Preparing your Path…
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Path Catalog
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7801 Paths · page 308 / 781
This advanced learning path equips senior industrial engineering students with the knowledge and skills to design and optimize logistics networks. It covers foundational concepts in supply chain management, network design, routing, freight, and distribution, emphasizing practical application and optimization techniques.
This learning path equips senior and graduate students with the skills to apply industrial engineering methods to healthcare systems. It covers patient flow, scheduling, resource allocation, and quality improvement, integrating operations research techniques. Learners will progress from foundational healthcare operations to advanced modeling and analysis.
This learning path guides senior industrial engineering students through the principles and practices of discrete-event simulation and agent-based modeling using Arena and AnyLogic. It covers model building, animation, statistical analysis, and optimization to enable effective system analysis and decision-making.
This learning path introduces sophomore industrial engineering students to programming fundamentals with Python, focusing on data structures and analytics libraries essential for engineering problem-solving. Starting with basic algebra and programming concepts, learners progress through Python syntax, data structures, and key libraries such as NumPy, pandas, and Matplotlib, culminating in data analysis and visualization projects relevant to industrial engineering.
This graduate-level path explores how human behavior—decision biases, risk perception, trust, and incentives—shapes operational systems. It integrates operations management and psychology to design better processes and policies. The path progresses from foundational concepts to advanced applications, emphasizing evidence-based insights and practical implications.
This learning path guides graduate students through the theory and practice of multi-objective optimization. Starting with fundamental concepts in single-objective optimization and linear programming, it progresses to Pareto optimality, classical weighted methods, and advanced metaheuristic approaches, culminating in practical applications and performance evaluation.
This advanced graduate-level path develops a rigorous understanding of queuing models applied to industrial systems, from foundational probability and stochastic processes through Markov chains, birth-death processes, and queuing networks. Learners will acquire the analytical tools to model, analyze, and design service and production systems under uncertainty.
This path equips senior industrial engineering students with the skills to apply data analytics to industrial problems. It covers essential statistics and programming foundations, then progresses through data mining and machine learning, culminating in prescriptive analytics for decision-making.
This advanced learning path provides a systematic understanding of systems engineering principles, focusing on the system lifecycle, requirements engineering, integration, and verification. It begins with systems thinking and foundational concepts, then progresses through lifecycle models, requirements, architecture, integration, verification, and cross-cutting concerns like risk and configuration management, culminating in a capstone project.
This learning path equips senior industrial engineering students with the knowledge to design advanced production planning systems. It covers fundamental operations management concepts, MRP/ERP systems, advanced scheduling techniques, and lean/JIT approaches, culminating in an integrated system design project.