Abstract
The transport sector of the European Union is the only sector of the economy that has been increasing its emissions since 2014. To reduce the use of fossil fuels and achieve the greenhouse gas emissions mitigation target, many countries are focusing on the deployment of electric vehicles. This paper aims at analysing recent literature on the deployment of electric vehicles (EV) and typifying objectives, methods and indicators generally exploited, to better understand the state of the art on this topic. The Web of Science database was used and the results showed that the interest in the topic of electric vehicles has been increasing exponentially since 2010. The main significant indicators and the assessment methodologies were analysed. The indicators identified were aggregated in four main clusters: environmental, economic, social and technical indicators. Although the factors that contribute to EV deployment can vary depending on the regions specific characteristics, most of the research studies pointed out that the main contributors are the high density of recharging points, the existence of government monetary incentives and the lower operational cost of EV.

Abstract
Hospitality is one of the most important sectors in the tourist activity in Portugal. Knowing the characteristics and features of the hotel units operating in Portugal is important for all those who make decisions about investment in this sector. Knowing the reality of brand affiliation of Portuguese hotel establishments is also a matter of great interest for hoteliers in supporting strategic decision-making. Performing the analysis of the universe of hotel establishments in Portugal, their characterization is made concerning their features, facilities and equipment. In addition, the reality of brand affiliation of Portuguese hotel establishments is analyzed and discussed through a logistic regression model. The results allow us to conclude that just over a third of hotel establishments in Portugal are brand affiliated and they are mostly located in the more touristic regions. The results also show that brand affiliated hotel establishments have, on average, a greater number of stars, greater capacity and a greater number of facilities than non-brand affiliated hotel establishments.

Abstract
This study aims to find and develop an appropriate optimization approach to reduce the time and labor employed throughout a given chemical process and could be decisive for quality management. In this context, this work presents a comparative study of two optimization approaches using real experimental data from the chemical engineering area, reported in a previous study [4]. The first approach is based on the traditional response surface method and the second approach combines the response surface method with genetic algorithm and data mining. The main objective is to optimize the surface function based on three variables using hybrid genetic algorithms combined with cluster analysis to reduce the number of experiments and to find the closest value to the optimum within the established restrictions. The proposed strategy has proven to be promising since the optimal value was achieved without going through derivability unlike conventional methods, and fewer experiments were required to find the optimal solution in comparison to the previous work using the traditional response surface method.

Abstract
Purpose Portugal has been experiencing a continuous growth in tourism activity, with hospitality industry as one of the main tourism sectors. Therefore, the assessment of hotel companies' performance is very important to assist decision processes. The purpose of this paper is to assess the financial performance (FP) of 570 hotel companies operating hotel units in Portugal in 2017. To explore the question of brand affiliation, a comparison was made between hotel companies with similar stars rating and market orientation. In addition, this paper intends to fill a gap in literature studying the Portuguese reality on the subject of brand affiliation. Design/methodology/approach The present study uses a methodology based on data envelopment analysis (DEA) to assess the overall performance for each company, which further decomposed into the within-group performance and the technological gap. The performance of the hotel company is assessed through the aggregation of multiple financial indicators using the composite indicator (CI) derived from the DEA model. A bivariate analysis based on the Tobit regression to test the robustness of brand effect on FP of hotel companies (HC) was also included. Findings The empirical results show that branded companies, on average, have significantly better overall FP than non-branded companies. On the one hand, the brand effect tends to improve the within-group FP of HCs and the brand presents a statistically significant positive effect on the FP. On the other hand, the best practices are observed in both branded and non-branded companies. Practical implications The results of this study illustrate that, globally, the better FP of the branded companies is because of their individual relative companies' performance and a better model of operation given by the brand effect. Brand affiliation will generally allow for a better FP and essentially a better profitability for invested equity, a higher return on sales and a higher value added per employee. Originality/value The study provides important theoretical and practical contributions that can assist the strategic decision of the HCs in choosing to operate independently or to adopt brand affiliation. Also, it is innovative because the FP of branded and non-branded HCs is measured not using a set of individual financial ratios but through a single CI that aggregates those financial ratios, using a DEA model.

Abstract
One of the main decisions that a car rental company has to make regards the definition of the fleet size and mix, i.e., the capacity to meet demand. This demand is highly unpredictable and price-sensitive; thus, the definition of the prices charged influences capacity decisions. Moreover, capacity decisions are also linked to other company strategies to meet demand, such as offering upgrades or transferring empty cars between stations. Typically, these problems are tackled focusing on the maximization of profits, disregarding the environmental impacts associated with these decisions. There is a growing need for models and analytical tools that can support decisions considering the trade-off between profit and environmental impact in mobility. Therefore, this work incorporates environmental concerns into the capacity-pricing problem for car rental, proposing a bi-objective model to tackle the trade-off between profit and environmental impact. The Life Cycle Assessment method is applied not only to vehicles but also to fuel to define environmental parameters accurately. Four types of vehicles are considered: internal combustion engine vehicles, hybrids, hybrids plug-in, and electric vehicles. Solving multi-objective models is a computationally challenging problem, which requires efficient and applicable methods. These methods can support policy and business decisions in a real-world context, running different scenarios and evaluating solutions under varying conditions. Due to its efficiency in solving bi-objective models, an Epsilon-constraint method is developed and applied in diverse situations to retrieve managerial insights. The results obtained enable quantifying the feasible trade-offs, overall showing that, on average, with a decrease of 14.44% in financial results, it is possible to obtain a decrease of 63.41% in environmental impact. Additional insights are also retrieved related to the fleet, fuel, prices and demand.

Abstract