Sin-In

This course will present the fundamental elements of probability theory. The topics to be covered are: Development of distribution families (discrete and continuous), Random variable algebra, limit, theorems, Large number Theorems.

This course uses probability theory to describe estimation concepts, sample distributions, estimation methods, Sampling distributions, properties of estimators, point and interval estimation, parametric hypothesis testing, introduction to multivariate and nonparametric statistics. The objective is to present in a unified and formal way the foundations of statistical inference. It is assumed that the student has fundamental knowledge in: Probability theory, limit theorems, convergence properties and the large number theory

Rohatgi, V.K., An Introduction to Probability Theory and Mathematical Statistics, Wiley, 1976.
Hogg, R.V., A.T. Craig, Introduction to Mathematical Statistics, MacMillan Publishing Co., 1978.
J.K. Lindsney, Parametric Statistical Inference, Clarendon Press.

Ph.D. or Doctoral degree in Statistics, Mathematics, Operations Research, Industrial Engineering or Management

Introduction of simulation and simulation studies, Organization of simulation languages, Statistical Aspects including variable generation, input and output analysis, random number and variate generation, and model validation. Experimental design of simulation studies, and variance reduction. This course will involve an extensive use of computers.

This course covers the fundamentals of linear programming. It is intended for Ph. D. students in their first year. It is assumed that students have an elementary introduction to linear programming modeling and the simplex method and some knowledge of linear algebra. The topics to be covered are: The linear programming problem, The Simplex algorithm, Convergence, Speed, Duality and the related theorems, The revised simplex method, Sensitivity analysis, Selected applications . Introduction to large scale problems.

PhD or Doctoral degree in: Industrial Engineering, Operations Research, or Mathematics.

Analysis and applications of experimental design. Topics include design for the comparison of two means and k means with one or more block variables, multiple level factorial and fractional factorial designs and central composite designs for response surface methods

Ph.D. or Doctoral Degree in: Industrial Engineering, Statistics, Operations Research or Mathematics.

This course is designed for the student to understand the markovian property from an stochastic process; It will develop in the student the intuition to understand the different ways in which this markovian property is manifested. During the course the students will be exposed to theoretical and practical concepts, and will develop the abstraction skill needed to detect candidate process for modeling and to be explained by the markovian property.

Discreate Markov Chains (Definitions and Examples, Computing Probabilities of Sample Path, Classifications of States, Probabilities and Means of Absortion times, Equilibrium or stationary distributions, Laws of large numbers).
Poisson Processes (Definition and Application Context, Distribution of Point Locations-Exponential, Gamma, Uniform, and Multinomial Distributions, Sum of Independent Poisson Processes, Partitions and Transitions of a Poisson Process, Compound Poisson Processes )
Renewal Process (Definition and elementary properties, Law of large numbers, Applications of the key renewal theorem, Regenerative process)
Browninan Motion
Stationary Processes.

Karlin, S. And H.M.Taylor, A first Course in Stochastic Processes, Academic Press New York.
Ross, Sheldon M. Introduction to Probability Models. USA 1997. Academic Press, sixth edition.

Ph.D. or Doctoral Degree in Mathematics, Stochastic, Industrial Engineering, Operations Research.

Foundations of the experimental models, multiple comparative experiments, adequacy measures of an experimental model, experimental stregies and factorial designs, two and three level factorial design, fractional factorial design, facotorial experiments with random factors, hierarchical designs.

Instructor Profile: Ph. D. in Industrial Engineering, Statistics or areas related.

The topics to be covered in this course are: logic and proofs, algebra of sets, real numbers system, sequences and series, Lebesgue integral, differentiation and integration, fundamental theorems of continuous functions, convergence theory of sequences, series and integrals, general theorems of partial differentiations, implicit function theorems, topology spaces, metric spaces, compact spaces, vectorial space, subspaces, linear transformations, dimension of vectorial spaces.

