Decision Optimization for Complex Systems

This course builds a strong foundation in mathematical modeling and optimization techniques for data-driven decision-optimization in complex systems. Real-world engineering or business decision problems are constrained by several factors — such as budget, time, space, infrastructure, material, equipment and human resources — and are often combinatorially complex due to several inter-related decisions, despite advanced descriptive/prescriptive analytics technology. For example, decisions for network design, resource assignments, scheduling, routing, production and inventory planning, supply-demand matching, staffing, etc.

In this course students will learn a variety of modeling and analytical techniques for optimal decision-making in such complex scenarios, which can help in reducing cost, increasing revenue, as well as in handling conflicting objectives while respecting the decision constraints. Students will learn how to go from data to optimization models to decisions, and how to integrate predictive and simulation modeling for optimizing dynamic systems under uncertainty – all of this with hands-on real-world projects from a wide spectrum of industries including generative AI, finance, healthcare, energy, transportation, manufacturing, retail and supply chain.

The curriculum integrates the full technological evolution — from ERP to RPA, Intelligent Automation, and autonomous Agentic AI — with a deep exploration of the managerial and human challenges that accompany it. Technology is not a standalone solution; it must be carefully orchestrated with organizational structures, human behavior, and leadership to drive real transformation. Through realworld case studies of success and failure, students gain a practical, evidence-based methodology for diagnosing, leading, and sustaining technology-driven change — structured around three core pillars. This learning is anchored by a hands-on project running across all 4 modules — where student teams build a real MVP Agentic System, layer by layer, and compete in a final showcase judged by industry partners.

Three Core Pillars

All Engineering Programs at Tandon

• Build a stronger analytics portfolio by leveraging prescriptive modeling/analytics alongside predictive and descriptive approaches
• Automate complex decision-making for efficiency and scale
  
  Target roles: Operations Analyst, Operations Consultant, Product and Operations Manager, Project Manager, Manufacturing Manager, Process Improvement Engineer, Transportation and Logistics Engineer, Supply Chain Analyst, Quantitative Finance Analyst

Computer Science

• Connect AL/ML skills with optimization for business impact
• Innovate on predictive modeling through optimization techniques
• Add skills for optimizing computing resources for cloud and data centers
  
  Target roles: Machine Learning and AI Architect, Data Center Engineer/Manager, Operations Engineer

Management of Technology

• Leverage the prescriptive technology edge
• Build a stronger analytics portfolio with prescriptive analytics
  
  Target roles: Product Manager, Project Manager, Operations Manager, Technology Consultant, Digital Transformation Manager

Business School (NYU Stern)

• Leverage the prescriptive technology edge
• Learn quantitative methods for strategic/tactical/ operational decision-making
  
  Target roles: Strategy & Operations Manager, Supply Chain Manager, Project Manager, Human Resources Manager, Service Manager, Technology Investment and Digital Transformation Manager