Research

Decorative research formulas and equationsThe Industrial and Systems Engineering (ISE) Department at Rensselaer focuses its research on applying the core disciplinary strengths of Industrial and Systems Engineering to both traditional and interdisciplinary applications. In general, ISE involves the application of mathematical, computational, statistical and information science methods to model, analyze, and solve complex decision problems in engineering, business, and social systems. It is distinctive from management and economics in the use of an engineering approach to design and analyze enterprise processes to optimize system-level performance. It is distinctive from computer science in its focus on the design of data and knowledge systems as the organizational nerve center where operations and enterprise systems are integrated.

In recent years, the two signature themes of Disaster Response and Adaptive Supply Chains have been a part of multiple faculty members' research activities. Our research in disaster response has been funded through the National Science Foundation and the Department of Homeland Security. Our research in adaptive supply chains has bend funded through the National Science Foundation, Office of Naval Research, and industry. Our faculty apply a variety of ISE tools to these critical application areas including, but not limited to, optimization, simulation, applied statistics, human factors and cognitive engineering. An overview of each of these areas is provided below and you can follow the links to access discussions on some example projects within each of these themes.

Robotic Arm for Manufacturing Processes
Machine Learning on Advanced Manufacturing

In the Department of Industrial and Systems Engineering, machine learning (ML) is revolutionizing advanced manufacturing by enabling data-driven process optimization, predictive maintenance, and real-time quality control. Research focuses on developing ML models that enhance efficiency, reduce downtime, and improve product quality by analyzing data from sensors and machinery. Additionally, ML is applied to optimize supply chains, enhance human-machine collaboration, and increase manufacturing flexibility, especially for customized production.

Gradient Descent Applied to a Surface Model to Find the Lowest Point.
Optimization

Optimization is a core mathematical tool for selecting a best solution among a possibly infinite number of alternatives. As a tool, it appears in most facets of industrial engineering, decision sciences, algorithm design, machine learning, and the management sciences including applications in healthcare, logistics, manufacturing, data science, and sports analytics. Theoretic and computational advancements in the area can drastically improve our ability to quickly make fast, effective decisions in a countless array of applications. 

adaptive supply chain diagram
Supply Chain Management and Logistics

Supply Chain Management covers all activities and decisions that take place during the transformation of raw materials to products or services delivered to end customers. Key decision areas include supply chain network design, production and inventory planning, transportation and logistics, aimed at efficient allocation and use of limited resources to meet demand in areas like commerce, disaster response, humanitarian aid, and national defense.

Industrial and Systems Engineering Department

CII 5015 - 5th Floor - Low Center for Industrial Innovation

Rensselaer Polytechnic Institute

Troy, NY 12180-3590

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