Bibliography:
• Bartle, R.G. and D.R. Sherbert. Introduction to Real Analysis. Third Edition. John Wiley & Sons, Inc., New York. 2000.
• Royden, H.L. Real Analysis, Third Ed. Prentice Hall. 1988.
• Strang, G. Introduction to Linear Algebra, Third Edition. Wellesley Cambridge Pr. 2003.

Professor profile: Ph. D. or Doctoral degree in: Mathematics, Operations Research, or Industrial Engineering with a solid background in mathematics.

The goal of this course is to induce the student in the research process that is required in the PhD program.
Bibliography
Several Scientific Journal papers
Professor Profile
Ph.D. or Doctoral degree in: Industrial Engineering, Operations Research, Statistics, Mathematics or related area.

The goal of this course is to discuss at group level the last papers published in the major areas of the program.

Ph.D. or Doctoral degree in Industrial Engineering, Operations Research, Statistics, Mathematics or Stochastics.

The goal of this course is to discuss at group level the last papers published in the major areas of the program.

Ph.D. or Doctoral degree in Industrial Engineering, Operations Research, Statistics, Mathematics or Stochastics.

The goal of this course is to discuss at group level the last papers published in the major areas of the program.

Ph.D. or Doctoral degree in Industrial Engineering, Operations Research, Statistics, Mathematics or Stochastics.

CLU: 3-0-12 Requisito: In5022, In5017, In4005, In4001
Equivalencia: No tiene
Programa: DII

This course covers advanced topics related to operating decisions in warehousing and inventory control. These topics are: warehouses, materials handling and demand management which includes forecasting and inventory.

Bibliography:
• Axsater, S. Inventory Control. Kluwer Academic Publishers, 2000.
• Francis, R.L., McGinnis, L.F. y White, J.A., Facility Layout and Location: an Analytical Approach, Prentice Hall, 1998.
• Silver, E., Pyke, E.A., Peterson, R., Inventory Management and Production Planning and Scheduling, Third Edition, Wiley, 1998.
• Zipkin, P.H. Foundations of Inventory Management. Irwin, McGraw-Hill. 2000.

Professor profile: Ph. D. or Doctoral degree in: Industrial Engineering, Operations Research, or Business Management with focus on Supply Chain Management.

The objective of this course is to complement what the students learned in the previous Supply Chain Courses. The topics to be covered are: mode selection, geographical information systems, routing and scheduling, multicriteria decision aid, vendor selection and outsourcing models.

Bibliography:
• Toth, P., Vigo, D. The Vehicle Routing Problem. SIAM. 2000.
• Vincke, P. Multicriteria Decision-aid, Wiley. 1992.
• Roy, B. Multicriteria Methodology for Decision-aiding, Kluwer Academic Publishers. 1996.
• Shapiro, J.F. Modeling the Supply Chain, Duxbury. 2000.
• Daganzo, C.F. Logistic Systems Analysis. Third Ed. Springer. 1999.

Professor profile: Ph. D. or Doctoral degree in: Industrial Engineering, Operations Research, or Business Management with focus on Supply Chain Management.

The objective of the course is that the students learn the fundamentals of Supply Chain Management. The topics to be covered are: concepts of Supply Chain Management, customer service, transportation, concepts of inventory, location, routing, sourcing, supply chain design, traffic management, INCOTERMS, packaging, cross-docking, vendor managed inventory, e-procurement and warehousing.

¨ Ballou, Ronald H. Business Logistics Management, 4rd edition, Prentice Hall, 1999.

¨ Bowersox, D.J., Closs, D.J. y Cooper, M.B. Supply Chain Logistics Management. McGraw-Hill, 2003.

¨ Bramel, J., and D. Simchi-Levi. The Logic of Logistics: Theory, Algorithms, and Applications for Logistics Management. New York: Springer, 1997.

