A machine learning approach for predictive warehouse design

Warehouse management systems (WMS) track warehousing and picking operations, generating a huge volumes of data quantified in millions to billions of records. Logistic operators incur significant costs to maintain these IT systems, without actively mining the collected data to monitor their business processes, smooth the warehousing flows, and support the strategic decisions. This study explores the impact of tracing data beyond the simple traceability purpose. We aim at supporting the strategic design of a warehousing system by training classifiers that can predict the storage technology (ST), the material handling system (MHS), the storage allocation strategy (SAS), and the picking policy (PP) of a storage system. We introduce the definition of a learning table, whose attributes are benchmarking metrics applicable to any storage system. Then, we investigate how the availability of data in the warehouse management system (i.e. varying the number of attributes of the learning table) affects the accuracy of the predictions. To validate the approach, we illustrate a generalisable case study which collects data from sixteen different real companies belonging to different industrial sectors (automotive, manufacturing, food and beverage, cosmetics and publishing) and different players (distribution centres and third-party logistic providers). The benchmarking metrics are applied and used to generate learning tables with varying number of attributes. A bunch of classifiers is used to identify the crucial input data attributes in the prediction of ST, MHS, SAS, and PP. The managerial relevance of the data-driven methodology for warehouse design is showcased for 3PL providers experiencing a fast rotation of the SKUs stored in their storage systems

Effects of dopamine infusion on forearm blood flow in critical patients.

Piazza O, Zito G, Valente A, Tufano R. Effects of dopamine infusion on forearm blood flow in critical patients. Med Sci Monit. 2006 Feb;12(2):CR90-3. Epub 2006 Jan 26

Hydrological control of soil thickness spatial variability on the initiation of rainfall-induced shallow landslides using a three-dimensional model

Thickness and stratigraphic settings of soils covering slopes potentially control susceptibility to initiation of rainfall-induced shallow landslides due to their local effect on slope hydrological response. Notwithstanding the relevance of the assessment of hazard to shallow landsliding at a distributed scale by approaches based on a coupled modelling of slope hydrological response and slope stability, the spatial variability of soil thickness and stratigraphic settings are factors poorly considered in the literature. Under these premises, this paper advances the well-known case study of rainfall-induced shallow landslides involving ash-fall pyroclastic soils covering the peri-Vesuvian mountains (Campania, southern Italy). In such a unique geomorphological setting, the soil covering is formed by alternating loose ash-fall pyroclastic deposits and paleosols, with high contrasts in hydraulic conductivity and total thickness decreasing as the slope angle increases, thus leading to the establishment of lateral flow and an increase of pore water pressure in localised sectors of the slope where soil horizon thickness is less. In particular, we investigate the effects, on hillslope hydrological regime and slope stability, of irregular bedrock topography, spatial variability of soil thickness and vertical hydraulic heterogeneity of soil horizons, by using a coupled three-dimensional hydrological and a probabilistic infinite slope stability model. The modelling is applied on a sample mountain catchment, located on Sarno Mountains (Campania, southern Italy), and calibrated using physics-based rainfall thresholds derived from the literature. The results obtained under five simulated constant rainfall intensities (2.5, 5, 10, 20 and 40 mm h−1) show an increase of soil pressure head and major failure probability corresponding to stratigraphic and morphological discontinuities, where a soil thickness reduction occurs. The outcomes obtained from modelling match the hypothesis of the formation of lateral throughflow due to the effect of intense rainfall, which leads to the increase of soil water pressure head and water content, up to values of near-saturation, in narrow zones of the slope, such as those of downslope reduction of total soil thickness and pinching out of soil horizons. The approach proposed can be conceived as a further advance in the comprehension of slope hydrological processes at a detailed scale and their effects on slope stability under given rainfall and antecedent soil hydrological conditions, therefore in predicting the most susceptible areas to initiation of rainfall-induced shallow landslides and the related I-D rainfall thresholds. Results obtained demonstrate the occurrence of a slope hydrological response depending on the spatial variability of soil thickness and leading to focus slope instability in specific slope sectors. The approach proposed is conceived to be potentially exportable to other slope environments for which a spatial modelling of soil thickness would be possible

Probabilistic approaches for assessing rainfall thresholds triggering shallow landslides. The study case of the peri-vesuvian area (Southern Italy)

Ash-fall pyroclastic soil deposits covering steep carbonate slopes in the peri-Vesuvian area (southern Italy) are periodically involved in shallow landslides (about 700 events were recorded during the last three centuries, as reported by CASCINI et alii, 2008), triggered by intense and/or prolonged rainfall events, which evolve as catastrophic debris flows. In the last decades, many studies have been focused on estimating reliable relationships among the triggering of shallow landslides and the amount and duration of rainfall events, as well as the role played by antecedent soil hydrological conditions. Results of these studies have been expected to give information on temporal hazard to landslide onset to be used for setting a reliable early warning system. In this paper we present probabilistic approaches to assess rainfall thresholds triggering shallow landslides by classical empirical methods and to manage the uncertainties related to biases of data. At this scope, rainfall events related to the occurrence of debris flows along slopes of the Sarno and Lattari Mountains, known from chronicles of the last century, were analyzed by means of the empirical models of Intensity-Duration (I-D) (CAINE, 1980) and rainfall recorded in the day of the landslide occurrence (P) vs the antecedent cumulated rainfall (Pa) (CROZIER & EYLES, 1980). In order to limit and to assess uncertainties related to biases of rainfall data, a comparison with the regional probability model of high intensity rainfall, carried out in the framework of the VAPI Project (ROSSI & VILLANI, 1994) has been carried out. Moreover, rainfall data were processed by a bivariate logistic regression model resulting in the assessment of probability to landslide triggering, given an assumed rainfall event. The I-D empirical rainfall thresholds obtained by Caine model (1980) were compared to rainfall thresholds estimated by deterministic approaches (DE VITA et alii, 2013; NAPOLITANO et alii, 2016) showing a good match

