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Courses

  • Stochastic Models (40120)
    • תקציר הקורס:

    Abstract:

    Binomial and Poisson Processes, Markov Chains in discrete and continuous time - Steady-state analysis and limiting distributions. Birth and Death Processes, Queuing Theory – Poisson i?nput and Exponential service, special Queuing systems.
  • Optimization Methods (40123)
    • תקציר הקורס:

    Abstract:

    Optimization problems in engineering. Unconstrained optimization:

    analytic and numerical approach,

    Golden-Section method, gradient based methods, Newton’s method.

    Constrained optimization: analytic approach - Lagrange multipliers,

    KKT conditions; numerical approach - penalty and barrier methods.

    Linear programming, dynamic programming, integer programming. Applications of optimization methods.
  • Introduction To Data-Based Management Models (40243)
    • תקציר הקורס:

    Abstract:

    During the course, a number of practical problems will be presented

    for analysis and joint discussion in class.

    Similar challenging problems will be given in housework as

    a task for independent analysis and solution (mini-projects).

    Homework will include working with spreadsheets in Excel and using

    software like Python or R (each group of students will be

    responsible for finding the platform that is convenient for them

    as well as the ability to use it for data analysis).

    We will review individual academic papers of practical significance.

    The main tools we will discuss are Time-Series and forecasting

    (regression) stochastic optimization, queueing theory,

    robust optimization, and the combination thereof.

    We will discuss the difference between dynamic versus static problems,

    off-line versus on-line problems, we will review varied practical

    problems in an effort to analyze and base the solution on real data.
  • Demand Prediction And Analysis (66004)
    • תקציר הקורס:

    Abstract:

    The course focuses on the methodology and on relevant applications

    for demand forecasting on short and long term in order to meet

    the needs of those who practice engineering and systems.

    The course will develop prediction demand models

    (e.g., travel demand forecasting, forecasting energy / water, and a choice of alternatives).

    The course consists of four parts: (1) Data science; (2) Regression; (3) Time Series; (4) Structural models.

    The examples in the course will include the last examples

    from real world systems and examples of research that used tools of regression and time series analysis.

    Classroom practice and homework will be done with the software R.