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The aerospace industry is one of the main drivers of the economies of Portugal and Andalusia (Spain), where it generates more than 30,000 jobs and sales of over 4000 million euros. This study analyses the needs and capacities regarding innovation of companies and R&D centres in the aerospace sector of both regions. 277 entities of Portugal and Andalusia provided valid responses to a questionnaire concerning 107 innovation resources. Regional and cross-border collaboration opportunities have been detected utilizing the survey data. The results show that the productivity and innovation capacity of companies in the sector could increase in both territories through better use of the existing resources. A significant set of identified needs can be addressed by capacities existing in the neighbouring region, which supports the urgency to enhance cross-border collaboration. Additionally, a basis for the prioritization of future investments is provided through the identification of several fields with weak innovation capacity. [ABSTRACT FROM AUTHOR]

• Development of a comprehensive five-step infrastructure asset management framework. • Definition of short and long-term investment needs in water and wastewater networks. • Integration of innovative and relevant operational and convenience factors. • Utilization of novel key performance indicators. • Inclusion of adjacency and group replacement considerations. Water supply and sewer networks are critical infrastructures that provide a basic service to society. However, these systems constantly age and degrade over time. In addition, since network infrastructures are so extensive in length, they require a significant investment in maintenance tasks. Hence, within the context of infrastructure asset management (IAM), accurately defining the most efficient investment planning possible is essential to ensure their long-term sustainability. This paper presents an original five-step comprehensive framework to successfully implement an infrastructure asset management strategy and plan long-term investments. Moreover, this methodology integrates innovative and relevant operational and convenience factors that, while provide the problem both with realism and practicality, have not been addressed so far. To illustrate the usefulness and applicability of this methodology, the case study of a large water company in Spain is presented. [ABSTRACT FROM AUTHOR]

This paper presents a guided and deep introduction to viral systems (VS), a novel bio-inspired methodology based on a natural biological process taking part when the organism has to give a response to an external infection. VS has proven to be very efficient when dealing with problems of high complexity. The paper discusses on the foundations of VS, presents the main pseudocodes that need to be implemented and illustrates the methodology application. A comparison between VS and other metaheuristics, as well between different VS approaches is presented. Finally, trends and new research opportunities are presented for this bio-inspired methodology. [ABSTRACT FROM AUTHOR]

The service industry has a key importance in the entrepreneurial activity. From that reason steams the significance of analysing the management of the companies of this sector. The use of the revenue management technique is because of the simultaneousness in the production and consumption of the service offered by these companies, along with the incapability of stocking these perishable products. In this paper, the application of this technique to parking lots management is analysed, accepting the existence of individual and subscribed drivers, two segments that are necessary for a parking to work because of their different uncertainty.Journal of Revenue and Pricing Management (2009) 8, 343–356. doi:10.1057/rpm.2008.35; published online 29 August 2008 [ABSTRACT FROM AUTHOR]

• Decision support system to help water utilities design practical work programs. • Two-stage grouping algorithm to aggregate water pipes into replacement programs. • Work programs optimization through a multi-objective genetic algorithm. • Integration of external criteria and the managers knowledge and experience. • Sensitivity analysis of the company's strategic policy on the work programs. Asset management in hydraulic infrastructures aims for the long-term sustainability of water distribution and wastewater networks. Strategic maintenance planning has thus been deeply analyzed in the literature for individual water and sewer pipes. However, water utilities do not plan and perform replacement activities on individual elements, but rather on coherent aggregations of neighboring pipes. We have developed a decision support system (DSS) to help water utilities design intervention programs for hydraulic infrastructures. It integrates a two-stage algorithm that groups water supply and sewer pipes into practical and efficient replacement works, based upon their proximity and their priority of renewal. A multi-objective genetic algorithm optimizes the work programs configurations while integrating the water company's strategic policy into an innovative multi-objective function. We have applied our methodology to a large water company in Spain and illustrated this application with a sensitivity analysis to determine how the company's strategic criteria influences the resulting work configurations. [ABSTRACT FROM AUTHOR]

