Prognostics-based scheduling in a distributed platform: Model, complexity and resolution

In the field of production scheduling, this paper addresses the problem of maximizing the production horizon of a heterogeneous platform composed of identical parallel machines and which has to provide a given production service. Each 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 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 machines. In the homogeneous case, we propose the Longest Remaining Useful Life first algorithm (LRUL) to find a solution and we prove its optimality. The NP-Completeness of the general case is then shown. Many heuristics are finally proposed to cope with the decision problem and are compared through simulation results. Simulations assess the efficiency of these heuristics. Distance to the theoretical maximal value comes close to 5% for the most efficient ones.