Industries seek manufacturing and systems engineering graduates equipped with advanced computational tools to streamline processes, cut costs, and improve decision-making across diverse sectors (e.g., manufacturing, healthcare, and supply chain management). In this course, students learn versatile general problem-solving techniques—heuristic search, constraint programming, and automated planning—that are widely applicable to diverse manufacturing and systems engineering challenges (e.g., scheduling, resource allocation, process optimization, and dynamic system planning). These techniques form a vital part of the engineering problem-solving toolkit, complementing traditional approaches like operations research and data-driven techniques such as big data and machine learning. Through this course, students gain added skills to tackle complex industrial problems.