4000

Courses attended by persons who joined the Master´s Degree Program in Industrial Engineering, who must revisit certain academic concepts to study the mandatory program subjects.

The purpose of Thesis I is to introduce students to the theoretical framework of their thesis and advances in the approach of the solutions to the selected problem. To enroll to this subject, the student must have a counselor (a task to be achieved by the student), must complete the corresponding form and obtain the Coordination permit.

This seminar offers the master´s degree program students the conceptual and methodological elements to formulate their preliminary graduation thesis project from a research question. It also develops certain strategies to write, review and present the preliminary project.   The Research Seminar includes Industrial Engineering Seminars Cycle (CSII), which is a forum where students (undergraduate, master´s degree and doctorate) and professors interact and share research results. CSII also includes guest professors. There are three types of seminars: minor, master and activity reports of the research groups.

During the second Thesis semester, students will devote all their time to research. Should more time be required, a maximum term of one semester will be granted (grace semester). To enroll in Graduation Thesis II, students must have passed Thesis I and must have completed the corresponding form.

The student enrolled in this course is finalizing the research project  and presents the results to a group of experts.

The purpose of this course is to help students acquire the knowledge and tools to allow their placement in the basic probabilistic model concepts, in order to succeed in the courses subsequently required.

The purpose of this course is to help students acquire the knowledge and tools to allow their placement in the basic probabilistic model concepts, in order to succeed in the courses subsequently required.

The purpose of this course is to teach students to estimate the parameters of linear models in order to quantify ratios between variables, compare theories and effects of various factors, prepare forecasts and build behavior models. The most commonly used and popular techniques in the statistical field are analyzed, the application of which covers a wide variety of natural science and social areas to tackle, in more detail, problems related to decision theory, market research, quality control, management, project evaluation, finance, etc. The main subjects studied include multiple regression analysis and design models for variance analysis. Specialized software, such as SAS, SPSS, MATLAB and Econometric Views are used.

The course presents various optimization subjects, such as lineal programming, restricted optimization, whole and combinatory programming. Among these, some specific subjects include convex analysis, simplex model, degeneration, cycling, duality, sensitivity analysis, Karush-Kuhn-Tucker conditions, inner point algorithm, single-modularity, Branch and Bound method, implicit numbering, decomposition, matroids and non-lineal programming elements.

The purpose of this course is to train students on how to manage useful statistical techniques to explore and model data collections, characterized by a large number of variables taken from a wide set of observations. The course content includes multi-varied measuring comparison, case study and tests of hypothesis of major interest, profiles and contrasts, analysis of main components, factorial analysis, correspondence analysis, and other segmentation methods, Conjoint Analysis, case presentation and analysis using the techniques and models studied.

A balanced consideration of factors influencing future events and perfect familiarity with the various existing forecasting techniques are required in order to properly make a forecast. The course’s main objective is studying techniques and tools which allow identifying the behavior or pattern of a time series (observations or data taken periodically throughout time) in order to predict its future behavior. The course has three modules: in the first one a review is made of the fundamentals of traditional forecasting methods for trend processes and for processes exhibiting some sort of seasonal pattern, in the second, ARIMA models and their GARCH extensions and transfer models are presented, the third module alternates sessions presenting cases illustrating the construction, identification and validation phases of the model with workshop sessions making extensive use of computers. Prerequisite: Linear Models or solid knowledge of statistics.

The objective of this course is to introduce modeling and optimization of stochastic systems with IIND-2104 as the prerequisite. The course will focus on formulation and analysis of industrial systems with stochastic components using analytical techniques such as continuous-time Markov chains, queues with batching, priorities, balking, open/closed queuing networks among others. Stochastic optimization techniques such as Markov Decision Processes and Stochastic Dynamic Programming will be discussed to conclude the course.    

Students who have demonstrated responsibility and interest for a specific area are offered this alternative, which gives them the opportunity to gain further knowledge of the area topics, under the guidance of a department professor. To take this course, the authorization of the academic coordination is required.

The general objective of this course is to give the students an integral view of systems and general industrial equipment for energy conversion (be it supply or consumption equipment). The student will use the general engineering fundamentals to analyze the performance of such systems and prime movers. The course is oriented towards the knowledge development for analysis, design of energy conversion systems, frequently used in industry.

This course introduces common technological methods involved in manufacturing and processing of products, made with different engineering materials. In order to achieve these objectives, the student will attend tutorials and discussion classes and will make guided investigations in the commercial and industrial environment. He also will participate in laboratory practice guided by the professor, class monitor or specialized technicians.

The course offers an analysis of manufacturing systems from the mathematical techniques standpoint (optimization and control) used to model and analyze the manufacturing production systems. It applies flow-shop and job-shop type systems and basic programming algorithms. Furthermore, it addresses topics such as flexible manufacturing, group technology, quadratic assignment methods and task sequencing, classic material management problems, queue and simulation models.

Students who have demonstrated responsibility and interest for a specific area are offered this alternative, which gives them the opportunity to gain further knowledge of the area topics, under the guidance of a department professor. To take this course, the authorization of the academic coordination is required.

This course provides the student with the tools required for the design of experiments and analysis of resulting data applied to solving engineering problems. The purpose of the course is to learn how to plan, design and carried out experiments efficiently and effectively, as well as to analyze the data produced by the experiment to reach objective conclusions under conditions of uncertainty. Concepts developed under this course can be applied in the execution of engineering processes, including design and development of new products, process development and improvement of manufacturing processes.

In this course, the student will learn how to use statistical tools and methods to facilitate the variables of processes observation, and their relations to find factors responsible for the low quality of products and industrial processes. The main objective of topics discussed is to promote a culture for continuous improvement through the implementation of a model based on defining, measuring, analyzing, improving and controlling.

This purpose of this course is to lead the student in the application of advanced statistical methodologies to determine the conditions under which an industrial process must operate. Topics are related to quality management, data gathering, analysis of information, monitoring using control graphics, process capacity analysis, auto-correlation, among others.

The purpose of this course is to provide conceptual and methodological tools to understand, analyze, diagnose and redesign social organizations, and to promote the development of a self-management culture using the systemic approach, particularly the organizational cybernetics approach.

This course seeks to explore new simple means - mechanisms and models - to interpret and work in complex systems through the use of tools, the design of which is based on cellular automaton and physical models, among others. Tools will be used and applied in Mathematics. To enroll in this course, students are expected to be competent in Physics 3, Differential Equations and Probability content. Compliance with these pre-requirements is subjected to the consideration and responsibility of each student. A master´s degree program student must observe the programs of these courses to establish his compliance with the pre-requirements.

This seminar contributes to the conflict understanding and to the negotiation processes and, this way, it allows moving ahead with the organizational process knowledge. The Organizational Metaphor methodology proposed by Morgan (1986) is followed, and work is carried out to propose an organization image seen as negotiation systems. In addition to the techniques and analytic skills that allow finding optimum solutions to problems, we must base ourselves on the fact that negotiation is the art and science implies ensuring agreements between two or more independent parties, therefore, the purpose of this course is to develop skills that allow negotiating the implementation of such solutions (or its subsequent modification, in accordance with the situation diagnosis).

This course covers decision-making in negotiation processes and general dispute resolution. The course explains how the different decision-making techniques can be used in dispute resolution processes. Special emphasis is made on how the use of different rationalities can lead to different negotiation schemes. Throughout the course, the student designs and conducts a decision-making research in dispute resolution processes.

This course is part of a research project on social networks. The scope used to work in this research is the complex network theory. Attendees to this course are invited to actively participate in the research. The situations studied refer to the economic dynamics of the world system, to the development of urban spaces and to mechanisms of trust construction. The course pretends to present conditions that enable systemic setup relations among subjects, locations and countries. Those registered in the course may attend to the presentations of lecturers throughout the investigation, as well as to workshops, colloquium, seminars and other activities related and announced in advance.

Basic concepts of algorithms design and analysis. At the end of the course the student should be able to apply developing techniques like divide and conquer, dynamic programming, and general search algorithmic, analyzing temporal and spatial complexity. Practical limitations for algorithmic solutions (e.g. NP-completeness) are studied at an introductory level.

The course is a research seminar for addressing basic questions and issues on engineering, its difference with science,  engineering knowledge, methods & engineering ethics. and provides tools and heuristics for coping with the challlenges of developing and applying engineering in and for social (human) systems.

This course aims to build a solid understanding of social and organizational systems and their dynamics through the application of agent-based computational approaches. Several key aspects are presented in the course, such as the emergence of cooperation and conflict in organizations, socio-technical system design, problems of aggregation in social systems, and organizational adaptation.

In this course students develop skills and abilities in their capability to analyze, structure and solve problems of decision making under risk and uncertainty, taking into account one or multiple decision criteria. In particular, as a result of the course the student must be able to: 

1) Identify and structuring a strategic type in a company decision problem and designing a methodology to solve it, using decision models appropriate to do so. 

2) Identify situations in which can make use of some of the appropriate methodologies for analysis of decision under uncertainty, with an emphasis on the importance of using structured methodologies and tools appropriate to support rational decision making in organizations. 

3) Build models for the analysis of decisions that involve risk and uncertainty.

4) Usecomputational tools best known in the field of decision analysis, which should be applied in home works, and in the development of their final project of the course.

The Financial Risk Management course is an advanced finance course that assumes that students have had a minimum exposure to basic micro-economy and macro-economy concepts, particularly in those aspects pertaining to utility functions, risk aversion and financial market balance. On the other hand, concepts on the assumption of basic statistics, finance and mathematics tools knowledge, particular intensive work will be conducted with models and tools that imply a minimum knowledge of concepts, such as random variables, statistical moments of first and second order, linear regression and time series, as well as the basic calculus and lineal algebra concepts. The course approach is clearly quantitative, with respect to what is currently known as "finance in continuous time " however, discrete binomial tree models will also be intensively used. However, the course is not an stochastic calculus course in finance, and neither a derivatives course.