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Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, without(More)
In this study, we consider the application of a simulated annealing (SA) heuristic to the truck and trailer routing problem with time windows (TTRPTW), an extension of the truck and trailer routing problem (TTRP). TTRP is a variant of the well-known well-studied vehicle routing problem (VRP). In TTRP, some customers can be serviced by either a complete(More)
The broad applications of cellular manufacturing make flowline manufacturing cell scheduling problems with sequence dependent family setup times a core topic in the field of scheduling. Due to computational complexity, almost all published studies focus on using permutation schedules to deal with this problem. To explore the potential effectiveness of(More)
Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that(More)