Supervisor Name : Maria Di Mascolo et Jean-Marie Flaus
Doctoral School : IMEP 2
Start Date : october 2015
Financing - Context - Partnerships : IMEP2 a research grant is requested
Positioning and Challenges :
Background: Production systems are subject to high uncertainty. These uncertainties are even greater in Health Care production systems, for which the duration of care, for example, will vary depending on the medical staff, on the patient's condition, complications that can happen .
The management of the production system requires research of robust solutions which are reactive to uncertainties.
Generally, the risk of occurrence is integrated in a limited manner in the performance evaluation models. Most of the time, we simply set random times to take into account the effect of possible risks and uncertainties. But the occurrence of a failure can cause a change in the structure of the system (for example, upon failure of a machine, you can set up a degraded mode, using other resources until repair or replacement of the failed machine). In this case, the solution of the degraded performance evaluation will be done by modifying the original model for each of the possible risks, and evaluating the performance of new solutions.
Our previous work (thesis of Khalil Negrichi) allowed the development of a tool to assess the performance of a production system taking into account the occurrence of failures and their propagation. In this thesis we are looking to enrich this tool by incorporating decisions following the occurrence of failures, and formulating uncertainties on different parts of the model, and the relationships between these uncertainties.
Objective: The objective of this work is thus to develop an integrated approach to analyze production systems, ranging from risk analysis to evaluate performance, taking into account the uncertainties efficiently.
We will seek to develop methodological tools enabling to:
- model different operating procedures in degraded mode, ie. decision making (following the occurrence of fault) in order to reduce the unavailability of resources and the risk of dysfunction. The developed models will take into account the uncertainties.
- define, model and solve new optimization problems using meta-heuristics, in the context of improving the organization of a hospital sterilization service. We will take particular account of additional constraints to those that have already been considered in the thesis of O. Ozturk, and different objectives: we will study objectives linked with the costs (essential in the current socio-economic context), but also the objectives related to sterilization devices, such as waiting times, pre-disinfection, and we will seek to better take into account the uncertainties in the arrivals of medical devices. We will also try to perform a modeling of the tasks of the human operator in connection with ergonomists.
Secondly, the model obtained will be used for:
- study the impact of different operating procedures in degraded mode performance of a sterilization service, taking into account the occurrence of failures, their propagation.
- study the impact of flow optimization policies on the performance of a sterilization service, taking into account the occurrence of failures, their propagation.
- study the impact of different flow management policies. Our work will be to look for policies that seem interesting and then test by simulation the impact they will have on the performance of the sterilization service, always taking into account the failures and their propagation.
This thesis will mobilize skills in flow modeling (simulation, queuing networks, Petri nets), in scheduling and programming, in risk analysis-oriented mode
Knowledge and skills