AT72 - Industrial Manufacturing Engineer

The course discusses a set of approaches for coordinating activities related to planning, implementation, and control of goods and information flows in logistic networks and supply chains in such a way that the total system wide cost is minimized while satisfying the service level requirement.  The objective of this course is to impart to the students basic knowledge on logistics & supply chain management, which includes key issues on inventory control and management, supply contract, bullwhip effect and the value of information sharing, distribution strategy, procurement strategy, and pricing strategy

The objective of this course is to impart knowledge on various statistical methods with a special emphasis on design of experiments

The objective of this course is to provide the students knowledge on the deterministic decision models which can facilitate the decision making process.  Modeling concepts and applications of linear, integer, nonlinear, and dynamic programming as well as network models are addressed.  Solution methodologies for each type of optimization models are discussed. The student will also learn how to use modeling and optimization software.


The objective of this course is to help students develop competences on statistical techniques needed for data analysis, and various data mining techniques and algorithms used in practical problems that require processing big data for decision making purpose.


The objective of this course is to impart knowledge on mathematical modeling process of decision problems in complex stochastic environments.  This course covers stochastic operations research models, algorithms, and applications, including Markov chains and queuing models. Renewal theory, reliability theory, and stochastic models for manufacturing systems are also introduced. Further this course covers the analytical models which are the complements to the discrete event simulation approach

This course is an introduction to the concepts, principles, problems, and practices of production and operations management in both the manufacturing and service sectors of the economy. Emphasis is focused on preparing students to identify and apply appropriate management processes to ensure efficient and effective operations, while achieving operational excellence. Topics covered include: building competitive advantage, manufacturing & service operations, operations strategy, inventory management, forecasting, capacity and facilities, location,planning, scheduling,supply chain management, just-in-time & lean production, project management, and human resources in operations management. Various techniques and mathematical algorithms for solving traditional operations management problems will be explored.

To introduce the student to deterministic models which can facilitate decision making. Modeling concepts and applications of linear, integer, nonlinear, and dynamic programming as well as network models are addressed. Solution methodologies for each type of optimization models are discussed. The student will also learn how to use modeling and optimization softwares.