Using floating car data to analyse the effects of ITS measures and eco-driving

The road transportation sector is responsible for around 25% of total man-made CO2 emissions worldwide. Considerable efforts are therefore underway to reduce these emissions using several approaches, including improved vehicle technologies, traffic management and changing driving behaviour. Detailed traffic and emissions models are used extensively to assess the potential effects of these measures. However, if the input and calibration data are not sufficiently detailed there is an inherent risk that the results may be inaccurate. This article presents the use of Floating Car Data to derive useful speed and acceleration values in the process of traffic model calibration as a means of ensuring more accurate results when simulating the effects of particular measures. The data acquired includes instantaneous GPS coordinates to track and select the itineraries, and speed and engine performance extracted directly from the on-board diagnostics system. Once the data is processed, the variations in several calibration parameters can be analyzed by comparing the base case model with the measure application scenarios. Depending on the measure, the results show changes of up to 6.4% in maximum speed values, and reductions of nearly 15% in acceleration and braking levels, especially when eco-driving is applied.

MILP for the inventory and routing problem for replenishment the car assembly line.

The inbound logistic for feeding the workstation inside the factory represents a critical issue in the car manufacturing industry. Nowadays, this issue is even more critical than in the past since more types of car are being produced in the assembly lines. Consequently, as workstations have to install many types of components, they also need to have an inventory of different types of the component in a compact space. The replenishment is a critical issue since a lack of inventory could cause line stoppage or reworking. On the other hand, an excess of inventory could increase the holding cost or even block the replenishment paths. The decision of the replenishment routes cannot be made without taking into consideration the inventory needed by each station during the production time which will depend on the production sequence. This problem deals with medium-sized instances and it is solved using online solvers. The contribution of this paper is a MILP for the replenishment and inventory of the components in a car assembly line.

Service Capacity Reserve under Uncertainty by Hospital’s ER Analogies: A Practical Model for Car Services

We define a capacity reserve model to dimension passenger car service installations according to the demographic distribution of the area to be serviced by using hospital?s emergency room analogies. Usually, service facilities are designed applying empirical methods, but customers arrive under uncertain conditions not included in the original estimations, and there is a gap between customer?s real demand and the service?s capacity. Our research establishes a valid methodology and covers the absence of recent researches and the lack of statistical techniques implementation, integrating demand uncertainty in a unique model built in stages by implementing ARIMA forecasting, queuing theory, and Monte Carlo simulation to optimize the service capacity and occupancy, minimizing the implicit cost of the capacity that must be reserved to service unexpected customers. Our model has proved to be a useful tool for optimal decision making under uncertainty integrating the prediction of the cost implicit in the reserve capacity to serve unexpected demand and defining a set of new process indicators, such us capacity, occupancy, and cost of capacity reserve never studied before. The new indicators are intended to optimize the service operation. This set of new indicators could be implemented in the information systems used in the passenger car services.

Service capacity reserve under uncertainty by hospital's ER's analogies: a practical model for car services

