Task Scheduling to Extend Platform Useful Life using Prognostics

Abstract

In this paper, we aim at maximizing the useful life of a heterogeneous distributed platform which has to deliver a given production. The machines perform independent tasks and may be configured with different profiles (one nominal mode and several degraded ones). Depending on the profile, a machine reaches a given throughput. At each time the sum of the machine throughputs that are currently running determines the global throughput. Moreover, each machine is supposed to be monitored and a prognostic module gives its remaining useful life depending on both its past and future usage (profile). The objective is to configure the platform so as to reach the demand as long as possible. We propose to discretize the time into periods and to choose a configuration for each period. We propose an Integer Linear Programming (ILP) model to find such configurations for a fixed time horizon. Due to the number of variables and constraints in the ILP, the largest horizon can be computed for small instances of the problem. For larger ones, we propose polynomial time heuristics to maximize the useful life. Exhaustive simulations show that the heuristic solutions are close to the optimal (5% in average) in the case where the optimal horizon can be computed. For other platforms with a very large number of machines, simulations assess the efficiency of our heuristics. The distance to the theoretical maximal value is about 8% in average.