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Heavy equipment overhaul facilities such as aircraft service centers and railroad yards face the challenge of minimizing the makespan for a set of preventive maintenance (PM) tasks, requiring single or multiple skills, within workforce availability constraints. In this paper, we examine the utility of evolution strategies to this problem. Comparison of the computational efforts of evolution strategies with exhaustive enumeration to reach optimal solutions for 60 small problems illustrates the ability of evolution strategies to yield optimal solutions increasingly efficiently with increasing problem size. A set of 852 large-scale problems was solved using evolution strategies to examine the effects of task-related problem characteristics, workforce-related variables, and evolution strategies population size (p) on CPU time. The results empirically supported practical utility of evolution strategies to solve large-scale, complex preventive maintenance problems involving single-and multiple-skilled workforce. Finally, comparison of evolution strategies and simulated annealing for the 852 experiments indicated much faster convergence to optimality with evolution strategies.

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Evolvable hardware combines the powerful search capability of evolutionary algorithms with the flexibility of reprogrammable devices, thereby providing a natural framework for reconfiguration. This framework has generated an interest in using evolvable hardware for fault-tolerant systems because reconfiguration can effectively deal with hardware faults whenever it is impossible to provide spares. But systems cannot tolerate faults indefinitely, which means reconfiguration does have a deadline. The focus of previous evolvable hardware research relating to fault-tolerance has been primarily restricted to restoring functionality, with no real consideration of time constraints. In this paper we are concerned with evolvable hardware performing reconfiguration under deadline constraints. In particular, we investigate reconfigurable hardware that undergoes intrinsic evolution. We show that fault recovery done by intrinsic reconfiguration has some restrictions, which designers cannot ignore.