Engineering Management Master's Program (without Thesis)

Mathematical algorithms: random numbers, polynomials, matrices, integration. Sorting and searching. String processing. Geometric algorithms. Graph algorithms: Connectivity, weighted and directed graphs, network flow. Dynamic programming. Algorithms will be studied using object-oriented approach as much as possible.

A survey of mathematical and numerical methods used in IE and OR related studies presented in a unified structure based upon the theory of computation with special emphasis given to the development of computer codes and design of database.

2 The effects of environmental conditions (illumination, climate, noise, and motion) on human performance, effects of control-display design, shift-work, human error, accidents and safety, research projects either in the human factors laboratory or in a real field.

This course is designed to introduce the student with the principles of safety and health hazards in industrial environment. It provides students with fundamentals of measurement, evaluation, regulation, and control of hazardous conditions, toxic substances, physical agents, and dangerous processes in the industrial environment. Skills development in record keeping, risk assessment and accident cause analysis will also be emphasized. This course will prepare the student for workplace safety and management.

Convex analysis; optimality conditions; methods for unconstrained and constrained nonlinear optimization; linear programming and linear goal programming. Quadratic programming; geometric programming; dynamic programming; multi-objective optimization methods; integer programming.

Geometry of LP and Simplex Method. Duality and its implications. Sensitivity. Simplex forms; revised Simplex, dual Simplex etc. LU factorization. Transportation and transshipment problems, assignment problem. Decomposition methods. Networks. Problems with upper bounds. Numerical stability and computational efficiency. Karmarkar's method.

Axiomatic approach to probability; conditional probability; random variable. Commonly used discrete and continuous distributions. Expectation of a random variable; jointly distributed random variables; marginal and conditional distributions; independent random variables. Multinomial and multivariate normal distributions. Functions of random variables, moments, conditional expectation, m.g.f. and p.g.f., Markov inequality, law of large numbers, central limit theorem. Discrete-time Markov chains, Kolmogorov-Chapman equations, classification of states, steady-state probabilities. Applications from different areas.

Review of conditional probability and conditional expectation. Basic definitions. Homogenous and non-homogenous Poisson processes, generation of random numbers from Poisson processes, compound Poisson processes, birth-death processes. Markov chains and pure jump processes. Renewal theory and applications. Markov-renewal processes. Applications to queuing, replacement, and inventory problems. Selected topics from stationary processes, r th order Markov chains, time series as stochastic processes.

Analysis of birth and death processes. Development of elements of queuing theory. Single and multiple server queues for Markovian and non-Markovian arrival and service time distributions (M/M/1, M/M/c, G/M/c, M/G/1, PH/PH/1). Bulk arrival and service systems. Networks of Markovian queues. Lindley's equation for the G/G/1 queue. Applications of queuing theory in manufacturing and computer systems.

Network representation and terminology. Network flow problems such as shortest path, minimal spanning tree and maximal flow problems. Graph theory.

The aim of this course is to introduce the fundamental concepts and methods of integer programming, i.e. optimization if a part of variables must be integer valued. There are many integer programming models in industrial engineering, e.g. in production control, scheduling, location theory. The course covers the basic solution methods: dynamic programming, implicit enumeration, branch and bound, cutting plane and polyhedral approach. It also gives an introduction to the most important heuristic methods as the use of Lagrange multipliers and local search. Some problems of industrial engineering have special combinatorial structure. Traveling Salesperson Problem (TSP) and optimization in graphs are of this type discussed here. The ultimate aim of the course is to provide tools for students dealing with integer programming models for developing their own algorithms for their special problems.

Local and global optima. Newton-type, quasi-Newton, and conjugate gradient methods for unconstrained optimization. Kuhn-Tucker theory and Lagrangean duality. Algorithms for linearly constrained optimization, including steepest ascent and reduced gradient methods with applications to linear and quadratic programming. Interior point methods. Non-linearly constrained optimization including penalty and barrier function methods, reduced and projected gradient methods, Lagrangean methods. Computer implementation.

