Coordinated inventory replenishment and outsourced transportation operatoins

Cataloged from PDF version of article.We consider a one-warehouse N retailers supply chain with stochastic demand. Inventory is managed in-house whereas transportation is outsourced to a 3PL provider. We develop analytical expressions for the operating characteristics under both periodic and continuous joint replenishment policies. We identify the settings where a periodic review policy is comparable to a continuous review one. In our numerical test-bed, the periodic policy performed best in larger supply chains operating with larger trucks. We also observed that if the excess utilization charge is less than 25%, outsourcing becomes beneficial even if outsourcing cost is 25% more than the in-house fleet costs

Reentrant valence transition in EuO at high pressures: beyond the bond-valence model

The pressure-dependent relation between Eu valence and lattice structure in model compound EuO is studied with synchrotron-based x-ray spectroscopic and diffraction techniques. Contrary to expectation, a 7% volume collapse at $\approx$ 45 GPa is accompanied by a reentrant Eu valence transition into a $\emph{lower}$ valence state. In addition to highlighting the need for probing both structure and electronic states directly when valence information is sought in mixed-valent systems, the results also show that widely used bond-valence methods fail to quantitatively describe the complex electronic valence behavior of EuO under pressure.Comment: 5 pages, 4 figure

Quantum Algorithms for Learning and Testing Juntas

In this article we develop quantum algorithms for learning and testing juntas, i.e. Boolean functions which depend only on an unknown set of k out of n input variables. Our aim is to develop efficient algorithms: - whose sample complexity has no dependence on n, the dimension of the domain the Boolean functions are defined over; - with no access to any classical or quantum membership ("black-box") queries. Instead, our algorithms use only classical examples generated uniformly at random and fixed quantum superpositions of such classical examples; - which require only a few quantum examples but possibly many classical random examples (which are considered quite "cheap" relative to quantum examples). Our quantum algorithms are based on a subroutine FS which enables sampling according to the Fourier spectrum of f; the FS subroutine was used in earlier work of Bshouty and Jackson on quantum learning. Our results are as follows: - We give an algorithm for testing k-juntas to accuracy $\epsilon$ that uses $O(k/\epsilon)$ quantum examples. This improves on the number of examples used by the best known classical algorithm. - We establish the following lower bound: any FS-based k-junta testing algorithm requires $\Omega(\sqrt{k})$ queries. - We give an algorithm for learning $k$-juntas to accuracy $\epsilon$ that uses $O(\epsilon^{-1} k\log k)$ quantum examples and $O(2^k \log(1/\epsilon))$ random examples. We show that this learning algorithms is close to optimal by giving a related lower bound.Comment: 15 pages, 1 figure. Uses synttree package. To appear in Quantum Information Processin

Influence of Magnetism on Phonons in CaFe2As2 Via Inelastic X-ray Scattering

In the iron pnictides, the strong sensitivity of the iron magnetic moment to the arsenic position suggests a significant relationship between phonons and magnetism. We measured the phonon dispersion of several branches in the high temperature tetragonal phase of CaFe2As2 using inelastic x-ray scattering on single-crystal samples. These measurements were compared to ab initio calculations of the phonons. Spin polarized calculations imposing the antiferromagnetic order present in the low temperature orthorhombic phase dramatically improve agreement between theory and experiment. This is discussed in terms of the strong antiferromagnetic correlations that are known to persist in the tetragonal phase.Comment: 4 pages, 3 figures; added additional information and references about spin fluctuation

Improved Bounds on Quantum Learning Algorithms

In this article we give several new results on the complexity of algorithms that learn Boolean functions from quantum queries and quantum examples. Hunziker et al. conjectured that for any class C of Boolean functions, the number of quantum black-box queries which are required to exactly identify an unknown function from C is $O(\frac{\log |C|}{\sqrt{{\hat{\gamma}}^{C}}})$, where $\hat{\gamma}^{C}$ is a combinatorial parameter of the class C. We essentially resolve this conjecture in the affirmative by giving a quantum algorithm that, for any class C, identifies any unknown function from C using $O(\frac{\log |C| \log \log |C|}{\sqrt{{\hat{\gamma}}^{C}}})$ quantum black-box queries. We consider a range of natural problems intermediate between the exact learning problem (in which the learner must obtain all bits of information about the black-box function) and the usual problem of computing a predicate (in which the learner must obtain only one bit of information about the black-box function). We give positive and negative results on when the quantum and classical query complexities of these intermediate problems are polynomially related to each other. Finally, we improve the known lower bounds on the number of quantum examples (as opposed to quantum black-box queries) required for $(\epsilon,\delta)$-PAC learning any concept class of Vapnik-Chervonenkis dimension d over the domain $\{0,1\}^n$ from $\Omega(\frac{d}{n})$ to $\Omega(\frac{1}{\epsilon}\log \frac{1}{\delta}+d+\frac{\sqrt{d}}{\epsilon})$. This new lower bound comes closer to matching known upper bounds for classical PAC learning.Comment: Minor corrections. 18 pages. To appear in Quantum Information Processing. Requires: algorithm.sty, algorithmic.sty to buil

