AI-Integrated Industrial Engineering

The next-generation industrial engineering program focused on smart factories, logistics automation, service operations, and decision intelligence.

AI-Integrated Industrial Engineering transforms the industrial engineering profession for a world where algorithms automate decisions, optimize systems, and coordinate workflows. Graduates will not just improve existing processes; they will co-create future-ready operations using artificial intelligence, data, simulation, and digital twins as key tools.

Built on operations research, supply chain management, manufacturing systems, and human factors—and expanded with machine learning, simulation, and intelligent automation—students will lead changes in factories, warehouses, logistics networks, and service sectors like healthcare, finance, e-commerce, travel, and public services. From real-time scheduling and predictive inventory to human-in-the-loop decision systems, artificial intelligence becomes the primary toolkit.

Program Overview

This track is part of the AI-Integrated Engineering Program (AIEP) and offered only for IUP (English Program) under the Department of Industrial Engineering.

Bachelor’s Program: B.Eng. in Industrial Engineering (International Program)
Master’s Program: M.Eng. in AI-Integrated Engineering (Regular Thai curriculum)
First intake: Academic Year 2026
Number of Students: 30 (IUP program)
Admission Channels: TCAS1 (Portfolio), TCAS2 (Quota)
Not offered in regular or special program (Thai Program)

Program Highlights

Co-teaching by faculty members from both the Department of Industrial Engineering and the Department of Computer Engineering
End-to-end integration of industrial engineering and artificial intelligence: operations research models enhanced by machine learning and reinforcement learning; simulation-based policy testing; predict-then-optimize decision-making workflows.
Studios and laboratories: Digital Twin Studio; Decision Intelligence Laboratory; Automation and Sensing Laboratory (covering the Industrial Internet of Things, computer vision, and time-series analysis).
Real data with measurable outcomes: industry projects focusing on throughput, work-in-process, lead time, service level, and energy consumption.
Human-Centered Artificial Intelligence: Explainable Interfaces, Workload Design, and Safety Grounded in Human Factors.
Deployment mindset: operational machine-learning practices, governance, privacy, ethics, and change management.
Service-industry emphasis: Omnichannel service analytics, workforce rostering, queueing networks, appointment and capacity planning, revenue management, and operations governed by service-level commitments (for contact centers, hospitals and clinics, airports and airlines, banks and e-commerce, and public sectors).
Undergraduate-to-graduate pathway: Capstone aligned with the four-plus-one progression toward learning-based control.

What You’ll Learn

Students will build strong foundations in industrial engineering, including deterministic and stochastic operations research, production and inventory management, facility layout, queueing theory, ergonomics and safety. These core competencies provide the essential framework for understanding and optimizing complex systems and processes.

The program develops artificial intelligence and data capabilities through programming for data analysis, time-series modeling and anomaly detection, supervised and unsupervised machine learning, and reinforcement learning for scheduling and control. Students learn to leverage these technologies to enhance traditional industrial engineering methods with intelligent automation and predictive analytics.

Students will master simulation and digital twins through discrete-event and agent-based simulation, twin-driven experimentation, and stress testing of policies. The curriculum covers optimization at scale, including mixed-integer programming, constraint programming and decomposition, heuristics and metaheuristics, and robust and risk-aware planning techniques.

The program emphasizes decision intelligence through predict-then-optimize workflows, causal reasoning, development of key performance indicators and dashboards, explainability, and human-in-the-loop systems. Students learn responsible deployment practices including data governance, model validation, controlled experiments (A/B testing), monitoring, and lifecycle management.

Additionally, students will specialize in service systems, covering service design and queueing networks, skill-based routing and analytics for contact centers, appointment and surge-capacity planning in healthcare, dynamic pricing and revenue management, customer-journey and churn analytics, service quality engineering, and service-level commitments.