In practice Multi-Criteria Decision Making (MCDM) methods are very popular in addressing complex problems involving multiple and typically conflicting criteria as well as several stakeholders or decision makers with different preferences with respect to the evaluation criteria. This course aims at training students in the field of MCDM with emphasis on rating, ranking and classification problems and methods with applications in business. Quantitative decision analysis. Multi-criteria benefit and utility theories. Decision making under uncertainty. Decision tree. Structuring of objectives and value hierarchies, and determination of value functions. Multi-objective decision making with mutually exclusive alternatives. Multi-objective ranking and classification. Multi-objective mathematical programming. Interactive methods and applications.

Bayesian decision models; decision trees; value of information; utility theory, use of judgmental probability, study of strategies; economics of sampling; risk sharing and decisions; implementation of decision models.

The meaning of investment process in general and for creating systems for products and services in particular. Classification of investment decision problems with respect to context and the precision of informational support, such as certainty, risk, and uncertainty. A general mathematical structure for modeling investment decisions; deterministic, stochastic, combinatorial, sequential, and dynamic investment decision models and optimization techniques used for their solutions. The evaluation of investment alternatives and derivation of value measures, types of risk taking.

This course is designed to enable students to understand and conceptualize basics of modern financial markets in the form of mathematical models. Several models of risk free assets and dynamics of risky assets will be discussed. Optimal portfolio management in risky environments will be analyzed. Call and put options of securities in discrete and continuous time settings will be explained. This part includes the famous work of Black-Scholes.

Analysis of some specific problem areas within the context of planning and scheduling of production activities. Definition, formulation and available solution procedures for aggregate planning, lot sizing, material requirements planning, cutting stock, line balancing, single processor scheduling, multi processor scheduling problems.

Study of inventory systems. Deterministic and stochastic models. Fixed versus variable reorder intervals. Dynamic and multistage models. Selection of optimal inventory policies for single and multi-item dynamic inventory models, with convex and concave cost functions, known and uncertain requirements. Myopic policies. Multi-echelon models. Heuristic algorithms.

Study of inventory systems. Deterministic and stochastic models. Fixed versus variable reorder intervals. Dynamic and multistage models. Selection of optimal inventory policies for single and multi-item dynamic inventory models, with convex and concave cost functions, known and uncertain requirements. Myopic policies. Multi-echelon models. Heuristic algorithms.

In this course, students are expected to study critically at least three papers published in the fields of aggregate planning, lot sizing, material requirements planning, continuous and periodic review inventory models, cutting stock, line balancing, single processor scheduling, multi processor scheduling problems. The papers will be selected from the recent issues of periodicals in those fields that are scanned by SCI. Each student is expected to discuss the papers in class and to prepare a literature survey on the subject.

Introduction to CIM systems. Computer process interfacing. Computer control. Industrial robots. Flexible manufacturing systems. CIM systems and their selection criteria.

Supply chain management; new product development; management and control of purchasing and logistics management systems. Strategic orientation toward the design and development of the supply chain for purchasing, materials, and logistics systems. Total Quality Management to assess and assure customer satisfaction. Global strategies. Expert systems for continuous improvement of the supply chain.

Single or multiple facilities location in the plane with minimum or minimax criteria. Discrete or continuous layout optimization. Single facility network location. Applications in public service, production, distribution, warehousing, emergency service, flexible manufacturing.

The design and performance issues in production, transfer lines, production/inventory systems, network of production/inventory systems, and flexible manufacturing systems. Phase type processing times, failures and service completion processes. Buffering and blocking issues. Decomposition methods. Control policies in pure inventory and production/inventory systems.

Major technological aspects of process and manufacturing industries in relation to their management, selection and implementation issues of new technologies, managing technological and the related organizational changes.

Analysis of linear continuous systems; controllability, observability, and stability; applications to physical, ecological, and socio-economic systems; control systems; introduction to optimal control.

The design and analysis of simulation models. The use of simulation for estimation, comparison of policies, and optimization. Variance estimation techniques including the regenerative methods, time series methods, and batch means. Variance reduction. Statistical analysis of output of simulations, applications to modeling stochastic systems in computer science, engineering, and operations research.