A general moment NRIXS approach to the determination of equilibrium Fe isotopic fractionation factors: application to goethite and jarosite

We measured the reduced partition function ratios for iron isotopes in goethite FeO(OH), potassium-jarosite KFe3(SO4)2(OH)6, and hydronium-jarosite (H3O)Fe3(SO4)2(OH)6, by Nuclear Resonant Inelastic X-Ray Scattering (NRIXS, also known as Nuclear Resonance Vibrational Spectroscopy -NRVS- or Nuclear Inelastic Scattering -NIS) at the Advanced Photon Source. These measurements were made on synthetic minerals enriched in 57Fe. A new method (i.e., the general moment approach) is presented to calculate {\beta}-factors from the moments of the NRIXS spectrum S(E). The first term in the moment expansion controls iron isotopic fractionation at high temperature and corresponds to the mean force constant of the iron bonds, a quantity that is readily measured and often reported in NRIXS studies.Comment: 38 pages, 2 tables, 8 figures. In press at Geochimica et Cosmochimica Acta. Appendix C contains new derivations relating the moments of the iron PDOS to the moments of the excitation probability function measured in Nuclear Resonant Inelastic X-ray Scatterin

Forecasting of Turkey inflation with hybrid of feed forward and recurrent artifical neural networks

Enflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir. Öngörülerin doğru bir şekilde elde edilmesi daha doğru kararlara neden olacaktır. Enflasyon öngörüsü için literatürde çeşitli zaman serileri teknikleri kullanılmıştır. Son yıllarda zaman serisi öngörü probleminde esnek modelleme yeteneği nedeniyle, Yapay Sinir Ağları (YSA) tercih edilmektedir. Yapay sinir ağları doğrusal veya eğrisel belirli bir model kalıbı, durağanlık ve normal dağılım gibi ön koşullara ihtiyaç duymadığından herhangi bir zaman serisine kolaylıkla uygulanabilmektedir. Bu çalışmada Tüketici Fiyat Endeksi (TUFE) için ileri ve geri beslemeli yapay sinir ağları yaklaşımı kullanılarak öngörüler elde edilmiştir. Çözümlemede kullanılan YSA modellerinin öngörülerinin girdi olarak kullanıldığı, YSA’ya dayalı yeni bir melez yaklaşım önerilmiştir.Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN) is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved specific model pattern, stationary and normal distribution. In this study, the predictions have been obtained using the feed forward and recurrent artificial neural network for the Consumer Price Index (CPI). A new combined forecast has been proposed based on ANN in which the ANN model predictions employed in analysis were used as data

Technology Acquisition and Utilization in Metal Goods

This paper analyzes the technology acquisition and utilization process in metal goods manufacturing organizations through a survey instrument which is conducted in the Mid-West region of the US. The goal of this study is to identify the channels, types, reasons, impacts, difficulties, and limitations of technology acquisition and utilization process in metal goods sector

NON-PARAMETRIC REGRESSION ESTIMATION FOR DATA WITH EQUAL VALUES

Parametric regression analysis depends on some assumptions. One of the most important of assumption is that the type of relationship between dependent and independent variable or variables is known. Under such circumstances, in order to make better assumptions, regression methods which enable flexibility in the linearity assumption of the parametric regression are needed. These methods are nonparametric methods known as semi parametric regression methods. Estimation of parameters in a parametric regression which has independent variables of different values has been studied extensively in literature. Sometimes, one or more observation series of independent variable values can be equal while dependent variable values are different. This study offers a new method for the estimation of regression parameters under such data. Proposed method and other nonparametric methods such as Theil, Mood-Brown, Hodges- Lehmann methods and OLS method were compared with the sample data and the results were evaluated