¨ Chopra, S., Meindl, P. Supply Chain Management, Strategy, Planning, and Operation. Second Ed. Prentice-Hall, 2004.

¨ Simchi-Levi, D. ; P. Kaminsky ; and E. Simchi-Levi. Designing and Managing the Supply Chain. U.S.: Irwin McGraw-Hill, 2000.

Professor profile: Ph. D. or Doctoral degree in: Industrial Engineering, Operations Research, or Business Management with focus on Supply Chain Management.

This course develops technique for solving non linear programming problems. We assume that students are familiar with the basic results of linear programming. Including the simlex algorithm and the relations between primal and dual variables in linear programming problems.

Non Linear Unconstrained Optimization (Steepest descent, One dimensional optimization, Newton´s Method, Conjugate Gradient, Variable Metric Methods )
Non Linear Constrained Optimization (Kuhn Tucker Optimality Conditions, Quadratic Programming, Gradient Projection Algorithms, The Frank Wolfe Algorithm, The Column Generation Algorithm of Dantzig)
Dual Methods for Constrained Optimizations Problems (Penalty Function Methods, Barrier Function Methods, Applications for linear programming, Lagrangian duality, Multiplier Methods)
Integer Programming (Branch and Bound Procedures, Lower bounds and approximate solutions by lagrangian relaxation and solution of a dual problem)

PhD or Doctoral degree in Operations Research, Mathematics or Industrial Engineering.

This course develops technique to solve network flow problems. The topics to be covered are: Network Simplex Method, Applications of the network simplex method, Upper bounded transshipment problems, Maximum flows through networks, The primal dual method, Approximate dual methods.

Ph.D. or doctoral degree in Operations research, Industrial Engineering or Mathematics.

This course covers the fundamentals of algorithm analysis. The topics to be covered are: Algorithm definition, asymptotic definition, data structure review, graph exploration, heuristic algorithms: greedy, meta heuristic improvers: simulating annealing, genetic algorithms, taboo search, dynamic programming, computational complexity, NP complete problems and NP hard transformations.

Ph.D. or Doctoral degree in Operations research, Industrial Engineering or mathematics.

Introduction to Markov Queues (Description of models, process of interest, review of continuous time Markov chain, birth/death process, M/M/s queue and variations, waiting time process, busy period, method of stages)
Queues with non Markovian Service or Interarrival Times (Embeded Markov Chain Method, Polaczek-Khintchine formula, Approximation for M/G/s queue)
-General Queues in Light Traffic (Structure of GI/G/1 queue, regenerative processes, Little´s Theorem, limit theorems, extreme values, GI/G/s queues)
Heavy Traffic Theory (Exponential Approximation of Waiting Times for GI/G/s queues)
Networks of Queues (Jackson networks, reversibility of Markov Chains, product form solution)
Approximation Methods (Diffusion Approximations, approximation stationary, distributions for closed Markovian queues)

PhD or Doctoral Degree in Mathematics, Stochastic, Industrial Engineering, Operations Research.

In this course the student will understand the importance of supply chain management through the study of basic concepts and its applications to different real-world situations. Some mathematical methods, specific to different stages of the supply chain, will also be studied. The chief objective of this course is to give the student a broad overview of what supply chain encompasses, and of the research opportunities in the area.

Simchi-Levi, D. ; P. Kaminsky ; and E. Simchi-Levi. Designing and Managing the Supply Chain. U.S. : Irwin McGraw-Hill, 2000.
Tayur, S. ; R. Ganesham ; and M. Magazine, eds. Quantitative Models for Supply Chain Management. Norwell, Massachusetts : Kluwer Academic Publishers, 1999.
Several readings.

This course covers the fundamentals of optimization under the use of integer variables. The topics to be covered are: Combinatorial problem formulation, The importance of a good representation, Polyhedral concepts, generic algorithms: branch and bound, valid innequalities, Lagrangian relaxation, sub gradient meted, duality theorems, special structure problem solution.