The simple comparative experiments, experiments with a single factor, fixed effect and random effect models, model adequacy checking, choice of sample size, randomized blocks and latin squares design, incomplete block design, factorial designs, rules for sums of squares and expected mean squares, fractional factorial designs and regression analysis. Moreover, the statistical package for social sciences (SPSS) will be introduced.

Sampling distributions; point estimation; measures of goodness estimators; methods of estimation; sample size determination; confidence intervals; sufficient statistics; Rao-Blackwell theorem; hypothesis testing. Errors of type I & II; power of a test; p-values; Neyman-Pearson theory; likelihood ratio test; some commonly used tests for means, variances and for ratio of variance etc.; method of least squares; curve fitting; regression analysis; some commonly used non-parametric tests for randomness, independence etc. Goodness-of-fit tests; sequential tests of hypothesis; use of order statistics to find statistical intervals etc., use of computer packages.

This course is designed to introduce a conceptual and practical notion of advanced quality control in engineering. It also provides students with methods and philosophy of statistical process control. The course contents include introduction to advanced quality control and improvement concepts in production processes, control charts for variable and attributes, cumulative sum control charts, economic design of control charts, fractional factorial experiments for process design, process optimization with designed experiments, advanced acceptance sampling techniques and lot-by-lot acceptance sampling for attributes.

Planning and forecasting; measures of forecast errors; correlation, covariances, autocorrelations and autocorrelation function and their use pattern recognition; smoothing methods; decomposition methods and seasonal indices. Methods for trend and seasonal patterns including differences, double smoothing, Holt & Winter methods; Fourier series forecast analysis; Box-Jenkins ARIMA methods and their applications. ARIMA intervention analysis and transfer functions; econometric methods; multiple regression; cyclic methods. Technological forecasting; use of neural networks, expert systems and genetic algorithm in forecasting. Mini-case studies and software packages.

Reliability and maintainability. Basic reliability models. Maintainability. Availability. Reliability testing. Implementation and case studies.

Recent advances in selected topics in Industrial Engineering will be covered. The topics and the contents of these courses will depend on the course instructor and will be announced in the beginning of each semester.

In this course, the student is expected to work on a well-defined short project, covering a specific area of his/her choice. Alternatively, the work could cover any specific industrial problem and its solution or could even be a literature survey of a research topic. The end result will be a compilation of a final technical report of the study and presentation before the jury.

The aim of the course is to provide the students with a basic understanding of the process of identifying, measuring, and analyzing information in pursuit of an organization's goals. This course aims at enabling the students to understand how to use the financial information provided by financial and cost accounting systems in cost management, profit planning, and budgeting decisions.

This course provides a complete overview of asset and liability management (ALM). It focuses on fundamental principles and applications to financial risk management in any financial institution. It also concentrates on a solid, practical basis for currency and international funds risk issues by linking them with ALM.

This course covers statistical techniques such as descriptve analysis of data, estimation, correlation and regression analyses along with financial data and financial applications. It covers a comprehensive application of cross-section nd time series financial data by also using MS Excel, SPSS and EVIEWS softwares.

The primary aim of the course is to develop the ability of students to think like an economist. Thinking like an economist makes use of basic concepts such as opportunity cost, rational choice, individual preferences, and scarcity and marginal costs. It also requires a thorough understanding of partial equilibrium analysis in a particular market and the strengths and weaknesses of a market economy. The course will help the students to deepen their understanding of the foundations of economic theory and also motivate them to study further in areas of applied economics.

The aim of this course is to provide students with a sound theory in explaining the short-run fluctuations as well as the long-run relationships and growth trends in macroeconomics. The course also applies these theoretical models to analytic thinking and provides empirical studies which test the theories taught.

This course aims at giving students basic understanding of econometrics theories and applying econometric techniques of regression analysis. Numerous applications are examined to achieve this goal. Emphasis is placed on the classical linear regression model, least-squares estimation, hypothesis testing, and modeling building. Various econometric models are adopted to analyze practical economic problems and make forecasts. Furthermore, in this course students are trained to use computer statistics software (i.e., EViews).