Seasonal and event-based hydrological and slope stability modeling of pyroclastic fall deposits covering slopes in Campania (Southern Italy)

The pyroclastic fall deposits mantling mountain slopes in the Campania region (Southern Italy) represent one of the most studied geomorphological frameworks of the world regarding rainfall-induced debris flows threating urban areas. The proposed study focused on advancing knowledge about the hydrological response of pyroclastic fall coverings from the seasonal to event-based time scales, leading to the initiation of slope instability. The study was based on two consequential tasks. The first was the analysis of a six-year monitoring of soil pressure head carried out in a sample area of the Sarno Mountains, located above a debris flow initiation zone. The second was based on coupled hydrological and slope stability modeling performed on the physical models of slopes, which were reconstructed by empirical correlations between the slope angle, total thickness, and stratigraphic settings of pyroclastic fall deposits mantling slopes. The results obtained were: (a) The understanding of a soil pressure head regime of the volcaniclastic soil mantle, always ranging in unsaturated conditions and characterized by a strong seasonal variability depending on precipitation patterns and the life cycle of deciduous chestnut forest; and (b) the reconstruction through a deterministic approach of seasonal intensity-duration rainfall thresholds related to different morphological conditions

a tailored maintenance management system to control spare parts life cycle

Abstract The maintenance of complex production systems became increasingly crucial to ensure the competitiveness of companies and service level to their clients. Because of product customization the number of mechanical and electrical components and functional groups of manufacturing lines enhanced with their complexity. To face this concern, the physical and logical design of such systems is typically partitioned among several groups of engineers and designers. Consequently, a holistic awareness of the whole project is lacking and the maintenance of such systems becomes even more challenging. In view of this, new tailored support-decision tools able to manage and control the life cycle of spare parts from their design, throughout the run time, and to their failure and replacement are necessary. This paper illustrates an original maintenance management system (MMS) resulting by the combination of different computerized tools able to integrate the information flow behind the life cycle of a generic component. The proposed system supports coordination among groups of engineers and practitioners through graphic user interfaces (GUIs) and performance i.e. cost, reliability, dashboards, which lead decision-making from the design phase to the planning of maintenance tasks along the life of the manufacturing line. These tools are validated with a real-world instance from the tobacco industry which allows assessing how components belonging to the same functional group may differently behave over their life cycle. The results suggest that the holistic awareness on the whole manufacturing system provided by the proposed MMS can support task design and schedule of maintenance actions providing the reduction of more than 20% of the total cost and time for maintenance actions. The practical example shown contributes to shed light on the potentials of new paradigms for maintenance management in the industry 4.0

S100B in Guillain-Barre syndrome.

BR J ANAESTH. 2006 JAN;96(1):141-2

Impact of the laminar flame speed correlation on the results of a quasi-dimensional combustion model for Spark-Ignition engine

Abstract In the present study, the impact of the laminar flame speed correlation on the prediction of the combustion process and performance of a gasoline engine is investigated using a 1D numerical approach. The model predictions are compared with experimental data available for full- and part-load operations of a small-size naturally aspirated Spark-Ignition (SI) engine, equipped with an external EGR circuit. A 1D model of the whole engine is developed in the GT-Power™ environment and is integrated with refined sub-models of the in-cylinder processes. In particular, the combustion is modelled using the fractal approach, where the burning rate is directly related to the laminar flame speed. In this work, three laminar flame speed correlations are assessed, including both experimentally- and numerically-derived formulations, the latter resulting from the fitting of laminar flame speeds computed by a chemical kinetic solver. Each correlation is implemented within the combustion sub-model, which is properly tuned to reproduce the experimental performance of the engine at full load. Then, the reliability of the considered flame speed formulations is proved at part-loads, even under external EGR operations

an application of collaborative robots in a food production facility

Abstract Despite the food industry being a leading sector of the European economy, the level of penetration of automation is still low. The main reasons lie on the small margin of food items which does not encourage technological investments, the extremely spread vendors market i.e. mostly small and medium enterprises, and the high level of flexibility and care required to handle food products along production, packaging, and storage operations. Nevertheless, the advent of collaborative, small and flexible robots provides great opportunities for the design and development of new effective processes integrating the human flexibility with the efficiency of automation. This paper explores the impact of adopting collaborative robots in the food catering industry, by illustrating a case study developed for the end-of-line of a catering production system. A generalizable methodology is proposed to support the study of the technical and economic feasibility of the implementation of such technology. This methodology is intended to support managers of the food industry to analyse the constraints that limit the automation of a process and to measure the expected performance of the system in terms of throughput, ergonomics and economic benefits resulting from the adoption of collaborative robots