Maria Rieders

Maria Rieders
  • Adjunct Professor
  • Faculty Fellow, Undergraduate Advisor

Contact Information

  • office Address:

    3730 Walnut Street
    517 Jon M. Huntsman Hall
    Philadelphia, PA 19104

Overview

Maria Rieders earned a M.Sc. degree in applied mathematics from the University of Ulm, Germany in 1984 and a M.Sc. and a Ph.D. in business administration from the University of Rochester in 1987 and 1990, respectively.

Maria Rieders is an adjunct full professor of Operations, Information and Decisions at the Wharton School of the University of Pennsylvania. She has extensive experience teaching engineering and business students at the undergraduate and graduate level and has been awarded several teaching awards while on the faculty at Northwestern University. At Wharton, she has been teaching Ph.D. level courses in the areas of stochastic models, queueing theory, and dynamic programming since 2001.

Her research interests focus on stochastic models, in particular on computational methods for large-scale systems, with applications in the performance analysis of communication systems and production facilities Her research has been funded by the National Science Foundation, by Ameritech Corporation, and, through collaboration with students, by Motorola and Argonne National Laboratory.

Previous employments include a scientific staff position at the Allensbach Institute for Public Opinion Research in Allensbach, Germany (1984-1985), a faculty position at the Department of Industrial Engineering and Management Sciences at Northwestern University (1990-1995), a visiting researcher post at the Institute for Operations Research at the Universität Bonn, Bonn, Germany (1993) and industrial consulting engagements.

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Teaching

Current Courses

  • OIDD930 - Stochastic Models I

    This course introduces mathematical models describing and analyzing the behavior of processes that exhibit random components. The theory of stochastic processes will be developed based on elementary probability theory and calculus. Topics include random walks, Poisson processes, Markov chains in discrete and continuous time, renewal theory, and martingales. Applications from the areas of inventory, production, finance, queueing and communication systems will be presented throughout the course.

    OIDD930002 ( Syllabus )

Past Courses

  • OIDD220 - INTRODUCTION TO OM

    This course introduces basic concepts of operations management and application of the same in business practice today. We will examine the theoretical foundations of operations management and how these principles or models can be employed in both tactical and strategic decision making. Topics covered in detail are forecasting techniques, planning under deterministic and uncertain demand, operations planning and scheduling, queuing theory, service operations management, newsvendor models, risk pooling strategies in firms, capacity and revenue management, and supply chain coordination. We will conclude by discussing how supply chains evolve under technological change.

  • OIDD930 - STOCHASTIC MODELS I

    This course introduces mathematical models describing and analyzing the behavior of processes that exhibit random components. The theory of stochastic processes will be developed based on elementary probability theory and calculus. Topics include random walks, Poisson processes, Markov chains in discrete and continuous time, renewal theory, and martingales. Applications from the areas of inventory, production, finance, queueing and communication systems will be presented throughout the course.

  • OIDD931 - STOCHASTIC MODELS II

    Extension of the material presented in OIDD930 to include renewal theory, martingales, and Brownian motion.

  • OIDD932 - QUEUING THEORY

    This course presents the mathematical foundations for the analysis of queueing systems. We will study general results like Little's law and the PASTA property. We will analyze standard queueing systems (Markovian systems and variations thereof) and simple queueing networks, investigate infinite server models and many server approximations, study GI/G/1 queues through random walk approximations, and read papers on applied queueing models.

  • OIDD934 - DYNAMIC PROGRAMMING

    The course goal is to provide a brief but fairly rigorous introduction to the formulation and solution of dynamic programs. Its focus is primarily methodological. We will cover discrete state space problems, over finite or infinite time horizon, with and without discounting. Structured policies and their theoretical foundation will be of particular interest. Computational methods and approximation methods will be addressed. Applications are presented throughout the course, such as inventory policies, production control, financial decisions, and scheduling.

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