1,720,957 research outputs found
A pricing problem with unknown arrival rate and price sensitivity
No full textWe study a pricing problem with finite inventory and semi-parametric demand uncertainty. Demand is a price-dependent Poisson process whose mean is the product of buyers’ arrival rate, which is a constant λ, and buyers’ purchase probability q(p) , where p is the price. The seller observes arrivals and sales, and knows neither λ nor q. Based on a non-parametric maximum-likelihood estimator of (λ, q) , we construct an estimator of mean demand and show that as the system size and number of prices grow, it is asymptotically more efficient than the maximum likelihood estimator based only on sale data. Based on this estimator, we develop a pricing algorithm paralleling (Besbes and Zeevi in Oper Res 57:1407–1420, 2009) and study its performance in an asymptotic regime similar to theirs: the initial inventory and the arrival rate grow proportionally to a scale parameter n. If q and its inverse function are Lipschitz continuous, then the worst-case regret is shown to be O((log n/ n)
1 / 4). A second model considered is the one in Besbes and Zeevi (2009, Section 4.2), where no arrivals are involved; we modify their algorithm and improve the worst-case regret to O((log n/ n)
1 / 4). In each setting, the regret order is the best known, and is obtained by refining their proof methods. We also prove an Ω (n
- 1 / 2) lower bound on the regret. Numerical comparisons to state-of-the-art alternatives indicate the effectiveness of our arrivals-based approach.
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A flexible method for estimating inverse distribution functions in simulation experiments
No full textModeling and analyzing the breakdown process
No full textAn Operation Dependent Environment Change (ODEC) model has been proposed to model the breakdown process of a real production line. The model allows for an individual machine's breakdown rates to alternate between low and high when the machine is operational. The ODEC model is natural and allows for the times between breakdowns to be positively auto-correlated, which is what exhibits in the data from an engine production line of a major UK automotive manufacturer. A Markov-modulated Poisson Process with two hidden Markov states (MMPP2) method has been proposed to estimate the transition rates. This enables us to solve the performance measure of the production line, i.e. the throughput, analytically using a Markovian model
Dynamic pricing with finite price sets: a non-parametric approach
No full textWe study price optimization of perishable inventory over multiple, consecutive selling seasons in the presence of demand uncertainty. Each selling season consists of a finite number of discrete time periods, and demand per time period is Bernoulli distributed with price-dependent parameter. The set of feasible prices is finite, and the expected demand corresponding to each price is unknown to the seller, whose objective is to maximize cumulative expected revenue. We propose an algorithm that estimates the unknown parameters in a learning phase, and in each subsequent season applies a policy determined as the solution to a sample dynamic program, which modifies the underlying dynamic program by replacing the unknown parameters by the estimate. Revenue performance is measured by the regret: the expected revenue loss relative to the optimal attainable revenue under full information. For a given number of seasons n, we show that if the number of seasons allocated to learning is asymptotic to (n2logn)1/3, then the regret is of the same order, uniformly over all unknown demand parameters. An extensive numerical study that compares our algorithm to six benchmarks adapted from the literature demonstrates the effectiveness of our approach.</p
A case study on modelling and analysing machine breakdowns
No full textMost manufacturing models to date have assumed independence of all random variables in the system. In practice, autocorrelation effects are present in production lines time series. In this thesis, we extend this literature by studying autocorrelation in machine times to failure in detail. Our work focuses on the practical aspects of detecting and modelling autocorrelated uptimes, as well as including them in simulations.We apply a practical procedure to detect autocorrelation in uptimes. The procedure has very mild assumptions and compensates for the number of machines it is applied to, ensuring that the probability of a Type I error is kept low.We then provide two ways to model autocorrelated times to failures. The first is to use ARMA models including GARCH terms. We also provide a method based on the Markov-Modulated Poisson Process, a special case of the Markov Arrival Process.For both methods discussed above, we provide diagnostic plots and a quantitative way to select the most appropriate model for a given series of uptimes. This allows us to automatically select an appropriate model.Finally, to enable Ford to use our methods in simulation, we provide a way to generate simulated uptimes from each of our models
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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