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).
• 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.).
• 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.
• 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.
• 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.
• 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.

Learning Outcomes of UE

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

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

Linear programming, notion of algorithm.

Q1 UE Assessment Comments

Not applicable

Type of Assessment for UE in Q2

• Written examination
• Practical test

Q2 UE Assessment Comments

Reports of practical works : 20%.   Written examination covering both parts of the course: Graph theory  : (theory and exercises)  40% Combinatorial optimization : (theory and exercises)  40%

Type of Assessment for UE in Q3

• Written examination
• Practical Test

Q3 UE Assessment Comments

Reports of practical works : 20%.   Written examination covering both parts of the course: Graph theory  : (theory and exercises)  40% Combinatorial optimization : (theory and exercises)  40%

Q1 UE Resit Assessment Comments (BAB1)

Not applicable