Ph.D. or Doctoral degree in Operations research, Industrial Engineering or mathematics.

This course is intended to train students to efficiently design an adequate, low cost manufacturing process taking into consideration the available raw materials and the final product that is going to be fabricated. To achieve this goal, the student must know the materials properties as well as the manufacturing processes that are used to enhance and transform raw materials into final goods. Another important aspect that will be covered in the course is the interface of the manufacturing process itself with the manufacturing planning process, including time study methods.

Ph.D or doctoral degree in Industrial or Mechanical Engineering with practical knowledge in manufacturing process.

Goal: The student will know, understand and use information technologies in accordance with the correspondent organizational structure. In order to satisfy the real needs of the stake holders in such a way that the investments in information technology pay.

Cash, J.I., McFarlan, F.W., Mckenney, J.L., y Applegate, L.M. Corporate Information Systems Managment: Text and Cases, IRWIN. Boston, 1992.
Laudon & Laudon, Management Information Systems, Prentice Hall Keen,
P.G. Shaping the Future: Business Design through Information Technology. Harvard Business School Press. Boston, 1991.
Scott Morton, M.. The Corporation of the 1990´s: Information Technology and Organizational Transformation. Oxford University Press. New York, 1991.
Readings.

Ph.D. or Doctoral degree in Computer Science, Information Systems or Management (major in information systems)

The course is oriented to the handling of modern manufacturing equipment and to the application of improvement techniques (mathematical and managerial) including planning and design of an integrated manufacturing system, handling of the most representative equipments and planning and control of manufacturing processes. Among the techniques, the course makes special emphasis on concurrent engineering and on group technology.

Askin y Standridge, Model and Analysis of Manufacturing Systems, John Wiley 1993 Bedworth, et. al.,
Computer-Integrated Design and Manufacturing, McGraw-Hill International Editions 1991
Readings and cases.

Ph.D. or Doctoral degree in Mechanics or Industrial Engineering in practical knowledge of Flexible Manufacturing Systems

In this course the student will learn about the different warehouse systems with emphasis in case studies. The chief objective of this course is to give the student a broad overview of what these systems encompass, and of the research opportunities in the area.

Several sources; among them: Ballou, R.H. Business Logistics Management. 4th ed. Englewood Cliffs, NJ : Prentice Hall, 1999
Krajewski, Lee J. and Ritzman, Larry P. Administración de Operaciones 5th. Ed. Mexico : Prentice Hall, 1999.
Warehouse Systems and the Supply Chain : A Survey of Success Factors by Anderson Consulting

This course covers the methods of statistical analysis of categorical data or when the judge´s or simple ordinal scale of measure is using, or may be serious doubt exist about the assumptions that underline standard statistical methodology. Particularly the course include the following topics: Inferences about medians based on ranks, Mann-Whitney U Test, Wilcoxon Signed-Rank test, Friedman Test for Randomized Block Design, the Test for Randomness, Nonparametric Regression Models, Goodness-of-fit Test for Discrete Data, Contingency Tables and Analysis of Association and Robust Estimators.

P. Sprent, N.C. Smeeton, Applied Nonparametric Statistical Methods, 3rd ed. ISBN: 1-5848-8145-3,
Chapman & Hall M.Hollander, D:A: Wolfe, Nonparametric Statistical Methods, Wiley

Ph.D. or Doctoral Degree in Statistics, Mathematics, Operation Research or Industrial Engineering

The topics to be covered in this course express the fundamental concepts of Probability and reliability theory and the interaction between them. The topics are: Probability distributions, Reliability distributions. Test and systems of useful life. During the course the student will adquire the skills of abstraction and the sense for detecting, planning and solveing practical problems.

Tobias, Paul A. & Trindade, David C. Applied Reliability. USA 1986.
Van Nostrand Reinhold Company. Grosh, Doris Lloyd. A Primer of Reliability Theory. USA 1989. John Wiley & Sons

Ph.D. or doctoral degree in Probability theory, Stochastics proceses or Mathematical Statistics.

