Master of Science (M.Sc.) in Industrial Engineering
Program tanımlarıProgram Description
Profitable design and operation of modern industrial systems requires integration of human, material, equipment and financial resources. In recent years this integration has become tighter due to the inclusion of information technology, and resulted in more complex systems. Industrial Engineering research focuses on quantitative analysis, synthesis and management of such complex systems. The affiliated faculty members come from the Industrial Engineering department of the College of Engineering, the Operations and Information Systems group of the College of Administrative Sciences and Economics, and other related fields. Our research areas are diverse, including Logistics, Supply Chain Management, Service Operations, Production Systems, Stochastic Processes, Financial Engineering, Mathematical Programming, Data Mining and Bioinformatics. The programs are built on the basic methodologies of operations research and their applications in manufacturing, distribution and service industries. Graduates of the M.Sc. program have been placed in respectable Ph.D. programs in North America, Europe and Turkey as well as various professional positions in industry. We expect to have the first graduates of the Ph.D. program in 2008.
Research Areas
Bioinformatics (Ceyda Oğuz, Metin Türkay)
Data Mining (Özden Gür Ali, Sibel Salman, Serpil Sayın)
Optimization Theory and Algorithms (Ceyda Oğuz, Deniz Aksen, Metin Türkay, Sibel Salman, Serpil Sayın)
Production Systems and Scheduling (Yaman Arkun, Selçuk Karabatı, Fikri Karaesmen, Onur Kaya, Ceyda Oğuz, Barış Tan, Metin Türkay)
Service Operations (Zeynep Akşin, Evrim Güneş, Fikri Karaesmen, Lerzan Örmeci)
Supply Chain Management (Yalçın Akçay, Evrim Güneş, Fikri Karaesmen, Onur Kaya, Lerzan Örmeci, Barış Tan, Metin Türkay)
Curriculum
Required Courses: All students have to take the following 2 courses.
INDR 501 Optimization Models and Algorithms
INDR 503 Stochastic Models and Their Applications
Elective Courses: All students have to take 5 of the following elective courses according to their research interests and thesis subjects to complete at least 15 credits. All courses have 3 credits unless otherwise specified.
INDR 506 Computer Integrated Manufacturing and Automation
INDR 508 Discrete Event Simulation
INDR 520 Network Models and Optimization
INDR 530 Decision Analysis
INDR 551 Advanced Optimization Methods
INDR 553 Advanced Stochastic Processes
INDR 566 Scheduling
INDR 568 Heuristic Methods
INDR 578 Advanced Models in Supply Chain Management (Crosslisting with OPSM 602)
INDR 580 Selected Topics in Industrial Engineering
OPSM 502 Operations Management
OPSM 602 Advanced Models in Supply Chain Management (Crosslisting with INDR 578)
OPSM 632 Introduction to Management Science
OPSM 636 Service Operations Management
OPSM 637 Operations Strategy
OPSM 638 Supply Chain Management
OPSM 639 Project Management
OPSM 650 Selected Topics: Manufacturing and Service Op. Strategy
MGIS 501 Introduction to Management Information Systems
MGIS 641 Database Management Systems
MGIS 650 Selected Topics in Operations Management
ECOE 554 Machine Learning
Courses which are not listed above can be taken with the suggestion and permission of the program coordinator and/or thesis advisor. In addition to the course load, students have to take a seminar course and complete their M.Sc. thesis. For this purpose, they register to the following courses.
INDR 590 Seminar
INDR 595 M.Sc. Thesis
Students who have TA assignments must take TEAC 500: Teaching Experience during the semesters of their assignments. Students must also take ENGL 500: Graduate Writing course.
Course Descriptions
INDR 501 Optimization Models and Algorithms (3 redits)
Convex analysis, optimality conditions, linear programming model formulation, simplex method, duality, dual simplex method, sensitivity analysis; assignment, transportation, and transshipment problems.
Prerequisite: An undergraduate level Operations Research course or consent of the instructor.
INDR 502 Logistics and Supply Chain Systems (3 redits) Introduction to the concepts and terminology of logistics and supply chain management. Examination of components of logistics and supply chain systems such as purchasing, storage, production, inventory, and transportation systems. Analysis of interactions and trade-offs among these components using mathematical models and quantitative techniques.
Prerequisite: INDR 501 or consent of the instructor.
