning model based upon those attributes. The computer receives a decision optimization request that includes an associated set of attributes corresponding to the primary attributes. The computer scores the associated set of attributes, using the first machine learning model, to generate a baseline purchase propensity. The computer trains a second machine learning model, based upon the baseline purchase propensity and the decision context data, to generate own-product and cross-product elasticity data.

Abstract: A method, a structure, and a computer system for customized bundles of products and services. The exemplary embodiments may include gathering data corresponding to one or more consumers, one or more products, and one or more services. In addition, exemplary embodiments may further include generating one or more bundles of the one or more products and services corresponding to a consumer of the one or more consumers based on applying one or more models to the gathered data. Moreover, exemplary embodiments may further include determining a price of the one or more bundles, and displaying the one or more bundles to the consumer.

Abstract: A computer generates an optimized decision distribution vector for a plurality of related, demand-correlated products. The computer receives data indexed by product, with each entry including several entry attributes. The computer receives decision context data for the products. The computer determines a set of primary attributes and trains a first machine learning model based upon those attributes. The computer receives a decision optimization request that includes an associated set of attributes corresponding to the primary attributes. The computer scores the associated set of attributes, using the first machine learning model, to generate a baseline purchase propensity. The computer trains a second machine learning model, based upon the baseline purchase propensity and the decision context data, to generate own-product and cross-product elasticity data.

Abstract: Techniques for a machine learning based tiered graphical user interface (GUI) are described herein. Aspects of the invention include receiving a set of offered products. The set of offered products is sorted into a plurality of tiers, and an initial tiered GUI is generated based on the sorting. Based on receiving customer feedback via the tiered GUI, the sorting of the set of offered products into the plurality of tiers is updated, and an updated tiered GUI is generated based on the updated sorting.

Abstract: A system that compresses data during neural network training. A memory stores computer executable components and neural network data, and a processor executes computer executable components stored in the memory. An anticipatory value of inventory (VOI) optimization component calculates optimal VOI and prices for immediate-future inventory levels in parallel and writes latest price updates for respective states to a price stack; and a recommendation component provides customized pricing recommendation for a product relative to a unique customer as a function of the latest price updates for respective states to the price stack.

Abstract: A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

Abstract: System and method for omni-channel retailer operations that integrate a network of brick-and-mortar stores with their online channel. The system and method includes calibrating a demand model for both brick-and-mortar sales and on-line channels over which a product is sold, the calibrating based upon a cross-channel fulfillment-aware inventory effect. An omni-channel sales prediction and fulfillment model is then constructed based on the calibrated demand model. Using constructed linear demand and revenue models, a plan is generated to optimize one or more: allocating of the product across physical stores, partitioning of the product for sales, and pricing of the product. Customer choices are jointly modeled across channels to allow switching, and a ship-from-store (SFS) inventory effect feature only for brick choice is applied to capture asymmetry.

Abstract: Systems and methods for modeling and forecasting cyclical demand systems in the presence of dynamic control or dynamic incentives. A method for modeling a cyclical demand system comprises obtaining historical data on one or more demand measurements over a plurality of demand cycles, obtaining historical data on incentive signals over the plurality of demand cycles, constructing a model using the obtained historical data on the one or more demand measurements and the incentive signals, wherein constructing the model comprises specifying a state-space model, specifying variance parameters in the model, and estimating unknown variance parameters.