Intermodal freight transport consists of using different modes of transport without changing the load unit. This results in a significant reduction in the time that goods spend at intermodal terminals, where transshipment takes place. Drayage refers to the transport of freight on trucks among intermodal terminals, depots, customers and suppliers. In spite of the fact that drayage only represents between 5 and 10 percent of total distance, it may amount up to more than 30 percent of the total costs. The aim of this work is to study drayage operations. First, an extensive literature review is undertaken. Since the intermodal transport chain can become more efficient by means of a proper organisation of the drayage movements, the optimization of the daily drayage problem has been identified as one of the main ways of reducing the drayage cost and improving intermodal operations. On this problem, the lack of a common benchmark has hindered reaching further conclusions from all the research carried out. Therefore, this paper proposes a common framework and presents a generalized formulation of the problem, which allows modeling most drayage policies, with the limitation of only considering one-container problems. Results show that flexible tasks in the repositioning of empty containers as well as soft time windows can reduce the operating costs and facilitate the management of drayage companies. This work may help consider adequate policies regarding drayage operations in intermodal terminals. [ABSTRACT FROM AUTHOR]

In this study, an evolutionary fuzzy system is proposed to predict unexpected pipe failures in water supply networks. The system seeks to underpin the decisions of management companies regarding the maintenance and replacement plans of pipes. On the one hand, fuzzy logic provides high degrees of interpretability over other black box models, which is requested in engineering application where decisions have social consequences. On the other hand, the genetic algorithm helps to optimize the parameters that govern the model, specifically, for two purposes: (i) the selection of variables; and (ii) the optimization of membership functions. Data from a real water supply network are used to evaluate the accuracy of the developed system. Several graphs that depict the ranking of pipes according to their risk of failure against the network length to be replaced support the choice of the most successful model. In fact, results demonstrate that the annual replacement of 6.75% of the network length makes it possible to prevent 41.14% of unexpected pipe failures. • Evolutionary fuzzy system to predict pipe failures in water supply networks. • Fuzzy logic provides interpretable results to real engineering problems. • Ranking pipes according to their risk of failure. [ABSTRACT FROM AUTHOR]

The assessment of indoor thermal comfort in schools has become an essential object of study; however, applying existing thermal comfort criteria would assume children and adults have a similar range of thermal comfort, without considering discrepancies regarding their level of activity or their behavioural adaptation. Therefore, the objective of the present study was to investigate the thermal comfort in a school building based on an adaptive thermal comfort field study in Seville, in the southwest of Spain, during a summer season. In this study, 2 free-running and 1 air-conditioned classroom were analysed; 67 students aged 10–11 years participated and 2010 thermal questionnaires were collected. A discrepancy was observed between the predicted mean vote and the thermal sensation vote, showing the former is not a good predictor of thermal perception. Thermoneutrality was not always the desired sensation for children; a preference for coolness was detected. A neutral temperature was observed at an average indoor temperature of 24–27 °C and a widening in the thermal comfort range was detected compared with international standards. Regarding adaptive strategies, they showed a preference towards opening windows and doors over using fans or changing clothes. The results suggest that the application of the current models for adults would not be suitable for estimating the thermal comfort of children, and these data could be used to promote natural strategies for assessing thermal comfort over conditioning systems in schools, with the aim of both space ventilation and energy efficiency. • Thermal perception was analysed for children aged between 10 and 11 years. • A preference for coolness situations was observed during the summer season. • The proposed comfort temperature is higher compared to other European countries. • A widening in the thermal comfort range of the students was observed. • The adaptive behaviour is mainly represented by using fans or opening windows. [ABSTRACT FROM AUTHOR]

The water supply networks of many countries are experiencing a drastic increase in the number of pipe failures. To reverse this tendency, it is essential to optimise the replacement plans of pipes. For this reason, companies demand pioneering techniques to predict which pipes are more prone to fail. In this study, an Artificial Neural Network (ANN) is designed to classify pipes according to their predisposition to fail based on physical and operational input variables. In addition, the usefulness and effectiveness of two sampling methods, under-sampling and over-sampling, are analysed. The implementation of the model is done using the open-source software Weka, which is specialised in machine-learning algorithms. The system is tested with a database from a real water network in Spain, obtaining high-accurate results. It is verified that the balance of the training set is imperative to increase the predictions' accurateness. Furthermore, under-sampling prioritises true positive rates, whereas over-sampling makes the system learn to predict failures and non-failures with the same precision. [ABSTRACT FROM AUTHOR]

