Method and system for generating explainable task-based optimization

A system for explainable task-based optimization, the system comprising:

• a memory;
• at least one processor in communication with the memory, the at least one processor configured to:
• receive a target decision variable that is of interest to the user and an auxiliary dataset associated with a decision-making problem;
• generate a prediction of the target decision variable using a forecasting machine learning (ML) model;
• determine an optimal decision variable for the decision-making problem with uncertain parameters and variables, based on the predicted target decision variable, the auxiliary dataset, user-defined constraints, and risk aversion parameters;
• execute the optimal decision variable and determining an actual cost outcome based on the execution of the optimal decision as computed for the decision-making problem;
• update the forecasting ML model using a rolling window technique based on the actual cost outcomes;
• evaluate the actual cost outcome of an executed decision based on comparing with the optimal performance;
• and
• update optimization parameters based on the actual cost outcome to refine the forecasting ML model.