On completion of the course, engineering students will be able to:
● Analyse and model the dynamics of an industrial system
● Lead a simulation project
● Take a critical look at the system and its modelling

● What is it used for?
● Problem-based learning followed by a reverse-classroom activity.
● Case studies
● Discussion
● Application examples

● Examen écrit individuel composé de Problem solving, de questions ouvertes et de QCM
● Compte-rendu sur la modélisation d'un système industriel complexe à l'issue de TP (fichiers à rendre)
● Exposé oral autour de la gestion d'un projet de simulation

Numerical simulation is one of the most effective tools for decision-making and performance evaluation. The importance of its role in the industry of the future (Digital Twin, Twin Factory, etc.) therefore makes it an essential tool for IMT Atlantique engineers.

In addition to mastering simulation tools, this module has two key objectives:
● To introduce students to a systems-based approach, which they will go on to develop further. As such, this module will provide an initial introduction to systems engineering.
● To provide students with a methodology for managing a simulation project in an industrial context.

All these elements form a set of prerequisites that will be extensively utilised throughout the remainder of their degree programme and, more broadly, throughout their professional careers.

Lectures, tutorials, case studies, business simulation games, feedback from professionals

One individual written examination, two synthesis reports, and one group case study.

The overall aim of this module is to familiarise students with the stages of the decision-making process in the fields of supply chain management and production management, with a particular focus on short- and medium-term planning.

On completion of the course unit, engineering students will be able to:
- use the concept of a graph to model and solve complex problems using appropriate methods (specific algorithms, exact methods – PLNE, approximate methods – heuristics and meta-heuristics),
- determine the complexity of a problem,
- assess the algorithmic quality of an optimisation problem.

- Practical work involving the development of the various algorithms presented. Putting the approaches presented into practice.
- Tutorials designed to help students learn the mechanisms and tools presented more quickly, using various application examples.
- An escape game to put graph theory concepts into practice

- Examen écrit individuel composé de Problem solving, de questions ouvertes et de QCM
- Compte-rendu collectif de Problem solving à l'issue de TP (fichiers à rendre)

A generalist engineer is, by definition, a technical manager capable of solving a wide range of real-world optimisation problems, often based on observation, mathematical modelling and the ability to analyse the context and constraints at hand. From a scientific and technical perspective, this module lies at the heart of operational research issues.
The overall aim of this module is to examine the concept of a graph, from theory through to operational practice, in relation to various problems that can be modelled using this mathematical structure. A graph is a classic data structure used to model many combinatorial optimisation problems. It is a structure that is simple to understand but offers a wealth of possibilities for manipulation.

At the end of the UE, engineering students will be able to:
• Model optimization problems encountered in the industry as linear programs and mixed-integer linear programs.
• Use a commercial solver to solve mathematical models, and understand the algorithms used by the solver.
• Implement (meta) heuristics to solve complex and large scale decision-making problems encountered in the industry.
• Determine the complexity of a problem and choose the appropriate method to solve a problem.

• Problem-based learning
• Case studies
• Lectures
• Practical work
• Project-based learning

• Individual written examination 1: Formulate practical problems in terms of programming (using integers). This examination assesses competence BCT3.
• Practical work and project: Propose metaheuristics and exact methods to solve problems encountered in practice. This assessment evaluates competence BCT4.

Decision-support tools and optimisation methods are key elements of business digitalisation and the smart factory. These tools are used to (semi-)automate decisions relating to production planning, production scheduling, vehicle routing, supply chain design, production system design, and so on. These tools are largely based on operational research methods.

To be able to solve the optimisation problems encountered in practice and suggest sound decisions, it is necessary to understand and model the problem correctly, apply the appropriate optimisation tools, and possess the skills required to solve the mathematical model. The aim of this course is to teach students how to formulate, analyse and solve mathematical models representing real-world problems.

This course aims to provide students with a solid foundation in operational research. This module forms a fundamental basis for any engineer specialising in the ‘factory of the future’.

At the end of this course, students will be able to:
- perform an LCA using SIMAPRO software,
- reduce the environmental impact of a product during its design phase.

tutorials, and practical work

- QCM
- Examen écris

- Acquire knowledge of eco-design and LCA.
- Become proficient in using the SIMAPRO tool
- Be able to carry out LCAs on practical case studies and implement eco-design recommendations.

