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Regular version of the site
Master 2019/2020

Contemporary Decision Sciences: an Integrated Perspective

Area of studies: Applied Mathematics and Informatics
When: 1 year, 3, 4 module
Mode of studies: offline
Instructors: Valentina Kuskova
Master’s programme: Applied Statistics with Network Analysis
Language: English
ECTS credits: 4

Course Syllabus


This course is a required foundational course for masters’ students in “Applied Statistics with Network Analysis” program, designed to familiarize them with the most recent developments in interdisciplinary decision sciences. This course covers many approaches to solving real-life problems from the mathematical point of view – in other words, we are using available mathematical tools to make good decisions. Various optimization techniques are surveyed with an emphasis on the why and how of these types of models as opposed to a detailed theoretical approach. Students develop optimization models which relate to their areas of interest. Spread-sheets are used extensively to accomplish the mathematical manipulations. Emphasis is placed on input requirements and interpretation of results.
Learning Objectives

Learning Objectives

  • The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.
Expected Learning Outcomes

Expected Learning Outcomes

  • Know the role of the modeling in decision-making and different model components.
  • Know different decision-structuring techniques.
  • Know model-building and model validation techniques.
  • Be able to understand and explain in your own words ways in which model-based support systems are needed and can be utilized in managerial decision processes.
  • Be able to explain how and why modeling is used in the support system environment.
  • Be able to identify and differentiate different model components.
  • Be able to criticize constructively and determine existing issues with the use of statistical methods in published work
  • Have a working knowledge of mathematics of decision sciences.
  • Have a working knowledge of different ways of using software programs for data analysis.
  • Have an ability to use model-based management solution using a variety of software packages.
Course Contents

Course Contents

  • Introduction to DS
    The first session will introduce the main concepts of problem solving and decision-making, quantitative analysis, management science techniques.
  • The basics – spreadsheet techniques
    The session will demonstrate the use of spreadsheets for modeling, review of the key Excel functions, examples of macro writing and advanced Visual Basic coding.
  • Linear programming methods I
    The session introduces the basic maximization model with its solutions, including graphical; ex-tremums and optima, minimization problems, special cases.
  • Linear programming methods II
    This session continues with linear programming, moving to sensitivity analysis and interpretation of solutions. It will also look at a variety of applications of linear programming to real-life data (in marketing, financial sector, operations management)
  • Advanced linear programming techniques
    This session covers more advanced forms of linear programming, including data envelopment analysis, revenue management and portfolio models.
  • Distribution and network models
    This session will discuss transportation models, assignment models, minimum cost network flow models, and shortest path models. It will also make a connection to the various network-analytic methods that are being taught elsewhere in the program.
  • Integer linear programming
    This session will discuss types of integer linear programming models, graphical and computer solutions for ALP, and also applications involving binary variables.
  • Nonlinear optimization models
    This session will discuss basic ideas of nonlinear optimization, pricing models, advertising re-sponse and selection models, production application, facility location models, Markowitz portfo-lio optimization models.
  • Project scheduling: PERT/CPM
    This session will focus on project scheduling with known and unknown activity times and time-cost tradeoffs.
  • Inventory models
    This session will look at basic inventory models, including Economic Order Quantity, Economic Production Lot Size Model, single-period inventory model with probabilistic demand, order-quantity- reorder point model with probabilistic demand
  • Simulation modeling
    This session will look at real applications of simulation, probability distributions for input variables, the effects of input distributions on results, operations models, financial models, simulating games of chance.
  • Conclusion: overview of the field
    This session is designed to give the final look at the vast field of decision sciences, with most up-to-date methods reviewed and put together into a one coherent whole.
Assessment Elements

Assessment Elements

  • non-blocking Final In-Class or Take-home exam (at the discretion of the instructor)
  • non-blocking Homework Assignments (5 x Varied points)
  • non-blocking In-Class Labs (9-10 x Varied points)
  • non-blocking Quizzes (Best 9 of 10, Varied points)
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.5 * Final In-Class or Take-home exam (at the discretion of the instructor) + 0.2 * Homework Assignments (5 x Varied points) + 0.2 * In-Class Labs (9-10 x Varied points) + 0.1 * Quizzes (Best 9 of 10, Varied points)


Recommended Core Bibliography

  • Kleinman, G., & Lawrence, K. D. (2015). Applications of Management Science. Bingley, U.K.: Emerald Group Publishing Limited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=948292
  • Ravindran, A. (2008). Operations Research and Management Science Handbook. Boca Raton: CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=209433
  • Rudall, B. H. (2007). Management Science : Current Researches and Developments. [Bradford, England]: Emerald Group Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=201937

Recommended Additional Bibliography

  • Mingers, J. (2006). Realising Systems Thinking: Knowledge and Action in Management Science. New York, NY: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=170755
  • Taudes, A. (2005). Adaptive Information Systems and Modelling in Economics and Management Science. Wien: Springer Science & Business Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=255803