Skip to Main content Skip to Navigation

Health management of industrial vehicles and fleet maintenance optimization: taking into account operation constraints and mission planning

Elodie Robert 1, 2
Abstract : This thesis work deals with the problems of joint scheduling for maintenance operations and missions for industrial vehicle fleets. The aim is to develop a methodology to adapt the joint scheduling of maintenance and missions according to the vehicles health state but also according to the missions features. These features correspond to the conditions of usage severity that have a significant impact on the truck deterioration and must be taken into account to adapt at best the maintenance operations schedule according to the deterioration evolution. The implementation of a decision support methodology to manage a fleet would improve productivity and reduce the maintenance costs while making the most of the fleet capacity. However, the joint scheduling problem for a fleet is a complex problem to solve and three main dimensions has to be considered. The first one is to jointly schedule missions and maintenance operations in a static case. The second one is to integrate the available monitoring information and the different events that can occur to update the schedule and treat the problem in a dynamic way. The third dimension is the fleet dimension that involves managing several vehicles in parallel. The first step is to jointly schedule the maintenance activity and the missions for a truck in a static case. It is assumed that all the missions to be performed are known and that no monitoring information is available. To do this, a vehicle deterioration model is defined to estimate its remaining useful lifetime to make decisions. It is a model with varying parameters since the vehicle operates under different conditions of usage severity according to the missions. It is the central point for setting up a scheduling algorithm to avoid any excessive risk of failure. The scheduling process is naturally optimized according to a criterion based on either the maintenance costs or the operating incomes. Once this methodology has been defined, it must be completed to include information on the vehicle deterioration, failure occurrences and new missions that may be requested. A dynamic approach has then been developed to solve the scheduling problem for a vehicle. If a breakdown occurs, the schedule must be updated because it is no longer adapted to the evolution of the current vehicle deterioration. Likewise, when a new mission is available, an update is essential because the priority order of the missions, defined by their deadlines, must be considered as soon as possible to avoid delay penalties. On the other hand, deterioration information can have a varying influence on the current schedule. Then, the schedule robustness has to be studied to avoid changing the mission order and the maintenance dates too often. The last step is to integrate the fleet dimension in the decision-making process. It is no longer just a question of mission order and timing for maintenance operations, but also of deciding which vehicle is assigned to which mission. The decision-making process then depends on the whole fleet. An analysis of the impact of considering the fleet dimension in the static case and then in the dynamic case is led. Simulation results are used to illustrate the developed methods and aim at showing their interest and the cost savings they generate.
Complete list of metadatas

Cited literature [213 references]  Display  Hide  Download
Contributor : Christophe Berenguer <>
Submitted on : Monday, March 23, 2020 - 10:01:13 AM
Last modification on : Wednesday, October 14, 2020 - 3:51:54 AM
Long-term archiving on: : Wednesday, June 24, 2020 - 1:26:19 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License


  • HAL Id : tel-02514920, version 1



Elodie Robert. Health management of industrial vehicles and fleet maintenance optimization: taking into account operation constraints and mission planning. Automatic. Communauté Universite Grenoble Alpes, 2019. English. ⟨tel-02514920⟩



Record views


Files downloads