Dynamic positioning of idle automated guided vehicles

An automated guided vehicle (AGV) is a mobile robot commonly used to carry loads in material handling systems (MHS). Once a transfer is completed, an AGV stops at a home position, a point where it can park until it is assigned a new task. Determining the home positions is an important control problem with a direct influence on the overall performance of the MHS. The problem can be viewed as a location-allocation problem on a network. In this paper two fast and effective heuristics which dynamically determine the home positions are proposed. The methods were tested using two real-world instances. The obtained results are shown and discussed.     

Bayesian analysis of repairable systems showing a bounded failure intensity

The failure pattern of repairable mechanical equipment subject to deterioration phenomena sometimes shows a finite bound for the increasing failure intensity. A non-homogeneous Poisson process with bounded increasing failure intensity is then illustrated and its characteristics are discussed. A Bayesian procedure, based on prior information on model-free quantities, is developed in order to allow technical information on the failure process to be incorporated into the inferential procedure and to improve the inference accuracy. Posterior estimation of the model-free quantities and of other quantities of interest (such as the optimal replacement interval) is provided, as well as prediction on the waiting time to the next failure and on the number of failures in a future time interval is given. Finally, numerical examples are given to illustrate the proposed inferential procedure.     

An Analytical Model to Measure the Effectiveness of Safety Management Systems: Global Safety Improve Risk Assessment (G-SIRA) Method

The ever-increasing complexity of production systems, together with the need to obtain efficient processes with limited costs, has led companies to develop custom tools for process control and management. Even for risk assessment, the traditional models often are overcome by methods that are best suited to specific needs. In this context, the aim of this paper was to propose a new model, which we call the global safety improve risk assessment (G-SIRA). This model can classify risks and identify corrective actions that allow the best risk reduction at the lowest cost. The proposed model, which is based on improvements to previous research, uses the analytic hierarchy process approach to develop a valid and simple tool for risk management. The G-SIRA method has been tested in a real-world application, i.e., it was applied to all of the processes of a textile company, and the results were compared with those obtained from the classical approach failure mode, effects, and criticality analysis. The comparison clearly showed the effectiveness of the proposed model.     

Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model

The leaf area index (LAI), defined as the one-sided green leaf area per unit ground area, is used in many numerical weather prediction (NWP) models as an indicator of the vegetation development state, which is of paramount importance to characterize land evaporation, photosynthesis, and carbon-uptake processes. LAI is often simply represented by lookup tables, dependent on the vegetation type and seasons. However, global LAI datasets derived from remote sensing observations have more recently become available. These products are based on sensors such as the Advanced Very High Resolution Radiometer (AVHRR) or the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard polar orbiting satellites that can cover the entire globe within typically 3 days and with a spatial resolution of the order of 1 km.

We examine the meteorological impact of satellite-derived LAI products on near-surface air temperature and humidity, which comes both from the stomatal transpiration of leaves and from the intercepted water on the surface of leaves, re-evaporating into the atmosphere.

Two distinct monthly LAI climatology datasets derived respectively from AVHRR and MODIS sensors are tested. A set of forecasts and data assimilation experiments with the integrated forecasting system of the European Centre for Medium-range Weather Forecasts is performed with the monthly LAI climatology datasets as opposed to a vegetation-dependent constant LAI. The monthly LAI is shown to improve the forecasts of near-surface (screen-level) air temperature and relative humidity through its effect on evapotranspiration, with the largest impact obtained over needleleaf forests, crops, and grassland. At longer time-scales, the introduction of the monthly LAI is shown to have a positive impact on the model climate particularly during the boreal spring, where the LAI climatology has a large seasonal cycle.     

Hexagrid – hexagonal tube structures for tall buildings: patterns,modeling, and design

This paper provides a first insight on tube configurations based on the hexagonal shape (hexagrid) for tall buildings. The idea is to investigate the mechanical properties of hexagrid to assess their applicability in tall buildings and to compare their potential efficiency to the more popular diagrid systems. For the above purposes, a general homogenization approach has been established for dealing with any structural patterns, and a methodology for characterizing the structural patterns from the mechanical point of view has been developed and specified for hexagrids and diagrids. Then on the basis of a simple stiffness criterion, a design procedure has been proposed and applied to a tall building case study, and several structural solutions (both hexagrids and diagrids) have been designed and assessed by varying the major geometrical parameters of the patterns. Copyright © 2015 John Wiley & Sons, Ltd.     

Complex event processing with T-REX

Several application domains involve detecting complex situations and reacting to them. This asks for a Complex Event Processing (CEP) middleware specifically designed to timely process large amounts of event notifications as they flow from the peripheral to the center of the system, to identify the composite events relevant for the application. To answer this need we designed T-Rex, a new CEP middleware that combines expressiveness and efficiency. On the one hand, it adopts a language (TESLA) explicitly conceived to easily and naturally describe composite events. On the other hand, it provides an efficient event detection algorithm based on automata to interpret TESLA rules. Our evaluation shows that the T-Rex engine can process a large number of complex rules with a reduced overhead, even in the presence of challenging workloads.     

Environmental data processing by clustering methods for energy forecast and planning

This paper presents a statistical approach based on the k-means clustering technique to manage environmental sampled data to evaluate and to forecast of the energy deliverable by different renewable sources in a given site. In particular, wind speed and solar irradiance sampled data are studied in association to the energy capability of a wind generator and a photovoltaic (PV) plant, respectively. The proposed method allows the sub-sets of useful data, describing the energy capability of a site, to be extracted from a set of experimental observations belonging the considered site. The data collection is performed in Sicily, in the south of Italy, as case study. As far as the wind generation is concerned, a suitable generator, matching the wind profile of the studied sites, has been selected for the evaluation of the producible energy. With respect to the photovoltaic generation, the irradiance data have been taken from the acquisition system of an actual installation. It is demonstrated, in both cases, that the use of the k-means clustering method allows data that do not contribute to the produced energy to be grouped into a cluster, moreover it simplifies the problem of the energy assessment since it permits to obtain the desired information on energy capability by managing a reduced amount of experimental samples. In the studied cases, the proposed method permitted a reduction of the 50% of the data with a maximum discrepancy of 10% in energy estimation compared to the classical statistical approach. Therefore, the adopted k-means clustering technique represents an useful tool for an appropriate and less demanding energy forecast and planning in distributed generation systems.     

A mixed-Weibull regression model for the analysis of automotive warranty data

This paper presents a case study regarding the reliability analysis of some automotive components based on field failure warranty data. The components exhibit two different failure modes, namely early and wearout failures, and are mounted on different vehicles, which differ among themselves for car model and engine type, thus involving different operating conditions. Hence, the failure time of each component is a random variable with a bimodal pdf which also depends upon a vector of covariates that indexes the specific operating condition. Then, a mixed-Weibull distribution, where the pdf of each subpopulation (namely the ‘weak’ and ‘strong’ subpopulation) depends on the covariates through the scale parameter, is used to analyze the component lifetime. A Fortran algorithm for the maximum likelihood estimation of model parameters has been implemented and a stepwise procedure, in its backwards version, has been used to test the significance of covariates and to construct the regression model. The presence of a weak subpopulation has been verified and the fraction of weak units in the population has also been estimated. Finally, the adequacy of the proposed model to fit the observed data has been assessed.