Railway track maintenance is a critical problem for any railway administrator. More precisely, preventive maintenance scheduling is an NP-hard problem, which additionally involves multiple 1 Peralta, October 25, 2017 objectives such as economical cost, maximum capacity, serviceability, safety and passenger comfort. This paper proposes a multi-objective optimization approach to this problem, combined with a track deterioration model that takes into account the degradation caused by maintenance operations. The track behavior is simulated by an exponential deterioration model based on a two-level segmentation. The maintenance schedule is built using a Pareto-based algorithm with two objectives (cost and delay) and three constraints, on top of an initialization heuristic based on expert knowledge. The proposed approach has been tested with two different algorithms (NSGA-II and AMOSA), over a model of a real track, to create schedules for different horizons ranging between three and twenty years. The solutions obtained by AMOSA outperform those designed by human experts both in terms of time delay and economical cost, demonstrating the capability of the proposal to produce near-optimal long-term maintenance schedules.
Rank correlation measures are intended to measure to which extent there is a monotonic association between two observables. While they are mainly designed for ordinal data, they are not ideally suited for noisy numerical data. In order to better account for noisy data, a family of rank correlation measures has previously been introduced that replaces classical ordering relations by fuzzy relations with smooth transitions -thereby ensuring that the correlation measure is continuous with respect to the data. The given paper briefly repeats the basic concepts behind this family of rank correlation measures and investigates it from the viewpoint of robust statistics. Then, on this basis, we introduce a framework of novel rank correlation tests. An extensive experimental evaluation using a large number of simulated data sets is presented which demonstrates that the new tests indeed outperform the classical variants in terms of type II error rates without sacrificing good performance in terms of type I error rates. This is mainly due to the fact that the new tests are more robust to noise for small samples.
This paper considers the static flowshop-scheduling problem with the objective of minimizing, as a cost function, the mean job-completion time. Within the more general framework of combinatorial optimization problems, it defines a heuristic search technique-an approach that has been successful in the past in obtaining near-optimal solutions for problems that could not be solved exactly, either for lack of theory or because of exorbitant computational requirements. The paper presents a two-phase algorithm: The first phase searches among schedules with identical processing orders on all machines; the second refines the schedule by allowing passing. Results of computer study are presented for a large ensemble of pseudorandom problems, and for two particular problems previously cited in the literature. The method is shown to provide solutions that are exceptionally low in cost, and superior to those provided by sampling techniques in the cases for which comparison is possible. Computation time is also discussed and is given in machineindependent terms.
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