Freight railway crew scheduling consists of generating crew duties for operating trains on a schedule at minimal cost while meeting all work regulations and operational requirements. Typically, a freight railway operation uses thousands of trains and requires thousands of crew members to operate them. Because of the problem's large size, even moderate percentage savings in crew costs translate into large monetary savings. However, freight railway operations are complex, and a crew-scheduling problem is difficult to solve. We describe the development and implementation of crew-scheduling software at DB Schenker, the largest European railway freight carrier. The software is based on a column-generation solution technique. Computational results demonstrate that high-quality solutions can be obtained using reasonable run times, even for large problem instances. We implemented all of DB Schenker's major requirements to ensure that the software is operationally viable. Management also uses this software as a decision support tool for strategic planning.
In automatic recognition of unrestricted handwriting the ambiguities can be solved by top-down processing. However, automatic systems never have access to the extended background knowledge available to human readers. In order to replace this higher-level information we need to improve the reliability of the bottom-up processing. A handwriting-recognition system can be split up into six discrete blocks: (1) digitizing, word segmentation, pre-processing, and segmentation into strokes, (2) normalization of global handwriting parameters, (3) extraction of features per stroke, (4) allograph recognition, (5) optional word hypothesization, and, in order to allow recognition (6) a learning phase. The present paper discusses the design of three of these processing blocks: normalization, allograph recognition, and learning and brie y speci es feature extraction. Normalization concerns orientation, size, and slant. However, various alternative algorithms can be chosen and some algorithms yield more reliable results than others. A mechanism is proposed that will, sooner or later, nd the most appropriate normalization algorithms. Consequently, the features extracted from each stroke in the handwriting pattern will be more uniform within a writer and even between writers. In the recognition phase, handwriting patterns are segmented into allographs using an algorithm that can handle allographs with various numbers of strokes and with optional connection strokes between them. In order to teach the recognizer the allographs a method has been designed that builts non-interactively a lexicon of allographs by automatically discovering the allographs in a large corpus of cursive script.
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