Upon completion of the course, engineering students will be able to:
● Understand the progression of a systems engineering project
● Draw up functional specifications for the system to be designed
● Carry out a risk analysis during the design phase of a system
● Define the characteristics of added value based on client specifications
● Manage the costs of maintaining a system in operational condition
● Consider a product or service throughout its entire life cycle

● Classes
● Case studies
● Professional experience feedback
● Completing a project

● Report and oral presentation of a systems engineering project
● Written progress reports for the project at each development phase

The aim of this module is to provide students with the fundamentals of systems engineering and to enable them to understand the various phases of a systems engineering project. The design of a product, service or organisation is a delicate and crucial phase for its development and long-term viability. In particular, it ensures that the characteristics of a system (whether technological or organisational) meet customer expectations in terms of risk specifications and functional requirements, by identifying any deviations from the objective as early as possible in the product lifecycle. These challenges make it essential to carry out appropriate analyses right from the design phase, drawing on a range of synthesis and validation methods prior to the software development of a product or service.

At the end of this course, engineering students will be able to:
(1) Explore massive datasets and perform data cleaning and transformation to make them ready for analysis.
(2) Apply supervised and unsupervised machine learning models to complex datasets to address real-world problems.
(3) Use Python programming tools to efficiently handle, analyze, and visualize data.
(4) Present results clearly and visually using libraries like Matplotlib and Seaborn.

- Problem-based learning
- Tutorials & practical sessions
- Case studies

● 1 examen écrit
● Deux Mini-projets

The main aim of the module is to train students to master the essential techniques of data science, enabling them to process, analyse and utilise big data.

By the end of the module, students will be able to:
- Gain an in-depth understanding of data processing and analysis methods.
- Apply machine learning models to complex datasets.
- Use IT tools for data manipulation and visualisation.

The course aims to enable engineering students to master a range of stochastic optimisation methods, to be able to put them into context and to determine which are most appropriate for the situation they are facing.
By the end of the module, engineering students will be able to:
• model stochastic phenomena and integrate them into classical optimisation models,
• understand the value of incorporating risk into their decision-making at both the strategic and tactical levels,
• comprehend the consequences of their decisions on the future state of a process,
• develop appropriate approximations of complex problems to enable their resolution.

- Course
- Case studies

Project reports and presentations upon completion of the projects

Uncertainties surrounding production processes (of goods or services) are ever-present: market fluctuations, environmental and human factors, industrial risks, etc., are all factors that may affect the performance of industrial systems. It is therefore important that future engineers adopt an appropriate scientific approach to manage these uncertainties and take them into account in their decision-making.
The aim of this module is to introduce a range of optimisation and decision-support methods that will enable students, on the one hand, to incorporate these uncertainties into mathematical models and, on the other hand, to solve these models using appropriate techniques. Through the study of various case studies, students will be able to apply different approaches depending on the objective to be achieved and thus gain a relevant understanding of the trade-off between performance and risk management. Furthermore, the optimisation tools introduced may be utilised or supplemented by other modules within the TAF programme, such as Risk Management, Performance Management, Economic Performance or Performance Evaluation.

On completion of the programme, engineering students will be able to:
● Recognise and apply decision-making techniques in situations of uncertainty
● Assess the quality of a model
● Apply techniques to real-world data in real time

- Demonstrations
- Case studies
- Problem solving
- Discussion

- Compte-rendu collectif de Problem solving à l'issue de TP (fichiers à rendre)

This module aims to provide the tools needed to understand these techniques. It covers their mathematical and computational foundations, the main areas they encompass, and the key concepts and algorithms. Among other topics, it will cover classification methods and reinforcement learning, as well as graphical models for Bayesian inference. It will focus in particular on descriptive learning based on the concept of probability distributions and will place a strong emphasis on practical application.

At the end of this module, the students must:
- Have a clear overview of what is research, especially in France
- Know the different types of collaboration between academics and industries
- Know the basic steps of the methodology to develop some research works.
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Soutenance devant un jury

Engineers who complete this IMT-Atlantique apprenticeship programme are set to drive both technical and organisational innovation. As a result of this role, they will find themselves at the crossroads between industry and research. It is therefore essential to develop an understanding of the world of research, its practices, challenges, funding and opportunities for interaction with industry: Consequently, the future engineer graduating from this programme will act as a key link between industry, innovation and research, thereby creating scope for progress and driving innovation within their organisation.

