Clara Simón de Blas

VARIABLE NEIGHBORHOOD SEARCH FOR ORDER BATCHING IN A WAREHOUSE

In this paper we address the problem of batching orders in a warehouse, with the objective of minimizing the total travel time. Order batching is an NP-hard optimization problem that is very difficult to solve exactly in practice. Thus, most implemented solutions are based on elementary heuristic methods that perform a relatively limited exploration of the solution space. As an alternative, we propose a heuristic based on variable neighborhood search, where the emphasis is placed on performing an intensive exploration of the most promising regions of the solution space. Simulations are conducted to study the performance of the method with different warehouse configurations, and an exhaustive comparative analysis, which considers all the best known heuristics, is carried out. The results obtained show that the proposed heuristic is competitive and that it provides a suitable method which can be used in practice. Additionally, since the performance of the algorithms depends heavily on factors such as storage policy, routing strategies, or the structure of the orders, we have developed an ANOVA in order to consider the effect of all the above factors on the different methods tested.

Combined social networks and data envelopment analysis for ranking

Abstract In this work, we propose a method for ranking efficient decision-making units (DMUs) that uses measures of dominance derived from social network analysis in combination with data envelopment analysis (DEA). For this purpose, a directed and weighted graph is constructed, in which the nodes represent the systems DMUs and the edges represent the relationships between them. The objective is to identify and rank the most important nodes by taking into account the influence or dominance relations between the DMUs. The method uses a weighted HITS algorithm to identify the hubs and the authorities in the network by assigning to each DMU two numbers, the authority weight and the hub weight. Additionally, this method allows for the identification of DMUs whose exclusion from the DEA analysis does not modify the efficiency values obtained for the remaining DMUs.

Forecasting financial short time series

We study the application of time series forecasting methods to massive datasets of short financial time series. In our example, the time series arise from analyzing monthly expenses and incomes in personal financial records. Differently from traditional time series forecasting applications, we work with series of very short depth (as short as 24 data points), which prevents from using classical exponential smoothing methods. However, this shortcoming is compensated by the the size of our dataset: millions of time series. The latter allows tackling the problem of time series prediction from a pattern recognition perspective. Specifically, we propose a method for short time series prediction based on time series clustering and distance-based regression. We experimentally show that this strategy leads to improved accuracy compared to exponential smoothing methods. We additionally describe the underlying big data platform developed to carry out the forecasts in an efficient manner (comparisons to millions of elements in near real-time).

Extracranial Course of the Facial Nerve Revisited: FACIAL NERVE REVISITED

Introduction: The extrapetrous course of the facial nerve has been a matter of study and debate since XIX century. Two different classifications have been classically proposed and widely accepted by most of the authors. Nevertheless, there are reported cases which do not fit in any of those. The aim of this study is to propose a new and useful classification. Material and methods: We have used 23 embalmed Caucasian adult cadavers (11 male and 12 female) belonging to the Bodies Donation and Dissecting Rooms Centre of the University Complutense of Madrid. The extra‐petrous facial nerve was dissected in the possible specimens resulting in 38 facial nerves. The studied parameters were length, diameter of divisions, terminal branches, and nerve connections. Results: In every specimen two main divisions were found, temporofacial and cervicofacial. They divided into five terminal branches from cranial to caudal: temporal, zygomatic, buccal, marginal or mandibular, and cervical. Based on the comparison with previous proposed classifications, we have unified the patterns in 12 types being the most frequent types the type 3 (eight cases, 21.05%), with connections between temporal, zygomatic and buccal branches and the type 8 (eight cases, 21.05%), a complex network between temporal, zygomatic, buccal, and mandibular branches. The number of terminal branches was so variable. Conclusion: We propose a new 12‐patterned classification which summarizes the previous ones. However, we consider that a good study of the number of terminal branches, connections between branches or with other cranial nerves are more useful for surgeons to avoid injuries to the facial nerve during surgery than complex classifications. Anat Rec, 302:599–608, 2019.

Records Properties of Non Stationary Time Series

This article compares the statistical properties of the records from independent and identically distributed time series with those of time series containing a single unit root. It is shown that there are important differences in both the limiting distributions and the convergence rates of the associated record counting processes. Since the record properties of independent and identically distributed time series are shared by a large class of stationary time series, the reported differences underline the possibility of using record-based statistics for robust testing procedures of the unit root hypothesis. We make an extension of the nonparametric test for the Range Unit Root test (RUR) proposed in Aparicio et al. (2006). We prove some properties for the test statistic in the context of the renewal theory and we suggest two new candidates to test the hypothesis of random walk with positive and negative drift.