The pharmaceutical supply chain has features that distinguish it from other supply chains. Medicine is considered a strategic commodity, and the smallest disruption in its supply chain may cause severe crises. This is why the distribution of pharmaceutical products needs to combine the minimization of costs with strong compliance with service standards while taking into account risks due to uncertainty. In this study, we present a new multi-objective multi-echelon multi-product multi-period pharmaceutical supply chain network (PSCN) along with the production–distribution–purchasing–ordering–inventory holding-allocation-routing problem under uncertainty. We formulate the problem as a Mixed-Integer Non-Linear Programming model and develop a novel robust fuzzy programming method to cope with uncertainty parameters. To find optimal solutions, several multi-objective metaheuristic algorithms, namely, MOSEO, MOSAM MOKA, and MOFFA considering different criteria and multi-objective assessment metrics are suggested. Since there are no benchmarks existing in the literature, 10 numerical instances in large and small sizes are generated and also the trapezoidal fuzzy numbers of the uncertain parameters were randomly generated based on a uniform distribution. The required parameters were set and also the simulated data were examined in an exact method and by metaheuristic algorithms. The results confirm the efficiency of the MOFFA algorithm to detect a near-optimal solution within a logical CPU time. The solution methods are complemented with several sensitivity analyses on the input parameters of the proposed model. • Integrating production, distribution and purchasing in pharmaceutical supply chain. • Considering uncertainty for ordering, purchasing, and delivery costs. • Developing a novel robust fuzzy approach to manage uncertainty. • Several multi-objective meta-heuristic algorithms are applied and compared. • Providing several sensitivity analyses on the main parameters of PSCN. [ABSTRACT FROM AUTHOR]

• Determining the urban critical infrastructures in the event of an earthquake. • Designing Location-Routing-Inventory decisions in humanitarian relief chain. • Simulation of interaction of urban infrastructure in earthquake conditions. • Using a cooperative game to examine the manner of cooperation of disaster relief supply chain players. • Application of the model to a real-world disaster relief logistic case study. This paper describes a new simulation based mathematical model for locating distribution centres, vehicle routing, and inventory problems under earthquake conditions. In this paper, the proposed network includes affected areas, suppliers, distribution centres, and hospitals. Additionally, the basic infrastructures of the city, which are very fragile at the time of the earthquake, are identified. Then, the demand of each relief commodity is calculated depending on the different earthquake scenarios using simulation. The estimated demand is incorporated into the mathematical model as an uncertain parameter. The proposed methodology is designed as a two-stage model so that in the first stage the location and inventory of distribution centres are addressed. Thereafter, in the second stage the routing decisions are taken for the distribution of relief commodities from the distribution centres and suppliers to the affected areas. Due to the NP-hardness of the second stage model, this model is solved using multi-objective stochastic fractal search. This algorithm is one of the population-based and stochastic optimization techniques and inspired by the natural phenomenon of fractal growth. It should be noted that in the second stage, a cooperative game theory of coalition type is considered, which resulted in synergies that minimize the relief golden time. The kind of cooperation is the use of potential of co-operators' vehicle. In this stage, the possibility for the players to cooperate by sharing people and commodities transportation requests is considered. Also, to validate the model, a real case study is provided for a possible earthquake in Tehran. Finally, the comparison between the simulation results and the values obtained from the real system evaluates the performance of the implemented model. Considering normality and a 95% confidence interval, it can be concluded that the proposed model provides a precise representation of the real system's performance. [ABSTRACT FROM AUTHOR]

Sewer networks are mainly composed of pipelines which are in charge of transporting sewage and rainwater to wastewater treatment plants. A failure in a sewer pipe has many negative consequences, such as accidents, flooding, pollution or extra costs. Machine learning arises as a very powerful tool to predict these incidents when the amount of available data is large enough. In this study, a real-coded genetic algorithm is implemented to estimate the optimal weights of a logistic regression model whose objective is to forecast pipe failures in wastewater networks. The goal is to create an autonomous and independent predictive system able to support the decisions about pipe replacement plans of companies. From the data processing to the validation of the model, all stages for the implementation of the machine-learning system are explored and carefully explained. Moreover, the methodology is applied to a real sewer network of a Spanish city to check its performance. Results demonstrate that by annually replacing 4% of pipe segments, those whose estimated failure probability is higher than 0.75, almost 30% of unexpected pipe failures are prevented. Furthermore, the analysis of the estimated weights of the logistic regression model reveals some weaknesses of the network as well as the influence of the features in the pipe failures. For instance, the predisposition of vitrified clay pipes to fail and of that pipes with smaller diameters. [ABSTRACT FROM AUTHOR]