A distance based variable neighborhood search for parallel machine scheduling

Abstract

Throughout the years, scheduling problems have been broadly addressed in the literature due to their wide application in practice. Some examples include the production line optimization, the scheduling aircraft landing, the daily nurse care, among others. In this work one investigate the efficiency of applying geometric-based operators in a version of this problem that deals with the schedule of independent tasks for parallel machines, which can be either identical or unrelated. In order to validate this study, a Variable Neighborhood Search approach is proposed and applied to a specific scheduling problem regarding the minimization of the weighted sum of the earliness/tardiness task, a well-known NP-Hard problem. The test instances are solved for either a due date known a priori or not. The algorithm is compared with two other methods from the literature and the results show promising.

Large scale similarity-based time series mining

“…When searching for patterns on time series, several invariances are required (BATISTA et al, 2014). As earlier discussed, ψ-DTW is a distance measure to provide invariance to spurious endpoints.…”