Sample Capstone and Research Topics

Reinforcement-learning-driven job-shop or flow-shop scheduling with energy or due-date objectives.
Predictive maintenance from sensor and event logs with cost-of-failure optimization.
Demand sensing with inventory optimization under service-level targets.
Warehouse digital twins for co-optimization of routing, slotting, and labour scheduling.
Dynamic vehicle routing with uncertain travel times and real-time dispatch.
Quality analytics using computer vision with prescriptive rework policies.
Service operations: workforce rostering that balances fairness, fatigue, and demand uncertainty.
Control-room decision support: alert triage and explainable recommendations.
Service-sector: contact-centre staffing and skill-based routing; hospital clinic appointment scheduling and bed or operating-room capacity planning; airline disruption recovery and crew pairing; dynamic pricing and capacity allocation for e-commerce or mobility services; optimization of service-level commitments for information-technology and shared-service operations.

Industry & Research Partners

Delta Electronics (smart factory collaboration)
Thai logistics, warehouse, and industrial automation firms (in development)
Research groups in intelligent systems at Kasetsart University
Service-sector collaborators (in development): hospital and health networks, banks and financial-technology firms, e-commerce platforms, and public-service agencies

4+1 Pathway

Students must meet academic requirements by their 3rd year by taking AI foundational courses.
Students start enrolling Master’s courses also in the 4th year. (See information for the M.Eng in AI-Integrated Engineering program.)
4th-year project linked to Master’s research in the 5th year.
Two degrees (B.Eng. + M.Eng.) in 5 years for future professionals.

The detailed study plan will be available soon. Read more about the undergraduate AI core courses and the Master program.

Career Opportunities

Smart-factory and automation engineer for production lines, robotics coordination, and real-time control
Operations research and optimization engineer for scheduling, planning, and network design
Supply chain and logistics analytics specialist for forecasting, inventory, routing, and risk-aware planning
Industrial data scientist and simulation engineer for time-series analysis, anomaly detection, and digital-twin experimentation
Digital-twin and systems-integration engineer for factories, warehouses, and service operations
Decision-intelligence and human-centred systems designer for control rooms, contact centres, and enterprise platforms
Reliability and predictive-maintenance engineer using sensor and event data
Operations manager in manufacturing or service organizations who can lead technology adoption and change
Entrepreneur or product leader building decision and automation solutions for industry and services

Competitive Advantages

Go beyond Excel and heuristics — learn to use cutting-edge AI to solve ops problems
End-to-end capability: from modelling and forecasting to optimization, deployment, and continuous monitoring
Master tools for simulation, real-time control, and decision modeling
Be ready for disruption, not replaced by it — design the systems that will reshape your field
Join a cross-functional ecosystem of AI+Engineering innovators
Portfolio on graduation that includes reproducible models, simulation artefacts, and operational playbooks

Enrichment Activities

Industry-based Capstone projects in smart manufacturing, logistics and service operations with real data
Undergraduate Research Opportunities with AI and optimization labs
Internships in factories, hospitals and clinics, banks and commerce platforms, airlines and airports, and technology service organizations
Technical workshops on the integration of operations research, machine learning, simulation, and responsible deployment
Site visits to production plants, distribution centres, control rooms, and service-operations hubs
Practitioner seminars, guest lectures, and mentorship from industry partners
Case competitions and design studios focused on decision intelligence and human-in-the-loop systems

AI Foundation Courses (Undergraduate Level)

Students will take the following courses during their first 3 years as undergradute students.

Applied AI for Engineering (01204162)
Mathematical Foundations for AI Engineers (course under development)
Programming Concepts for Data Processing and Analysis (course under development)

See course descriptions.

Equivalent internal courses from Industrial Engineering may be submitted for equivalency review.

Distinctive Graduate Outcomes

Graduates of this track will:

Combine classic IE methods with modern AI techniques
Model, analyse, and optimize complex operations in manufacturing, logistics, and service environments
Design adaptive, data-driven systems that respond to real-time changes
Lead automation projects that integrate human and AI decision-making
Communicate with both floor-operations teams and information-technology teams to lead cross-functional change
Be resilient in a changing job market where AI redefines operations leadership