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The Military Flight and Maintenance Planning (MFMP) Problem assigns missions and schedules maintenance operations (checks) for military aircraft. It has been studied in short, medium and long-term planning horizons ([1, 2, 5, 4]) and it has been proved NP-Hard in its long-term variant by [3]. The present model is an alternative formulation to that of [3] where mission assignments and maintenance cycles are modeled as start-stop assignment.

International audience; This abstract is focused on the Periodically Aggregated Resource-Constrained Project Scheduling Problem (PARCPSP) ), that can be seen as a continuous-time variant of a restricted Resource-Constrained Project Scheduling Problem with partially renewable resources (RCPSP/π) (Böttcher et. al. 1999). The purpose of this work is to compare an existing compact formulation with a new extended formulation.

The Military Flight and Maintenance Planning Problem considered here aims to assign missions and maintenance tasks to military aircraft. It is a variant of the Flight and Maintenance Planning problem where flights are not modeled geographically since a round-trip to the base is assumed for each flight. It also includes different objectives and constraints.

The Flight and Maintenance Planning (FMP) problem deals with the scheduling of maintenance operations and the assignment of flight activities to a fleet of aircraft. It has two main variants: civil (or commercial) and military. We study the latter, lesser known variant. Previous work include short and medium term planning.

This thesis is a study within the framework of the Helimaintenance project; a European project approved by the French aerospace valley cluster that aims to establish a center for civil helicopter maintenance which is also able to make R&D projects in the field. Our work consists of integrating uncertainties into both tactical and operational multiresources, multi-projects planning and dealing with Rough Cut Capacity Planning, Resource Leveling Problem and Resource Constraint Project Scheduling Problem under uncertainties. Uncertainty is modelled within a fuzzy/possibilistic approach instead of a stochastic approach since very limited data is available in our case of study. Three types of problems are referred in this study which are the Fuzzy Rough Cut Capacity Problem (FRCCP), the Fuzzy Resource Leveling Problem (FRLP) and the Fuzzy Resource Constraint Project Scheduling Problem (RCPSP). Moreover, a Genetic Algorithm and a Parallel SGS are provided to solve the FRLP and FRCPSP problems, respectively. A Simulated Annealing is provided to solve the FRCCP problem., Cette thèse entre dans le cadre du projet Hélimaintenance ; un project labellisé par le pôle de compétitivité Français Aérospace-Valley, qui vise à construire un centre dédié à la maintenance des hélicoptères civils qui soit capable de lancer des travaux en R&D dans le domaine. Notre travail consiste à prendre en considération les incertitudes dans la planification et l'ordonnancement de projets et résoudre les problèmes Rough Cut Capacity Planning, Resource Leveling Problem et Resource Constraint Project Scheduling Problem sous incertitudes. L'incertitude est modélisée avec l'approche floue/possibiliste au lieu de l'approche stochastique ce qui est plus adéquat avec notre cas d'étude. Trois types de problèmes ont été définis dans cette étude à savoir le Fuzzy Rough Cut Capacity Problem (FRCCP), le Fuzzy Resource Leveling Problem (FRLP) et le Fuzzy Resource Constraint Project Scheduling Problem (RCPSP). Un Algorithme Génétique et un Algorithme "Parallel SGS" sont proposés pour résoudre respectivement le FRLP et le FRCPSP et un Recuit Simulé est proposé pour résoudre le problème FRCCP.