We explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (T eff , log g, and [Fe/H]) for Galactic F-and G-type stars. The ANNs are fed with medium-resolution (∆λ ∼ 1 − 2Å ) non flux-calibrated spectroscopic observations. From a sample of 279 stars with previous high-resolution determinations of metallicity, and a set of (external) estimates of temperature and surface gravity, our ANNs are able to predict T eff with an accuracy of σ(T eff ) = 135 − 150 K over the range 4250 ≤ T eff ≤ 6500 K, log g with an accuracy of σ(log g) = 0.25 − 0.30 dex over the range 1.0 ≤ log g ≤ 5.0 dex, and [Fe/H] with an accuracy σ([Fe/H]) = 0.15 − 0.20 dex over the range −4.0 ≤ [Fe/H] ≤ +0.3. Such accuracies are competitive with the results obtained by fine analysis of high-resolution spectra. It is noteworthy that the ANNs are able to obtain these results without consideration of photometric information for these stars. We have also explored the impact of the signal-to-noise ratio (S/N) on the behavior of ANNs, and conclude that, when analyzed with ANNs trained on spectra of commensurate S/N, it is possible to extract physical parameter estimates of similar accuracy with stellar spectra having S/N as low as 13. Taken together, these results indicate that the ANN approach should be of primary importance for use in present and future large-scale spectroscopic surveys.
This paper presents a mixed-integer programming model for a variant of the pickup and delivery problem with time windows. The fleet is assumed to be heterogeneous with a novel feature that allows the vehicles to be configured before service begins to handle various types of demand. The work was motivated by a daily route planning problem arising at a senior activity center. A fleet of configurable vans is available each day to transport participants to and from the center, as well as to secondary facilities for rehabilitative and medical treatment. The number of participants and support equipment that a van can accommodate depends on how it is configured. An exact method is introduced based on branch and price and cut. At each node in the search tree, the master problem is solved by column generation to find a lower bound. To improve the bound, subset-row inequalities are applied to the variables of the master problem. Columns are generated by solving the pricing subproblems with a labeling algorithm enhanced by new dominance conditions. Local search on the current set of columns is used to quickly find promising additions. Implementation details and ways to improve the performance of the overall procedure are discussed. Testing was done on a set of real instances as well as a set of randomly generated instances with up to 50 customer requests. The results show that optimal solutions are obtained in the majority of cases.
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