Abstract
The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure. © 2019, Springer Nature Switzerland AG.

Abstract
Promotional tools such as cross-market discounts have been increasingly used as a means to increase customer satisfaction and sales. This paper aims to assess whether the implementation of a cross-market discount campaign by a retailing company encouraged customers to increase their purchases level. It contributes to the literature by using neural networks to detect novelties in a real context involving cross-market discounts. Besides the computation of point predictions, the methodology proposed involves the estimation of neural networks prediction intervals. Sales predictions are compared with the observed values in order to detect significant changes in customers' spending. The use of neural networks is validated through the comparison with the forecasting estimates of support vector regression, regression trees, and linear regression. The results reveal that the promotional campaign under analysis did not significantly impact the sales of the rewarded customers.

Abstract
Build to Order or Make to Order is a common approach for highly configured products such as special vehicles (vehicles that are adapted and altered to suit a specific purpose). Examples of such vehicles are special ambulances as well as vehicles adapted for the support and transport of passengers with less mobility. In this type of business, operations are scheduled in response to a confirmed order received from a final customer. Thus, the variability and the uncertainty characterizing what is project based, generate a complexity that requires specifically tailored managerial approaches to handle all the involved processes - from design and engineering to production and delivery. Hence, in this accentuated complexity, it is extremely important to guarantee that both the material and information flows are efficient and effective. The present study, framed in a program of operational improvement in a manufacturer of special vehicles, aims to address some concrete improvement opportunities related to the significant number of raw materials stockouts and to the high number of changes made by the client after production has started. In fact, during the manufacturing and assembly process, there are constant changes that delay and difficult planning and consequently decreases the overall efficiency and effectiveness. Strategies to address all these matters are to be identified and applied. © IEOM Society International.

Abstract
This paper proposes an innovative composite indicator to evaluate Corporate Social Responsibility (CSR). The methodology proposed involves two stages. The first stage specifies an optimization model, based on Directional Distance Functions, to obtain a relative measure of CSR at the firm level that can guide performance improvements. This model allows distinguishing the firms with best practices from those with potential for improvement and can suggest targets for future achievements. In the second stage, a goal programming model is proposed to identify a common set of weights for the key performance indicators, enabling the evaluation of all firms on common grounds. These weights are used to construct an industry ranking, which is based on the distance of firms to a common frontier of technology that respects the trade-offs determined for the industry. An illustrative application of the method proposed is presented at the end of the paper. The indicators considered in the evaluation were selected according to international standards and guidelines applicable to mining firms. All dimensions of the Triple Bottom Line (economic, environmental and social) were taken into account. The results and their managerial implications are discussed with the objective of promoting the awareness of CSR levels in the mining activity, supporting the sustainable development of industrial activities.

Abstract
Electric power systems are facing significant challenges regarding their organization and structure. Energy infrastructures are crucial to ensure a transition to low-carbon societies, contributing to sustainable development. This paper uses Data Envelopment Analysis to compare the performance of the power systems in 16 European countries using data available to the public. Three perspectives were considered, focusing on technical aspects affecting quality of service, network costs and environmental impact. It is proposed a new formulation of the DEA model that estimates a composite indicator (CI) aggregating individual indicators which should be minimized. The benchmarking results can give insights to electric operators, regulators and decision-makers on the strengths and weakness of national power systems and disclose the potential for performance improvements. Based on the outcomes from the CI model, Austria, Croatia, Denmark, Germany, Greece, Ireland, Italy and Netherlands are identified as the benchmarks for the power systems in the Europe. The discussion of the results is intended to raise public awareness on the performance of the European power systems and contribute to the definition of public policies for the promotion of continuous improvement. © 2019, Springer Nature Switzerland AG.

Abstract
The search for logistics best-practices in international trade has led to the appearance of the Berth Allocation Problem. If the vessels have release dates, the problem is proved to be NP-hard and the performance of exact algorithms is not satisfactory, leading to the use of metaheuristics. This paper develops a Hybrid Evolutionary Algorithm for the discrete and dynamic Berth Allocation Problem. A challenge of using Genetic Algorithms is the identification of the best approach to model a specific problem. This paper proposes the use of frontier techniques (Data Envelopment Analysis and Free Disposal Hull models) to compare the performances of alternative specifications of the parameters for the algorithm proposed and to identify efficient solutions.