El objetivo de esta investigación consiste en definir un modelo de reserva de capacidad, por analogías con emergencias hospitalarias, que pueda ser implementado en el sector de servicios. Este está específicamente enfocado a su aplicación en talleres de servicio de automóviles. Nuestra investigación incorpora la incertidumbre de la demanda en un modelo singular diseñado en etapas que agrupa técnicas ARIMA, teoría de colas y simulación Monte Carlo para definir los conceptos de capacidad y ocupación de servicio, que serán utilizados para minimizar el coste implícito de la reserva capacidad necesaria para atender a clientes que carecen de cita previa. Habitualmente, las compañías automovilísticas estiman la capacidad de sus instalaciones de servicio empíricamente, pero los clientes pueden llegar bajo condiciones de incertidumbre que no se tienen en cuenta en dichas estimaciones, por lo que existe una diferencia entre lo que el cliente realmente demanda y la capacidad que ofrece el servicio. Nuestro enfoque define una metodología válida para el sector automovilístico que cubre la ausencia genérica de investigaciones recientes y la habitual falta de aplicación de técnicas estadísticas en el sector. La equivalencia con la gestión de urgencias hospitalarias se ha validado a lo largo de la investigación en la se definen nuevos indicadores de proceso (KPIs) Tal y como hacen los hospitales, aplicamos modelos estocásticos para dimensionar las instalaciones de servicio de acuerdo con la distribución demográfica del área de influencia. El modelo final propuesto integra la predicción del coste implícito en la reserva de capacidad para atender la demanda no prevista. Asimismo, se ha desarrollado un código en Matlab que puede integrarse como un módulo adicional a los sistemas de información (DMS) que se usan actualmente en el sector, con el fin de emplear los nuevos indicadores de proceso definidos en el modelo. Los resultados principales del modelo son nuevos indicadores de servicio, tales como la capacidad, ocupación y coste de reserva de capacidad, que nunca antes han sido objeto de estudio en la industria automovilística, y que están orientados a gestionar la operativa del servicio. ABSTRACT Our aim is to define a Capacity Reserve model to be implemented in the service sector by hospital's emergency room (ER) analogies, with a practical approach to passenger car services. A stochastic model has been implemented using R and a Monte Carlo simulation code written in Matlab and has proved a very useful tool for optimal decision making under uncertainty. The research integrates demand uncertainty in a unique model which is built in stages by implementing ARIMA forecasting, Queuing Theory and a Monte Carlo simulation to define the concepts of service capacity and occupancy, minimizing the implicit cost of the capacity that must be reserved to service unexpected customers. Usually, passenger car companies estimate their service facilities capacity using empirical methods, but customers arrive under uncertain conditions not included in the estimations. Thus, there is a gap between customer’s real demand and the dealer’s capacity. This research sets a valid methodology for the passenger car industry to cover the generic absence of recent researches and the generic lack of statistical techniques implementation. The hospital’s emergency room (ER) equalization has been confirmed to be valid for the passenger car industry and new process indicators have been defined to support the study. As hospitals do, we aim to apply stochastic models to dimension installations according to the demographic distribution of the area to be serviced. The proposed model integrates the prediction of the cost implicit in the reserve capacity to serve unexpected demand. The Matlab code could be implemented as part of the existing information technology systems (ITs) to support the existing service management tools, creating a set of new process indicators. Main model outputs are new indicators, such us Capacity, Occupancy and Cost of Capacity Reserve, never studied in the passenger car service industry before, and intended to manage the service operation.

CXR, a chicken xenobiotic-sensing orphan nuclear receptor, is related to both mammalian pregnane X receptor (PXR) and constitutive androstane receptor (CAR)

Nuclear receptors constitute a large family of ligand-modulated transcription factors that mediate cellular responses to small lipophilic molecules, including steroids, retinoids, fatty acids, and exogenous ligands. Orphan nuclear receptors with no known endogenous ligands have been discovered to regulate drug-mediated induction of cytochromes P450 (CYP), the major drug-metabolizing enzymes. Here, we report the cloning of an orphan nuclear receptor from chicken, termed chicken xenobiotic receptor (CXR), that is closely related to two mammalian xenobiotic-activated receptors, the pregnane X receptor (PXR) and the constitutive androstane receptor (CAR). Expression of CXR is restricted to tissues where drug induction of CYPs predominantly occurs, namely liver, kidney, small intestine, and colon. Furthermore, CXR binds to a previously identified phenobarbital-responsive enhancer unit (PBRU) in the 5′-flanking region of the chicken CYP2H1 gene. A variety of drugs, steroids, and chemicals activate CXR in CV-1 monkey cell transactivation assays. The same agents induce PBRU-dependent reporter gene expression and CYP2H1 transcription in a chicken hepatoma cell line. These results provide convincing evidence for a major role of CXR in the regulation of CYP2H1 and add a member to the family of xenobiotic-activated orphan nuclear receptors.

Clasificación y caracterización de zonas húmedas del litoral alicantino (SE España), mediante las metodologías CAR/ASP y MEDWET: estudio de caso del Saladar de Aguamarga y el Fondet de La Senieta

En este estudio se pretende caracterizar las comunidades vegetales presentes en ambientes litorales en el SE de la provincia de Alicante, mediante el cartografiado de zonas homogéneas de vegetación, mediante el empleo de los catálogos propuestos por el RAC-SPA y Medwet. De este modo, se han determinado 7 unidades ambientales en cada uno de los sectores analizados en el estudio (saladar de Urbanova y el Fondet de la Senieta), pudiendo comparar las diferencias entre ambas técnicas. Así, se seleccionaron 10 parcelas al azar y su vegetación fue muestreada según la metodología, basada en transectos, denominada «quadrat technique» y se identificaron las diferentes especies vegetales en el campo. Por otro lado, la medida de la cobertura vegetal total también fue tomada según la metodología de «Braun Blanquet». Se han empleado los programas informáticos CartaLinx® y ArcView® para el cartografiado de las unidades vegetales. Así, trabajo se pretende proporcionar herramientas técnicas y garantizar la gestión sostenible de los humedales y sus recursos naturales.