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OPERATIONS RESEARCH

CODE 80155
ACADEMIC YEAR 2022/2023
CREDITS
  • 9 cfu during the 1st year of 11160 COMPUTER ENGINEERING (LM-32) - GENOVA
    		•
    SCIENTIFIC DISCIPLINARY SECTOR MAT/09
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
    		•
    SEMESTER 1° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The Course introduces to optimization models and methods for the solution of decision problems. It is structured according to the basic topics of problem modelling, its tractability, and its solution by means of algorithms that can be implemented on computers.  Case studies from Engineering, with particular attention to Information Technology, are presented and investigated.

    The lectures are organized in i) methodology and ii) case-studies from real-world applications. Additional exercises and use of software tools are presented during exercise hours.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    The Course introduces to optimization models and methods for the solution of decision problems. It is structured in the main topics of problem modelling, computational tractability, and solution by means of algorithms that can be implemented on a computer. Several applications are considered and various case studies are detailed. The target of the Course consists in making the students acquire the expertise to face decision problems by means of models and methods that can operate in the presence of limited resources. The students will be taught to: understanding and modelling a decision process in terms of an optimization problem by defining the decision variables, the cost function to be minimized (or the figure of merit to be maximized), and the constraints; framing the obtained problem within the range of the reference optimization problems (linear/nonlinear, discrete/continuous, deterministic/stochastic, static/dynamic, etc); achieving the matching between the corresponding solving algorithm and a suitable software.

    AIMS AND LEARNING OUTCOMES

    The students will be taught to:

    - interpret and shape a decision-making process in terms of an optimization problem, identifying the decision-making variables, the cost function to minimize (or the figure of merit to maximize), and the constraints;

    - framing the problem in the range of problems considered "canonical" (linear / nonlinear, discrete / continuous, deterministic / stochastic, static / dynamic, etc.);

    - realizing the "matching" between the solving algorithm (to choose from existing or to be designed) and an appropriate processing software support.

     

     

    PREREQUISITES

    Linear Algebra. Vector and matrix calculus. Basic concepts of Mathematical Analysis and Geometry.

    TEACHING METHODS

    Lectures and exercises.

    SYLLABUS/CONTENT

    INTRODUCTION TO OPERATIONS RESEARCH

    LINEAR PROGRAMMING

    DUALITY

    INTEGER PROGRAMMING

    GRAPH AND NETWORK OPTIMIZATION

    COMPLEXITY THEORY

    NONLINEAR PROGRAMMING

    DYNAMIC PROGRAMMING

    CASE STUDIES FROM INFORMATION TECHNOLOGY

    SOFTWARE TOOLS FOR OPTIMIZATION

     

    RECOMMENDED READING/BIBLIOGRAPHY

    Lecture notes provided by the teacher and available in electronic format.

    TEACHERS AND EXAM BOARD

    Exam Board

    MARCELLO SANGUINETI (President)

    MAURO GAGGERO

    DANILO MACCIO'

    ANGELA LUCIA PUSILLO

    MASSIMO PAOLUCCI (President Substitute)

    LESSONS

    LESSONS START

    https://courses.unige.it/11160/p/students-timetable

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    Written, if it will be possible to make exams "in presence". Otherwise, the teacher will decide whether the exam via Teams will be written or oral.

     

    ASSESSMENT METHODS

    Comprehension of the concepts explained during the Course.

    Capability to:

    - interpret and shape a decision-making process in terms of an optimization problem, identifying the decision-making variables, the cost function to minimize (or the figure of merit to maximize), and the constraints;

    - frame the problem in the range of problems considered "canonical" (linear / nonlinear, discrete / continuous, deterministic / stochastic, static / dynamic, etc.);

    - choose and/or develop a solving algorithm and apply it to solve the problem.

     

    Exam schedule

    Date Time Location Type Notes
    21/12/2022 11:30 GENOVA Scritto
    09/01/2023 10:30 GENOVA Scritto
    02/02/2023 10:30 GENOVA Scritto
    29/05/2023 10:30 GENOVA Scritto
    17/07/2023 10:30 GENOVA Scritto
    11/09/2023 10:30 GENOVA Scritto

    FURTHER INFORMATION

    For the Laurea in Mathematics, which "borrows" only 7 cfu, the following topics are excluded:

    DYNAMIC PROGRAMMING

    CASE STUDIES FROM INFORMATION TECHNOLOGY