Multi-Objective Data-Driven Production Optimization with a Feedback Mechanism

Abstract

Operation recommendations and automation for various industries rely on optimization over the top of the real-time IoT data streams. Such recommendation applications have become imperative for industrial manufacturing, such as petroleum, chemicals, and food processing. We build a novel system of user-in-the-loop multi-objective optimization under the initial uncertainty of the optimization objectives, wherein the uncertainty is iteratively resolved via user feedback. We propose an interactive optimization system wherein both business and operational goals become defined as the optimization processes and where objective selection is incorporated as part of the optimization procedure. We show that such a solution exists during the iteration process if the feasibility space is not empty initially and is constrained by industry operations. Using an oil sands application, we demonstrate this approach and compare modeling results in values, response to business and operational priorities, and interpretability to the weighted sum optimization.