A management of multi-stacks fuel cell systems is proposed to extend systems useful life in a Prognostics and Health Management (PHM) framework. The problem consists in selecting at each time which fuel cell stacks have to run and which output power has to be chosen for each of them to satisfy a load demand as long as possible. Multi-stacks fuel cell system useful life depends not only on each stack useful life, but also on both the schedule and the operating conditions settings that define the contribution of each stack over time. As the impact of variable operating conditions on fuel cell lifetime is not well-known, a simplified representation of fuel cell behavior under wear and tear is used to estimate the available outputs over time and their associated Remaining Useful Lives (RUL). This health state prognostics model is configured to suit to Proton-Exchange Membrane Fuel Cells (PEMFC) specific characteristics. The proposed scheduling process makes use of an optimal approach based on a Mixed Integer Linear Program (MILP). Efficiency of the associated commitment strategy is assessed by comparison with basic intuitive strategies, considering constant and piecewise constant load demand profiles.
In a post-prognostics decision context, this paper addresses the problem of maximizing the useful life of a platform composed of several parallel machines under service constraint. Application on multi-stack fuel cell systems is considered. In order to propose a solution to the insufficient durability of fuel cells, the purpose is to define a commitment strategy by determining at each time the contribution of each fuel cell stack to the global output so as to satisfy the demand as long as possible. A relaxed version of the problem is introduced, which makes it potentially solvable for very large instances. Results based on computational experiments illustrate the efficiency of the new approach, based on the Mirror Prox algorithm, when compared with a simple method of successive projections onto the constraint sets associated with the problem.
This paper adresses the problem of optimizing the useful life of a heterogeneous distributed platform which has to produce a given production service. The purpose is to provide a production scheduling that maximizes the production horizon. The use of Prognostics and Health Management (PHM) results in the form of Remaining Useful Life (RUL) allows to adapt the schedule to the wear and tear of equipment. This work comes within the scope of Prognostics Decision Making (DM). Each considered machine is supposed to be able to provide several throughputs corresponding to different operating conditions. The key point is to select the appropriate profile for each machine during the whole useful life of the platform. Many heuristics are proposed to cope with this decision problem and are compared through simulation results. Simulations assess the efficiency of these heuristics. Distance to the theoretical maximal value comes close to 10% for the most efficient ones. A repair module performing a revision of the schedules provided by the heuristics is moreover proposed to enhance the results. First results are promising.
In this work, we study the long-haul stage of parcel transportation, which involves the integration of the sorting operation allowing better consolidation of parcels in containers. The transportation is optimized over a complex two-level hybrid hub-and-spoke network and is conducted with a heterogeneous fleet; there are two types of vehicles which are balanced over the network on a daily basis with the management of empty trucks. We are not aware of a framework for long-haul parcel transportation in the literature that is sufficient to handle all aspects of the industrial problem we address. In terms of finding a solution, we propose a hierarchical algorithm with aggregate demands whose performance is related to the value of a truck filling rate threshold. Roughly speaking, the demands above this threshold can be routed directly while the ones below this threshold follow the hierarchical structure of the network. The routing of the two types of demands is optimized, first separately and then together in a multi-step process in which the subproblems are solved via Mixed Integer Linear Programs. Numerical experiments are carried out on datasets provided by a postal company (225 sites with 2500 demands). Various threshold values are tested to find out which one is the best, in terms of solution quality obtained and computational time.
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