At the end of the UE, the engineering students, will be able to:
● Model a production optimization problem and determine its complexity
● Recognize a standard problem and choose an adapted software
● Select the data relevant to the description and resolution of the problem addressed,
● Take into account the uncertainties in the problem considered,
● Develop an optimization algorithm to solve specific problems

Translated with www.DeepL.com/Translator (free version)

● What is it for ?
● Problem-based learning followed by a reverse-classroom activity.
● Demonstrations
● Case studies
● Problem solving
● Discussion
● Application examples

● Individual written examination comprising problem-solving tasks, open-ended questions and multiple-choice questions
● Group report on problem-solving tasks following practical sessions (files to be submitted)
● Group oral presentation on a case study
● Group reports relating to problem-based learning

Production, by its very nature, plays a central role in the life of a business, as it is, so to speak, its primary objective. Improving the production process is therefore a key priority that enables a business to maintain or even enhance its competitiveness in increasingly competitive markets. It is against the backdrop of Industry 4.0 and the digitalisation of businesses that production optimisation, through its techniques, can reveal the full extent of its potential, thereby enabling decision-making support that is as efficient as it is rapid and responsive, and consequently becoming a particularly effective driver of performance. A sound understanding of the decision-support techniques applied in this context is therefore essential for an engineer who has completed this apprenticeship programme.

At the end of the UE, the engineering students, will be able to:
● Construct a relevant measure that characterizes the expected performance of a system
● Set up a global performance control policy
● Define the means to be implemented to restore the system to its nominal phase if necessary
● Establish a knowledge and information management system for the capitalization and analysis of knowledge (experience feedback)
● Audit an industrial system
● Plan the production of an industrial system under constraint

● A quoi ça sert ?
● Problem-based learning followed by a reverse-classroom activity.
● Demonstrations
● Case studies
● Problem solving
● Discussion
● Application examples

● Individual written examination comprising problem-solving tasks, open-ended questions and multiple-choice questions
● Group report on problem-solving tasks following practical sessions (files to be submitted)
● Group oral presentation on a case study
● Group reports relating to problem-based learning

Planning plays a vital role in production, as it helps to ensure the long-term viability and performance of the complex systems involved in manufacturing processes. The digitalisation of the company – and, in particular, its production system – paves the way for major developments in the techniques used in this context (we now speak of predictive maintenance, etc.). The aim of this module is therefore to present the key issues, software developments, models and techniques involved, so that engineering students can define appropriate strategies to ensure that the production system performs in line with the company’s production targets.

At the end of the course, the engineering students, will be able to:
● Model an optimization problem in transport and logistics
● Recognize a standard problem and choose an adapted software
● Develop an optimization algorithm to solve specific problems
● Integrate a professional team in many fields of industry and services

Lectures, tutorials/labs & seminars

● Individual written examination comprising problem-solving, open-ended questions and multiple-choice questions
● Group written report on a case study
● Group oral presentation on a literature review focusing on innovative applications (flipped classroom)

Transport and logistics are an integral part of modern societies. Numerous networks, modes and types of transport co-exist and sometimes utilise the same resources: mail and parcels, courier networks, international road haulage, multimodal transport, urban logistics networks, public transport and medical transport. Even the operation of warehouses and production lines involves internal challenges relating to the movement of goods and materials.
These transport networks have considerable impacts, for example in terms of pollution, delivery times, noise pollution and cost. Digital technology, and in particular operational research, is one of the key drivers for reducing these effects by optimising the use of resources.
Indeed, digital and analytical tools play a central role at numerous stages: network design; siting of logistics hubs and warehouses; choice of modes and means of transport; organisational and operational planning of the network; traceability; and real-time decision-making.
This course unit is taught in the third year of the programme. Its objectives are:
• To demonstrate how the skills acquired during the programme can be applied to transport and logistics;
• To develop new knowledge on the optimisation of transport networks;
• To familiarise students with industry-specific concepts and tools used in logistics and transport.

At the end of the course unit, students should be able to:
- analyze the impact of external disruptions on supply chains;
- identify the digital levers that can improve their flexibility and resilience;
- use digital supply chain tools (real-time data, digital twins, blockchain and artificial intelligence) to design, manage and optimize industrial systems in a context of uncertainty.

Lectures and tutorials or practical sessions delivered by experts in the field, conferences from industry professionals, and case studies.

Pupils will be assessed through projects based on case studies

The aim of this course is to examine the knock-on effects of external disruptions on the various links in the supply chain. Real-time data and new technologies (digital twins, blockchain, artificial intelligence, etc.) are used to optimise the flexibility and resilience of systems, from design through to operations. Real-world examples from industry are used as case studies.