INDR 503 Stochastic Models and Their Applications (3 redits) The basic theory of the Poisson process, renewal processes, Markov chains in discrete and continuous time, as well as Brownian motion and random walks are developed. Applications of these stochastic processes are emphasized by examples, which are drawn from inventory and queueing theory, reliability and replacement theory, finance, population dynamics and other biological models.
Prerequisite:An undergraduate level statistics course or consent of the instructor.
INDR 504 Advanced Engineering Materials Manufacturing (3 redits) Advanced Engineering Material Manufacturing Processes will be studied for (i) metals and (ii) plastics and composites. Material removal, addition, and change of form processes will be studied for metals. In the plastics and composites part, similarities/differences, advantages/disadvantages, and proper selection of manufacturing processes such as Injection Molding, Compression Molding, Extrusion, Sheet Forming, Tow Placement, Pultrusion, Liquid Molding, Filament Winding, Pultrusion and Autoclave Processing will be illustrated with applications from aerospace, automotive, biomedical, sporting goods and civil infrastructure industries. Issues and their solutions with in-site sensing and on- and off-line control will be studied with examples.
Prerequisite: INDR 505 or consent of the instructor.
INDR 505 Manufacturing Systems (3 redits) This course will cover the basic concepts and techniques in hierarchical design, planning, and control of manufacturing systems. Topics include flow line and assembly systems, group technology and cellular manufacturing, just-in-time, flexible manufacturing systems.
INDR 506 Computer Integrated Manufacturing and Automation (3 redits) This course introduces Computer Aided Design and Manufacturing (CAD/CAM) Systems, Computer Numerical Control (CNC) Machine Tools, Modern Sensors in Manufacturing, Machining Processes, Rapid Prototyping, and Fundamentals of Industrial Robotics.
INDR 508 Discrete Event Simulation (3 redits) Topics on distribution fitting and generating random numbers and random variates will be covered as well as the statistical analysis of simulation output including some well-known analysis methods and variance reduction techniques. Recent developments in the area will also be discussed.
Prerequisite: INDR 503 or consent of the instructor.
INDR 520 Network Models and Optimization (3 redits) Network flow models and optimization problems. Algorithms and applications. Minimum spanning tree problem. Shortest path problems. Maximum flow problems, minimum cuts in undirected graphs and cut-trees. The minimum cost network flow problem. Matching problems. Generalized flows. Multicommodity flows and solution by Lagrangean relaxation, column generation and Dantzig-Wolfe decomposition. Network design problems including the Steiner tree problem and the multicommodity capacitated network design problem; formulations, branch-and-cut approaches and approximation algorithms.
Prerequisite: INDR 262 or consent of instructor.
INDR 530 Decision Analysis (3 redits) Tools, techniques, and skills needed to analyze decision-making problems characterized by uncertainty, risk, and conflicting objectives. Methods for structuring and modeling decision problems and applications to problems in a variety of managerial decision-making contexts. Structuring decision problems: Decision trees, model building, solution methods and sensitivity analysis; Bayes' rule, the value of information and using decision analysis software. Uncertainty and its measurement: Probability assessment. Utility Theory: Risk attitudes, single- and multi-attribute utility theory, and risk management. Decision making with multiple objectives.
Prerequisite: ENG 200 or consent of instructor.
INDR 551 Advanced Optimization Methods (3 redits) Combinatorial optimization, structure of integer programs, pure integer and mixed integer programming problems, branch and bound methods, cutting plane and polyhedral approach, convexity, local and global optima, Newton-type, and conjugate gradient methods for unconstrained optimization, Karush-Kuhn-Tucker conditions for optimality, algorithms for constrained nonlinear programming problems, applications in combinatorial and nonlinear optimization.
Prerequisite: INDR 501 or consent of the instructor.
INDR 553 Advanced Stochastic Processes (3 redits) Brief review of basic processes like Poisson, Markov and renewal processes; Markov renewal processes and theory, regenerative and semi-regenerative processes; random walk, Wiener process and Brownian motion; martingales; stochastic differential equations and integrals; applications in queueing, inventory, reliability and financial systems.
Prerequisite: INDR 503 or consent of the instructor.