Alternative concepts in exchange rate analysis. Demand – supply analysis of foreign exchange markets. Alternative Exchange Rate Regimes. Purchasing Power Parity Theorem. Real Exchange Rate. Stability of Foreign Exchange Markets. Monetary Theory of Exchange Rate. Uncovered and covered Interest Rate Parity Theorem.

The course concentrates basically on the introduction to the basic econometrics that covers regression analyses, detection of problems and their solutions in regression models, hypothesis testing procedures, and introducing basic time series analysis that covers stationarity and cointegration.

The last decade has been one of rapid, profound, and ongoing change in the investment industry. The main objective of the course is to give students an intermediate understanding of capital markets and financial instruments. We will focus on three modules which are equity markets, fixed income markets, and derivative markets (options and futures). The course is designed in line with the subject topics covered in the Chartered Financial Analysts (CFA) Exam (Level I).

This course is devoted to the techniques of cost-benefit analysis of investment projects. The course covers both the analytical techniques and as well as their practical applications in decision-making. The course topics will cover financial modeling, alternative investment criteria, and maintaining consistency between real and nominal prices, inflation rates, exchange rates and interest rates. It will also cover how to determine the optimal scale and timing of investments and how to construct income statements and balance sheets from cash flow projections.

This course will teach students how to analyze and manage the risks of major investment projects through project financing techniques. The risk management techniques will include the study of modern international project financing and security arrangements. The design and application of real options will be applied in the evaluation of investment projects. The course will also cover both the theory as well as applications of the theories to various projects. The students will be required to complete a number of computer based assignments.

The last decade has been one of rapid, profound, and ongoing change in the investment industry. This course is designed for students at the doctoral level giving students an understanding of capital markets theory and empirical evidence. The course lays the foundation for future research in investments and teaching in the field. We focus on three modules which are portfolio theory, capital market equilibrium and market efficiency.

This course is designed for students at the doctoral level giving students an understanding of corporate finance theory and empirical evidence. The course puts significant emphasis on the interactions between capital markets and the value of the underlying real assets. The course lays the foundation for future research in corporate finance and teaching in the field. We focus on three modules which are capital structure, dividend policy, raising capital and one sub-module focusing on event studies as an empirical corporate finance method.

The primary aim of the course is to develop the ability of students to think like an economist and apply it to both problems of economics and finance. The student will learn how to use the basic concepts such as opportunity cost, rational choice, individual preferences, and scarcity and marginal costs. It also requires a thorough understanding of partial equilibrium analysis in a particular market and the strengths and weaknesses of a market economy. The course will help the students to deepen their understanding of the foundations of economic and financial theory and also motivate them to study further in the applied areas of these fields.

The course mainly aims to examine the exchange rate policies and issues of exchange rate determination with fixed, flexible and sticky prices, international monetary system, previous and contemporary exchange rate regimes and policies starting with gold standard, and then fixed and fluctuating exchange rates. The case studies will be provided for real life applications throughout the chapters.

This course will cover the components of an integrated investment appraisal analysis. It will initially introduce basic economics aspects that are used in capital budgeting for a comprehensive analysis. The course will also examine how the economic analysis fits into the financial, stakeholder and risk components that are part of an integrated investment appraisal analysis. Applications to number of infrastructure projects such as road, electricity and water will also be studied as well as education. This course will cover both theoretical as well as applied aspects of advanced capital budgeting.

Managerial Economics is the application of economic theory and methodology to managerial decision making problems within various organizational settings such as a firm or a government agency.  The emphasis in this course will be on demand analysis and estimation, production and cost analysis under different market conditions, forecasting and decision making under uncertainty. Students taking this course are expected to have had some exposure to economics and be comfortable with basic algebra. Some knowledge of calculus would also be helpful although not necessary.

