Coupling Simulation and Machine Learning for

Il y a 2 mois


Grenoble, France Idex université Grenoble Alpes Temps plein

**Coupling simulation and machine learning for decision aid in supply chains**:

- Réf
- **ABG-114226**
- Sujet de Thèse- 07/05/2023- Contrat doctoral- Idex université Grenoble Alpes- Lieu de travail- Grenoble - Auvergne-Rhône-Alpes - France- Intitulé du sujet- Coupling simulation and machine learning for decision aid in supply chains- Champs scientifiques- Informatique
- Sciences de l’ingénieur

**Description du sujet**:

- Simulation and machine learning are two decision support tools commonly used independently for predictive analysis. Each of these methods has advantages and disadvantages, but they are also very complementary.- The simulation models rely on in-depth knowledge of business processes. It allows to observe and to understand causal effects between the events and offers an overview of the levers by which the system can be controlled. This provides the user with some transparency and explainability of the results obtained by these techniques. One of the advantages of simulation is that it does not require lots of input data to predict a future state of the system it models. As a consequence, it can be applied to cases where the data are missing or insufficient. However, the construction and the exploitation of the simulation models are time consuming. Even though the quantity of data is small, the models need to test lots of scenarios in order to capture as much relevant information on the system as possible. An important problem is then to be able to define the most interesting scenarios to test for the analysis. Nowadays, in most industries, these scenarios are defined using the judgement and the expertise of professionals, but they do not include all the scenarios that are potentially promising. In this context, the techniques of machine leaning can help to define the initial potentially promising scenarios to increase the efficiency [1, 2].- Contrary to the simulation, machine learning is a data centric approach. It is able to learn and to predict future data using exclusively information about what has already happened or is about to happen. The quality of data used is then critical: if the data include errors or bias, or if they are not representative of the future data or incomplete, the reliability of the predictions provided by machine learning is affected. Furthermore, machine learning can be seen as a black box. It provides a predictive analysis without explanations on how the learning algorithm determines the results. In this case, the simulation can be used, for example, to validate the recommendations provided by the learning method and to explain the results. It can also be useful to generate missing data of a data base in order to reduce the potential bias (e.g. for rare events).-
[1]
M. S. S. R. B. C. G. J. von Rueden L., Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions, vol. 12080, C. Springer, Éd., Berthold M., Feelders A., Krempl G. (eds) Advances in Intelligent Data Analysis XVIII. IDA 2020. Lecture Notes in Computer Science., 2020.
[2]
D. Ivanov et A. Dolgui, A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0, vol. 32:9, T. &. Francis, Éd., Production Planning & Control, 2021, pp. 775-788.

**Main Tasks**:

- A state of the art on the simulation and machine learning algorithms is expected. This literature review should help to enumerate the complementary characteristics of these two approaches.
- Design and development of models and algorithms
- Analysis of the performance of the coupling approaches and the limits of the proposed methods

**Nature du financement**:

- Contrat doctoral**Précisions sur le financement**:

- Projet MIAI**Présentation établissement et labo d'accueil**:

- Idex université Grenoble AlpesOne of the major research-intensive French universities, Univ. Grenoble Alpes** enjoys an international reputation in many scientific fields, as confirmed by international rankings. It benefits from the implementation of major European instruments (ESRF, ILL, EMBL, IRAM, EMFL* ). The vibring ecosystem, grounded on a close interaction between research, education and companies, has earned Grenoble to be ranked as the 5th most innovative city in the world. Surrounded by mountains, the campus benefits from a natural environment and a high quality of life and work environment. With 7000 foreign students and the annual visit of more than 8000 researchers from all over the world, Univ. Grenoble Alps is an internationally engaged university.
A personalized Welcome Center for international students, PhDs and researchers facilitates your arrival and installation.

Key figures:

- + 50,000 students including 7,000 international students
- 3,700 PhD students, 45% international
- 5,500 faculty members
- 180 different nationalities
- 1st city in France where it feels good to study and 5th city where it feels good to work
- ISSO: International Students & Sch



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