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Main
UQU Programs
Industrial Engineering

Industrial Systems Simulation

IE4106
3 hours English

Industrial Systems Simulation

Industrial Systems Simulation
IE3104 Operations Research (2)
3 hours One requisite
1 Foundations of Simulation in Industrial Engineering / Introduction to simulation concepts, types of simulation models, and their applications in industrial engineering / Understanding simulation project lifecycle and methodology for successful implementation / Overview of simulation tools and software platforms used in industry
2 Core Simulation Concepts and Programming Mechanism / Deep dive into simulation building blocks: states, events, queues, and their interactions / Implementation of time advancement mechanisms in discrete event simulation / Design and management of efficient data structures for simulation modeling
3 Probabilistic Modeling and Statistics / Principles and techniques of random number generation for simulation models / Implementation of various probability distributions to model real-world scenarios / Development of statistical frameworks using MATLAB for simulation analysis
4 System Dynamics, Markov Chains, and Petri Nets / Fundamental concepts of Markov chains and their application in system modeling / Development and analysis of transition probability matrices / Implementation of state transition models and steady-state analysis techniques / Application of Markov chains in industrial system optimization / Introduction to Petri net models for system representation and analysis. / Application of Petri nets in workflow and process modeling
5 Queuing Systems Analysis / Comprehensive study of Single-Server Queue systems and their industrial applications / Analysis and modeling of complex waiting line systems with multiple scenarios / Practical implementation using MATLAB/SimEvent with real-world examples
6 Inventory Systems and Validation / Design and implementation of Simple Inventory System models for industrial applications / Advanced techniques for model verification and validation procedures / Methods for measuring and analyzing system performance metrics
7 Advanced Modeling with Simulink / Implementation of model-based design principles in industrial systems / Development of entity transfer systems and process flow modeling / Advanced techniques for modeling complex industrial systems
8 Industrial Applications with Arena / Comprehensive implementation of Arena simulation software for industrial processes / Development of complex flow simulation models for manufacturing systems / Optimization techniques for production line efficiency and scheduling
9 Statistical Analysis Methods / Advanced techniques for analyzing simulation output data and interpreting results / Implementation of steady-state simulation analysis for continuous processes / Development of next-event simulation techniques for discrete systems
10 Advanced Applications / Implementation of Monte Carlo simulation techniques for complex systems / Analysis of comprehensive industrial case studies and their solutions / Integration of all concepts through final project presentations
1.1 Mapped to: K1

Understand fundamental simulation concepts, types, and methodologies and applications in industrial systems.

Teaching Strategy Lectures Problem-based learning Case Stadies Lab Demonstrations
Assessment Methods Three exams (theoretical midterm, practical lab, and final exams). Monthly quizzes. Homework simulation software assignments.
1.2 Mapped to: K2

Explain effective planning and management techniques for simulation projects to support industrial system design

Teaching Strategy Lectures Problem-based learning Case Stadies Lab Demonstrations
Assessment Methods Three exams (theoretical midterm, practical lab, and final exams). Monthly quizzes. Homework simulation software assignments.
1.3 Mapped to: K2

Apply mathematical and statistical principles, including probability distributions, random number generation, and data analysis, in simulation modeling.

Teaching Strategy Lectures Problem-based learning Case Stadies Lab Demonstrations
Assessment Methods Three exams (theoretical midterm, practical lab, and final exams). Monthly quizzes. Homework simulation software assignments.
2.1 Mapped to: S1

Define and conceptualize computational simulation models and explain their operations.

Teaching Strategy Interactive lectures Lab sessions Project-based learning (case study) Software tutorials
Assessment Methods Exams and quizzes Conceptual model design assignments Lab exam Group presentation
2.2 Mapped to: S2

Design, analyze, and interpret simulation experiments using statistical methods.

Teaching Strategy Interactive lectures Lab sessions Project-based learning (case study) Software tutorials
Assessment Methods Exams and quizzes Conceptual model design assignments Lab exam Group presentation
2.3 Mapped to: S3

Build, validate, and refine simulation models for various industrial systems and processes.

Teaching Strategy Interactive lectures Lab sessions Project-based learning (case study) Software tutorials
Assessment Methods Exams and quizzes Conceptual model design assignments Lab exam Group presentation
2.4 Mapped to: S4

Implement and evaluate industry-standard simulation tools (e.g., MATLAB, Simulink, Arena), recognizing their strengths and limitations.

Teaching Strategy Interactive lectures Lab sessions Project-based learning (case study) Software tutorials
Assessment Methods Exams and quizzes Conceptual model design assignments Lab exam Group presentation
2.5 Mapped to: S5

Integrate digital solutions and predictive modeling to define, measure, analyze, and control industrial processes for system optimization.

Teaching Strategy Interactive lectures Lab sessions Project-based learning (case study) Software tutorials
Assessment Methods Exams and quizzes Conceptual model design assignments Lab exam Group presentation
3.1 Mapped to: V1

Incorporate ethical considerations into simulation practices, emphasizing transparency and informed decision-making.

Teaching Strategy Interacive learning Self-directed learning
Assessment Methods Assignment Documents
3.2 Mapped to: V4

Develop self-driven learning approaches through advanced simulation methods, tools, and real-world case studies.

Teaching Strategy Interacive learning Self-directed learning
Assessment Methods Assignment Documents
Assessment
Weight
Midterm
25%
Quizes
5%
Lab work and assignments
10%
Group Project
20%
Final
40%
Total
100%