The topics to be covered are: Autoregressive Integrated Moving Average Models (ARIMA), Model Building,, The Autocorrelation and Partial Correlation Functions, Identification of ARIMA models, Parameter estimation, Diagnostics, Forecast (intervals and points), seasonal models, ARIMA models properties, Univariable model extensions, transferring functions and intervention models.

Ph.D. or Doctoral degree in Statistics, Operations Research, Industrial o Engineering

This course covers the topics of regression analysis, the use of computers is unavoidable, Minitab is recommended but, the use of spread sheets programs and statistical programs can be used as well.. The topics to be covered are: Simple regression analysis, Properties, Estimators, Simple linear regression hypothesis tests, Multiple Regression Analysis, ANOVA, Generalizations of Multiple Regression Analysis, Assumptions and fix Ups, Polynomial regressions, qualitative variable (categories), Model selection; collinearity among regressors, Autoregresive time series models, linear regression model predictions, nonlinear regression, Computational and inference for nonlinear models.

Ph.D. or Doctoral Degree in Satistics, Operation Research or Industrial Engineering

The student is presented to the theoretical and practical issues of statistical quality control. The topics to be covered are: Introduction to quality control, Acceptance Samplings, Process control, Management of quality, Robust design and the Taguchi method, Automation Quality and CIM, Quality System concepts

Ph.D. or doctoral degree in Industrial Engineering, Statistics, Mathematics or Operations research

This course will present the mathematical theory behind interior point methods for linear programming. The topics to be covered are: Introduction, Linear Programming Prerequisites, Properties of Convex Sets, Interior Point Algorithms, Related Problems.

This course studies algorithms for linear and non linear optimization. The main element is the numerical solution of manufacturing and statistic problems. The topics to be covered are: Revised simplex method, direct search methods, gradient method, Newton, quasi Newton, conjugate gradients, reduced gradient, recursive linear and quadratic programming, separable programming, least square non linear algorithms.

Dennis and Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, SIAM, 1996.
Nocedal, Wright, Numerical Optimization, Springer Verlag, New York, 1999.

Ph.D. or Doctoral degree in Operations research, Industrial Engineering or mathematics.

The purpose of this course is to give an introduction to the state of the art in computational methods for large scale linear programming problems. The topics to be covered are: Introduction to large scale problems, Sparse matrices, Pre-Processing, Simplex method in general, Interior point methods in general, Cholesky factorization, Initial conditions and parameter design.

Ph. D. or doctoral degree in Industrial Engineering, Operations Research, Mathematics or Computer science.

The objective of this course is the study of application cases related to industrial statistics. In such a way that the student will obtain an integral vision of the area of specialization

Introduction to integrated manufacturing systems. Production operations and automation strategies.Econimic engineering in the production and automation systems. Detroit automation. Automation of Assembly Systems. Numerical control for production systems. Robotized systems, programming and applications. Automated quality control and inspection. The automated plant in the future.

Instructor Profile: Ph.D. in Industrial Engineering, Manufacturing Systems or related areas.

Introduction to discrete optimization problems and to heuristic methods. Introduction to complexity theory. Formulation of integer programming problems. Exact Methods. Alternative prrocedures for heuristic design. Worst-case heuristic analysis. Empirical Analysis of heuristics. Aplications.

Instructor Profile: Ph. D. in Operations Research, Industrial Engineering, Computer Sciences or related areas.

Introduction to simulation. Basics of discrete event simulation. Basic Simulation Models. Simulation Languages - part I. Simulation of stochastic models. Review of select statistic concepts. Random number generation. Random variable generation.. Statistical Analysis of the results of the simulation model. Experimental design of simulation models. Simulation languages - part II

Instructor Profile: Ph.D. in Industrial Engineering, Operations Research, Statistics, Manufacturing Systems or related areas.

