Study programme 2020-2021Français
Propose a functional solution
Programme component of Master's in Computer Engineering and Management à la Faculty of Engineering

Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what special Covid-19 assessment methods are possibly planned for the end of Q3

CodeTypeHead of UE Department’s
contact details
UI-M1-IRIGIG-012-MCompulsory UETUYTTENS DanielF151 - Mathématique et Recherche opérationnelle
  • TUYTTENS Daniel
  • GILLIS Nicolas

of instruction
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français486000099.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-MARO-011Graph Theory and Combinatorial Optimization3612000Q1
I-MARO-017Modeling Workshop in Operational Research1248000Q1
Programme component

Objectives of Programme's Learning Outcomes

  • Imagine, design, develop, and implement conceptual models and computer solutions to address complex problems including decision-making, optimisation, management and production as part of a business innovation approach by integrating changing needs, contexts and issues (technical, economic, societal, ethical and environmental).
    • Identify complex problems to be solved and develop the specifications with the client by integrating needs, contexts and issues (technical, economic, societal, ethical and environmental).
    • On the basis of modelling, design a system or a strategy addressing the problem raised; evaluate them in light of various parameters of the specifications.
    • Deliver a solution selected in the form of diagrams, graphs, prototypes, software and/or digital models.
    • Evaluate the approach and results for their adaptation (modularity, optimisation, quality, robustness, reliability, upgradeability, etc.).
    • Integrate technological innovation and intelligence within engineering teams.
  • Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out computer and management engineering missions, using their expertise and adaptability.
    • Master and appropriately mobilise knowledge, models, methods and techniques specific to computer management engineering.
    • Analyse and model an innovative IT solution or a business strategy by critically selecting theories and methodological approaches (modelling, optimisation, algorithms, calculations), and taking into account multidisciplinary aspects.
    • Identify and discuss possible applications of new and emerging technologies in the field of information technology and sciences and quantifying and qualifying business management.
    • Assess the validity of models and results in view of the state of science and characteristics of the problem.
  • Plan, manage and lead projects in view of their objectives, resources and constraints, ensuring the quality of activities and deliverables.
    • Define and align the project in view of its objectives, resources and constraints.
    • Exploit project management principles and tools, particularly the work plan, schedule, document monitoring, versioning and software development methodologies.
    • Assess the approach and achievements, regulate them in view of the observations and feedback received.
    • Respect deadlines and timescales
  • Work effectively in teams, develop leadership, and make decisions in multidisciplinary, multicultural and international contexts.
    • Interact effectively with others to carry out common projects in various contexts (multidisciplinary, multicultural, and international).
    • Contribute to the management and coordination of a team that may be composed of people of different levels and disciplines.
    • Identify skills and resources, and research external expertise if necessary.
    • Make decisions, individually or collectively, taking into account the parameters involved (human, technical, economic, societal, ethical and environmental).
  • Communicate and exchange information in a structured way - orally, graphically and in writing, in French and in one or more other languages - scientifically, culturally, technically and interpersonally, by adapting to the intended purpose and the relevant public.
    • Argue to and persuade customers, teachers and boards, both orally and in writing.
    • Select and use the written and oral communication methods and materials adapted to the intended purpose and the relevant public.
    • Use and produce scientific and technical documents (reports, plans, specifications) adapted to the intended purpose and the relevant public.
  • Adopt a professional and responsible approach, showing an open and critical mind in an independent professional development process.
    • Exploit the different means available in order to inform and train independently.
  • Contribute by researching the innovative solution of a problem in engineering sciences.
    • Construct a theoretical or conceptual reference framework, formulate innovative solutions from the analysis of scientific literature, particularly in new or emerging disciplines.
    • Develop and implement conceptual analysis, numerical modelling, software implementations, experimental studies and behavioural analysis.
    • Collect and analyse data rigorously.
    • Adequately interpret results taking into account the reference framework within which the research was developed.
    • Communicate, in writing and orally, on the approach and its results in highlighting both the scientific criteria of the research conducted and the theoretical and technical innovation potential, as well as possible non-technical issues.

Learning Outcomes of UE

- Team work- Investigate and develop new operational reasearch methods for an industrial problem- Oral and written presentations- Contact with a client The team work is based on the notions learned in graph theory and in combinatorial optimisation.     Understand the fundamental notions and problems appearing in graph theory;study the corresponding algorithms;go deeply into algorithmic notions from the algorithm efficiency point of view;understand the fundamental problems and techniques of combinatorial optimization;illustrate some methods on some particular problems;show the utility of algorithms for solving practical problems in scheduling management, logistics

Content of UE

This class aims at introducing the students to a real-world industrial project related to operational reasearch. The class is based on the notions learned in graph theory and in combinatorial optimisation.   Basic notions of graph theory and data structure; study of classical graph theory problems : trees, shortest paths, connexity, flows;introduction to complexity theory : P and NP classes; study of classical combinatorial optimization problems : knapsack, set covering, travelling salesman; introduction to metaheuristics  

Prior Experience

Good knowleldge of optimisation techniques and modelling.

Type of Assessment for UE in Q1

  • Presentation and/or works
  • Written examination

Q1 UE Assessment Comments

It concerns a global evaluation that is calculated as follows : If  X = evaluation / 20 of  AA I-MARO-017  and if  Y = evaluation / 20 of  AA I-MARO-011.   If Min(X,Y) is lower or equal to 9 then the Global evaluation of  UE = Min(X,Y).   If Min( X, Y )  is strictly greater than 9  then the Global evaluation of UE =  5/9 *  X + 4/9 * Y  

Type of Assessment for UE in Q3

  • Presentation and/or works
  • Written examination

Q3 UE Assessment Comments

If  the evaluation  of AA  I-MARO-017 is not successful at Q1, there is no possible evalaution at Q3. If  the evaluation of AA  I-MARO-011 is not successful  at Q1, the evaluation is similar to that of Q1. 

Type of Resit Assessment for UE in Q1 (BAB1)

  • N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
  • Cours magistraux
  • Travaux pratiques
  • Cours magistraux
  • Conférences
  • Ateliers et projets encadrés au sein de l'établissement

Mode of delivery

AAMode of delivery
  • Face to face
  • Mixed

Required Reading


Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-MARO-011Not applicable
I-MARO-017Not applicable

Recommended Reading


Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-MARO-011Not applicable
I-MARO-017Not applicable

Other Recommended Reading

AAOther Recommended Reading
I-MARO-011P. Lacomme, C. Prins & M. Sevaux Algorithmes de graphes, Editions Eyrolles, 2003. J. Dréo, A. Pétrowski, P. Siarry & E. taillard Métaheuristiques pour l'optimisation difficile, Editions Eyrolles, 2003.
I-MARO-017Not applicable
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term
Date de génération : 09/07/2021
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