INDR 566 Scheduling (3 credits) Introduction to scheduling: examples of scheduling problems, role of scheduling, terminology, concepts, classifications; solution methods: enumerative methods, heuristic and approximation algorithms; single machine completion time, lateness and tardiness models; single machine sequence dependent setup models; parallel machine models; flow-shop models; flexible flow-shop models; job-shop models; shifting bottleneck heuristic; open-shop models; models in computer systems; survey of other scheduling problems; advanced concepts.
Prerequisite: Consent of instructor.
INDR 568 Heuristic Methods (3 Credits) Constructive heuristics; improving heuristics; metaheuristics: simulated annealing, genetic algorithms, tabu search, scatter search, path relinking, ant colony optimization, variable neighborhood search, and their hybrids; heuristic methods based on relaxation and decomposition; applications: routing, scheduling, cutting and packing, inventory and production management, location, assignment of resources, bioinformatics, and telecommunications. Prerequisite: INDR 501 or consent of the instructor.
INDR 578 Advanced Models in Supply Chain Management (3 redits) Dynamic inventory policies for single-stage inventory systems: concepts of optimality and optimal policies. Multi-Echelon Systems: uncapacitated models and optimal policies, capacitated models: different control mechanisms. Multiple locations and multiple items: inventory and capacity allocation. Decentralized control and the effects of competition on the supply chain: coordination and contracting issues.
Prerequisite: INDR 503, INDR 505 or consent of the instructor.
OPSM 502 Operations Management (3 redits) Fundamental decisions and tradeoffs in control of a firm’s operations: obtaining and controlling the flow of materilas through a production facility and distributing them to customers. Four modules: process fundamentals; cross functional integration, coordination, and control; improving the perfermance of productive systems; and competing through technology and operations.
OPSM 602 Advanced Models in Supply Chain Management (3 redits) Dynamic inventory policies for single-stage inventory systems: concepts of optimality and optimal policies. Multi-Echelon Systems: uncapacitated models and optimal policies, capacitated models: different control mechanisms. Multiple locations and multiple items: inventory and capacity allocation. Decentralized control and the effects of competition on the supply chain: coordination and contracting issues.
Prerequisite: INDR 503, INDR505 or consent of the instructor.
QMBU 632 Introduction to Management Science (3 redits) Fundamental quantitative methods used in business decision-making: mathematical programming, stochastic modeling, and simulation, with emphasis on formulation, analysis, and implementation.
OPSM 637 Operations Strategy (3 redits) Coordination of marketing, operations, and finance functions within a framework designed to meet the competitive requirements of the marketplace. Interface issues between corporate strategy and the management of the operations function.
OPSM 638 Supply Chain Management (3 redits) Process-oriented, integrated approach to procuring, producing, and delivering products and services to customers. Strategic and operational issues, such as sharing information and joint planning, reduction in supplier base, channel-wide inventory management, channel-wide total cost approach, supply chain competitiveness, compatibility of corporate philosophies.
OPSM 639 Project Management (3 redits) Managerial skills and competencies for project management, defining a project, setting goals, defining the scope, planning the activities, managing the resources, organizing for project management, implementing the project, monitoring and controlling, and closing out the project.
OPSM 650 Selected Topics in Operations Management(3 redits) Topics will be announced when offered.
MGIS 501 Introduction to Management Information Systems (3 redits) The technological and institutional factors that influence the choice of hardware and software components of a management information system; introduction to systems analysis through teamwork on an actual business analysis problem.
MGIS 641 Database Management Systems (3 redits) Database concepts for management, planning, and conceptual design, design and administration, classical systems, relational and distributed systems, implementation of database management systems.
MGIS 650 Selected Topics in Management Information Systems (3 redits) Topics will be announced when offered.
INDR/ OPSM 590 Seminar A series of lectures given by faculty or outside speakers. Participating students must also make presentations during the semester.
INDR 591 Project Independent research towards M.S. degree without thesis option.
INDR 595 M.S. Thesis Independent research towards M.S. degree with thesis option.
TEAC 500 Teaching Experience Provides hands-on teaching experience to graduate students in undergraduate courses. Reinforces students' understanding of basic concepts and allows them to communicate and apply their knowledge of the subject matter.
ENGL 500 Graduate Writing This is a writing course specifically designed to improve academic writing skills as well as critical reading and thinking. The course objectives will be met through extensive reading, writing and discussion both in and out of class. Student performance will be assessed and graded by Satisfactory/Unsatisfactory