Introduction. International comparisons of Manufacturing Systems. Developement of a Manufacturing Strategy: Principles and Concepts. Developement of a Manufacturing Strategy:: Methodology. Manufacturing Systems and their Organization. World Class Manufacturing Systems. Customer chains in Manufacturing Systems. Basic Conditions for operation in Manufacturing Systems. Dedicated Plants: principles, concepts y methodology. Developement of the manufacturing infrastructure. Financial and accounting considerations and its impact in Manufacturing Systems. Case study.

Perfil del profesor :Ph. D. in Industrial Engineering, Manufacturing Systems and related areas.

Combinatorial problems modeling. Graph Problems. Matching Problems. Covering Problems. Integer programming formulations. Branch and Bound Method. Duality and relaxation. Polihedral Theory. Valid inequalities and facets. Heuristics. Computational complexity and NP-complete problems.

Instructor Profile: Ph. D. in Operations Research, Mathematics, Computer Sciences and related areas.

This course is the first of a series of five courses. The general goal of this series is to develop in the student the skills and knowledge to define his/her area of study and topic of research, as well as to develop the necessary elements in the student so that at the end of the series the student must be able to defend successfully before his/her dissertation committee his/her dissertation proposal satisfying all the requirements that the PhD program establishes for that.
Bibliography
Several Scientific Journal papers and books related to the research involved
Professor Profile
Ph.D. or Doctoral degree in: Industrial Engineering, Operations Research, Statistics, Mathematics or related area

This course is the second of a series of five courses. The general goal of this series is to develop in the student the skills and knowledge to define his/her area of study and topic of research, as well as to develop the necessary elements in the student so that at the end of the series the student must be able to defend successfully before his/her dissertation committee his/her dissertation proposal satisfying all the requirements that the PhD program stablishes for that.
Bibliography
Several Scientific Journal papers and books related to the research involved
Professor Profile
Ph.D. or Doctoral degree in: Industrial Engineering, Operations Research, Statistics, Mathematics or related area

This course is the third of a series of five courses. The general goal of this series is to develop in the student the skills and knowledge to define his/her area of study and topic of research, as well as to develop the necessary elements in the student so that at the end of the series the student must be able to defend successfully before his/her dissertation committee his/her dissertation proposal satisfying all the requirements that the PhD program stablishes for that.
Bibliography
Several Scientific Journal papers and books related to the research involved
Professor Profile
Ph.D. or Doctoral degree in: Industrial Engineering, Operations Research, Statistics, Mathematics or related area

This course is the fourth of a series of five courses. The general goal of this series is to develop in the student the skills and knowledge to define his/her area of study and topic of research, as well as to develop the necessary elements in the student so that at the end of the series the student must be able to defend successfully before his/her dissertation committee his/her dissertation proposal satisfying all the requirements that the PhD program stablishes for that.
Bibliography
Several Scientific Journal papers and books related to the research involved
Professor Profile
Ph.D. or Doctoral degree in: Industrial Engineering, Operations Research, Statistics, Mathematics or related area

This course is the fifth of a series of five courses. The general goal of this series is to develop in the student the skills and knowledge to define his/her area of study and topic of research, as well as to develop the necessary elements in the student so that at the end of the series the student must be able to defend successfully before his/her dissertation committee his/her dissertation proposal satisfying all the requirements that the PhD program stablishes for that.
Bibliography
Several Scientific Journal papers and books related to the research involved
Professor Profile
Ph.D. or Doctoral degree in: Industrial Engineering, Operations Research, Statistics, Mathematics or related area

The goal of this course is to weekly expose the students to a lecture in the state of the art of Industrial engineering and related fields in order to give the students more options to choose his/her dissertation topic.

Any researcher or scholar doing research in the state of the art of industrial engineering or related fields.

At the end of this course the student must defend his dissertation topic proposal before the correspondent committee.