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Course 2020-2021 a.y.


Department of Decision Sciences

Course taught in English

Supported by Siemens

Go to class group/s: 31

CLEAM (6 credits - II sem. - OP  |  SECS-S/06) - CLEF (6 credits - II sem. - OP  |  SECS-S/06) - CLEACC (6 credits - II sem. - OP  |  SECS-S/06) - BESS-CLES (6 credits - II sem. - OP  |  SECS-S/06) - WBB (6 credits - II sem. - OP  |  SECS-S/06) - BIEF (6 credits - II sem. - OP  |  SECS-S/06) - BIEM (6 credits - II sem. - OP  |  SECS-S/06) - BIG (6 credits - II sem. - OP  |  SECS-S/06) - BEMACS (6 credits - II sem. - OP  |  SECS-S/06)
Course Director:

Classes: 31 (II sem.)

Suggested background knowledge

It is suggested for students to have attended a basic course on mathematics and a basic course on statistics before this course.

Mission & Content Summary

The scope of this course is to offer participants a thorough exploration of business analytics and of how computational modelling can be combined with big data to achieve given industry goals. In a well known communication to the European Parliament on 2 July 2014, the European Community evidenced the need of training a generation of managers who know how to combine information derived from data into models to support decisions. In fact, in recent years, the data driven revolution is changing the way in which institutions and corporations make decisions. We are heading towards industry 4.0. The great availability of data, the increased computing and information technology capabilities are creating new jobs and changing the way in which companies operate. The February 2018 report of the UK Government Office for Science highlights that computational modelling is a source of competitive advantage for corporations. In the first part of the course, participants are exposed to the fundamental theoretical and methodological basis, analyzing relevant quantitative and mathematical methods. In the second part, students are exposed to industry case studies. With the participation of data scientists and experts coming from the industry participants discover how innovative methods based on big data and information technology have allowed to solve modern industrial problems.

  • The Principles of Machine Learning.
  • Formulation of quantitative models via Linear Programs.
  • The symplex method, Duality.
  • Sensitivity Analysis.
  • Network Type Problems.
  • Big Data and Lasso: The Dantzig Selector.
  • Industry 4.0 and Descriptive Analytics: Business Case studies.

These case studies are discussed and solved in the presence of guest lecturers from Siemens and from the involved partner companies.

Intended Learning Outcomes (ILO)
At the end of the course student will be able to...
  • Formulate a computational model to solve business and management problems.
  • Appreciate the principles and the solution algorithms of linear programs at the basis of dedicated software for their application.
  • Distinguish the wide range of business problems whose solution is supported by computational models.
  • Recognize the challenges posed to quantitative methods by large dimensionality and big data and identify the corresponding technological solutions.
At the end of the course student will be able to...
  • Organize information to build a quantitative model in line with the input posed.
  • Translate a business problem into a corresponding computational modelling frame.
  • Use dedicated software in order to obtain quantitative information.
  • Interpret solutions derived from implementing the chosen model in order to make optimal decisions.
  • Analyze quantitative models with sensitivity analysis tools to obtain "managerial insights".

Teaching methods
  • Face-to-face lectures
  • Online lectures
  • Guest speaker's talks (in class or in distance)
  • Case studies /Incidents (traditional, online)

The course makes use of a combination of teaching techniques. Remote (online) but synchronous lectures are used for the sessions in which methodological and theoretical parts of the paper are proposed and discussed.

  • In these sessions students are assisted in identifying the quantitative model, in implementing the model through dedicated software and in performing sensitivity analysis.
  • In the second part of the course, students are exposed to the solution of industry case studies presented in a triplet of lectures. After the exposition by the experts of the industrial problem, participants are introduced to the methods of solution and are guided in critically discussing the results, the methodologies adopted and in identifying weaknesses and remaining open questions.

Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Individual assignment (report, exercise, presentation, project work etc.)
  • x    

    Assessment, is performed as follows.

    The assessment will be written in the form of: Assignments plus Final Respondus test. Students can take the exam in the following form, mode A or mode B at their choice.

    Mode A: There will be two intermediate assignments concerning the first part of the course and one concerning the second part of the course and a final general exam through the Responds platform.

    • The first two assignments concern the material explained first part of the course. They consist of a series of quantitative questions in closed form. Students solve mathematical problems divided into open-ended numerical questions and multiple-choice (or multiple answer) questions. The first assignments wish to test student's ability in formulating a computational model to solve a given business problem and their understanding of the principles of the solution of linear programming problems.
    • The third assignment concerns the material explained in the second part of the course. It consists of closed form/ multiple choice questions concerning the methodological aspects of the case studies and the material developed in the second part of the course. Students will be tested on their knowledge of the business problems illustrated in the second part of the course and in their ability to recognize the challenges to the use of quantitative models to solve such problems.

    In Mode B students take a unique final assignment on the entire course program. This assignment is, virtually, the union of the assignments in Mode A.

    With the assignments, students will also be tested on the dedicated subroutines developed for the course and implemented in the software Matlab.

    All students will then be required to take a final Respondus test, which will test their knowledge on the entire course program, through multiple-choice/multiple answer questions. The Respondus exam is closed book.

    Teaching materials
    • R.J. VANDERBEI,  Linear Programming, Springer Series in Operational Research and Management Science, 2014, Fourth Edition, ISBN 978-1-4614-7629-0.
    • F.S. HILLIER and G.J. LIEBERMAN, Introduction to Operations Research, Second Edition, 2001.
    • Teaching notes and slides provided by the teachers.
    Last change 10/12/2020 17:11