This course provides a solid foundation in managerial aspects of information systems and technology as well as providing a thorough exposure to a wide range of IT applications in business and management, including electronic commerce. The emphasis throughout the course is on making use of information technology to improve organizational effectiveness and business performance. Programming and software skills are neither required nor taught. The course focuses on the role of information within organizations and analyses how technologies such as artificial intelligence and decision support systems help organizations manage their knowledge assets. The course also deals with electronic commerce and looks at the rapidly expanding set of business opportunities and risks posed by the advances in telecommunications and internet infrastructure. Additionally, the course focuses upon the relationship between strategy and IT and looks at financial aspects of IT investments. The course also touches upon managerial aspects of international information systems.

Short-Run fluctuations vs. Long-Run growth of an Economy. Gross Domestic Product. Keynesian consumption and saving functions. Determinants of investment and net exports. Determination of equilibrium level of income in simple Keynesian model and the effects of Fiscal policy and Random shocks on macroeconomic equilibrium based on income-expenditure approach. Injection –Leakages approach to the analysis of equilibrium in simple Keynesian model. IS-LM model of an open economy and the effects of Monetary and Fiscal policy and Random shocks.

The aim of the course is to analyze the primary issues in modern corporate financial theory and practice. The focus will be on the corporate practice (corporate financial policies) that will create value and maximize the wealth of shareholders. We will be concerned with what financial managers do and why, and how the theory of finance explains.

The target of the course is the analysis that concerns the major aspects of strategic corporate elements. Naturally, the framework of the system is constructed upon the business environment and deals with the internal analysis. The method of strategic choices and optimal corporate decisions are promoted. Additionally, the simulations and dramatized team works are applied to achieve students' perceptions and motives concerning the post business life.

This course considers the objectives and strategies of international business in the context of global competition. It equips managers with a comprehensive framework to formulate strategies in the global marketplace. While accessible to students who have not yet taken Microeconomic Analysis, this course emphasizes economic analysis of the forces driving international business. The course covers competitive advantage, competitive strategies, alternative modes of market entry, including import and export through intermediaries, contracting with suppliers and distributors, and foreign direct investment (FDI). Case studies are used throughout to illustrate the basic principles of multinational business management and strategy.

The field of organizational behavior is oriented toward developing a better understanding of human behavior and using that knowledge to help people be more productive and satisfied in organizational settings. The primary values characterizing this field include (1) an emphasis on establishing cause-and-effect relationships, (2) a commitment to change, (3) a humanistic concern for people, (4) a concern for organizational effectiveness, and (5) a reliance on empirical research and the scientific method. The purpose of this course is to provide students with an understanding of the field of organizational behavior and the various research strategies that it employs. Students will receive extensive exposure to the scientific method, various research methodologies, and the use of academic research journals.

The course is aimed at providing the graduate students with an understanding of marketing research. The course objective is to teach "information gathering activity" that is intended to guide strategic or operational marketing decisions about target markets, competitive strategies, product, price, place (distribution) or promotion. Another objective of this course is to discuss the nature and scope of marketing research and explain its role in decision support systems. The students will understand the nature and scope of marketing research and its role in designing and implementing successful marketing programs.

To further disseminate and develop the knowledge and skills in the essential aspects of marketing management, marketing strategy, and emerging New Economy marketing applications, with a focus on the development and execution of programs, audits, and plans. This course is concerned with the development, evaluation, and implementation of marketing management in complex environments. The course deals primarily with an in-depth analysis of a variety of concepts, theories, facts, analytical procedures, techniques, and models.

The course is aimed at providing the graduate students with an understanding of marketing research. The course objective is to teach "information gathering activity" that is intended to guide strategic or operational marketing decisions about target markets, competitive strategies, product, price, place (distribution) or promotion. Another objective of this course is to discuss the nature and scope of marketing research and explain its role in decision support systems. The students will understand the nature and scope of marketing research and its role in designing and implementing successful marketing programs.

The primary objective of this course is to help prepare students to function as effective managers in a service economy. This course will make students more aware of the nature and characteristics of services, more knowledgeable about the way services need to be marketed due to their intangible core and more conscious of service quality, the foundation of services marketing. Students will also have a better understanding of the success factors in services marketing.