Subject Ph. D.

The redesign/reconfiguration of industrial systems relies, among other things, on behavioral models, notably discrete-event simulations (SED) [1, 2].The construction of these models is an important issue, as the performance of the redesign/reconfiguration depends on them. Several aspects come into play in the construction of these models, such as the time and expertise required to build them, as well as their validity, credibility and degrees of precision and granularity, depending on the modeling objective and targeted performance indicators [3]. In recent years, a number of studies [4-7] have looked at data-driven modeling/simulation as a means of automating the construction and consequent acceleration of more accurate and complete models of complex systems. The underlying principle of data-driven modeling/simulation is to build models using real data from the system (sensor measurements, computer system logs, etc.). Obtaining the data needed for data-driven modeling/simulation is a (major) bottleneck in the process [8]. Its main origins lie in the lack of knowledge and/or absence of the data needed to build the model for the redesign/reconfiguration problem being addressed. Eliminating this bottleneck requires the rapid availability of generic (reusable) models and suitable measurement systems.

In this context, in contrast to existing work that proposes ad hoc data-driven simulation models, a first objective is to propose generic models that can be rapidly adapted to specific needs. This requires first of all characterizing the data needed for simulation models according to the redesign/reconfiguration problem under consideration, and in parallel identifying the models that can be obtained from existing data, with regard to the performance indicators envisaged.

The data produced by the real system will therefore be used to instantiate the proposed generic models, and will also be used to connect the resulting simulation model with the real system, to the point of creating a digital shadow. This model will necessarily have to be updated in line with minor/major changes in the real system. A second objective, contiguous to the first, will therefore be to study the possibilities of modifying, dynamically over time, the structure of the initial model with the real system, while respecting the model's topology. In addition to the question of "how", there is also the question of "when" to carry out this update. This will require the study of drift detection approaches between real/virtual systems, such as [9,10], or data assimilation.

Particular attention wil need to be paid to the completeness of the data required to design/update simulation models. If additional data needs to be obtained, it will then be necessary to specify the measurement system requirements capable of meeting the information needs, with a view to assisting the human expert in the implementation of data collection through ephemeral or non-ephemeral instrumentation, as a parallel layer to the information systems already in place on the system of interest, rapidly implemented to acquire the missing data at lower cost.

This doctoral project will be carried out on a research/action basis. Research/action is a research methodology in which research activities and experimentation/observation in the factory mutually enrich each other. This research method is based on loops comprising 3 phases: Action planning taking into account existing scientific knowledge, Action/Observation to implement the proposals and Reflection to analyze the results obtained in the field. The end of a loop can give rise to a new one, whenever necessary. Two fields of application are envisaged for this work: on the one hand, the hospital world, with an application to the Saint-Etienne Pediatric Emergency Department, which will be the main target of the thesis project. Secondly, and more conventionally, in order to demonstrate the generalizability of the approach, we plan to apply this work to the school factories of the Haguenau IUT (University of Strasbourg). The techniques used will be those of discrete-event simulation (Witness, Arena, FlexSim software, etc.) and business engineering for model characterization.