The influence of soil moisture on the spatial and temporal variability of soil electrical conductivity

Abstract Soil electrical conductivity (EC) is a soil quality indicator that is associated to attributes of interest for site-specific soil management such as soil moisture and texture. The present study performed spatial monitoring of soil moisture in two experimental fields of Brazilian soils for two consecutive years and modeled its influence on soil EC. Soil EC, moisture and clay content were evaluated by statistical, geostatistical and regression analyses. Semivariogram models, adjusted for soil moisture, had strong spatial dependence, but the relationship between soil moisture and soil EC was obtained only in one of the experimental fields, where soil moisture and clay content range was higher. In this same field, the correlation coefficients between soil moisture and clay content were above 90%. In the second field, the low soil moisture and clay content range explain the absence of a relationship between soil electrical conductivity and soil moisture

Rural environment stakeholders and policy making: willingness to pay to reduce road transportation pollution impact in the Western Pyrenees

In spite of the strategic national and regional development importance of transportation infrastructures, road transportation is one of the major sources of externalities worldwide. Using data collected from 900 residents living in 14 rural towns near the roads crossing the Spanish Pyrenees, we model citizens’ willingness-to-pay (WTP) to reduce noise and air pollution. We collect the data adopting a contingent valuation method (CVM) design and we analyze the data employing a Zero-Inflated Ordered Probit (ZIOP) model, which allows us to account for an excessive number of zero observations. Our results are in contrast with previous studies’ results with regard to environmental attitudes and socio-economic profiles of residents. Our findings indicate that the stakeholders living near major roads have higher incentives to offset environmental costs. Also, younger, better educated, and more environmentally-aware citizens are willing to pay more to reduce externalities, as they are influenced by their values and environmentally friendly sub-culture, possibly fostered during the past 30 years of green movement worldwide campaigning

Green logistics at Eroski: A case study

In today's highly competitive environment, green logistics issues are gaining interest. This paper analyses how logistics managers could lead the initiative in this area by incorporating environmental management principles into their daily decision-making process. A case study is given to show how they can turn practices into green while simultaneously meet the efficiency objectives. We have chosen one of the leader companies of the Spanish food distribution sector to check this hypothesis. The study covers the introduction of several changes into its fleet management and the implementation of a methodology to solve vehicle routing problems with environmental criteria minimisation.Green logistics Environment Freight transportation Vehicle routing

Potential applications of discrete-event simulation and fuzzy rule-based systems to structural reliability and availability

This chapter discusses and illustrates some potential applications of discrete-event simulation (DES) techniques in structural reliability and availability analysis, emphasizing the convenience of using probabilistic approaches in modern building and civil engineering practices. After reviewing existing literature on the topic, some advantages of probabilistic techniques over analytical ones are highlighted. Then, we introduce a general framework for performing structural reliability and availability analysis through DES. Our methodology proposes the use of statistical distributions and techniques &ndash; such as survival analysis &ndash; to model component-level reliability. Then, using failure- and repair-time distributions and information about the structural logical topology (which allows determination of the structural state from their components&rsquo; state), structural reliability, and availability information can be inferred. Two numerical examples illustrate some potential applications of the proposed methodology to achieving more reliable and structural designs. Finally, an alternative approach to model uncertainty at component level is also introduced as ongoing work. This new approach is based on the use of fuzzy rule-based systems and it allows the introduction of experts&rsquo; opinions and evaluations in our methodology.<br /

Combining simheuristics with Petri nets for solving the stochastic vehicle routing problem with correlated demands

This paper analyzes a stochastic version of the vehicle routing problem in which customers’ demands are not only stochastic but also correlated. In order to solve this stochastic and correlated optimization problem, a simheuristic approach is combined with an adaptive demand predictor. This predictor is based on the use of machine learning methods and Petri nets. The information on real demands, provided by the vehicles as they visit the nodes of the logistic network, allows for a real-time forecast of the demand, as well as for an updated estimate of the correlation between them. A constrained prediction is provided by our hybrid algorithm, which is able to forecast an increase of 50% in the mean value of the demands of all nodes. With a very limited amount of information and reduced computational requirements, our algorithm provides a forecast with a high degree of reliability and a balanced capacity to reject false positives as well as false negatives. To illustrate its effectiveness, the methodology is applied to a wide range of benchmarks. The results show the benefits of applying this methodology in a context of correlated variation of the demands

Using safety stocks and simulation to solve the vehicle routing problem with stochastic demands

After introducing the Vehicle Routing Problem with Stochastic Demands (VRPSD) and some related work, this paper proposes a flexible solution methodology. The logic behind this methodology is to transform the issue of solving a given VRPSD instance into an issue of solving a small set of Capacitated Vehicle Routing Problem (CVRP) instances. Thus, our approach takes advantage of the fact that extremely efficient metaheuristics for the CVRP already exists. The CVRP instances are obtained from the original VRPSD instance by assigning different values to the level of safety stocks that routed vehicles must employ to deal with unexpected demands. The methodology also makes use of Monte Carlo simulation (MCS) to obtain estimates of the reliability of each aprioristic solution – that is, the probability that no vehicle runs out of load before completing its delivering route – as well as for the expected costs associated with corrective routing actions (recourse actions) after a vehicle runs out of load before completing its route. This way, estimates for expected total costs of different routing alternatives are obtained. Finally, an extensive numerical experiment is included in the paper with the purpose of analyzing the efficiency of the described methodology under different uncertainty scenariosFil: Juan, A.. Open University of Catalonia; EspañaFil: Faulin, J.. Public University of Navarre; EspañaFil: Grasman, S.. Missouri University of Science & Technology; Estados UnidosFil: Riera, D.. Open University of Catalonia; EspañaFil: Marull, J.. Open University of Catalonia; EspañaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentin