Abstract
Usually, Machine Learning systems are seen as something fully automatic. Recently, however, interactive systems in which human experts actively contribute towards the learning process have shown improved performance when compared to fully automated ones. This may be so in scenarios of Big Data, scenarios in which the input is a data stream, or when there is concept drift. In this paper, we present a system for supporting auditors in the task of financial fraud detection. The system is interactive in the sense that the auditors can provide feedback regarding the instances of the data they use, or even suggest new variables. This feedback is incorporated into newly trained Machine Learning models which improve over time.

Abstract
Breaks in water consumption records can represent apparent losses which are generally associated with the volumes of water that are consumed but not billed. The detection of these losses at the appropriate time can have a significant economic impact on the water company's revenues. However, the real datasets available to test and evaluate the current methods on the detection of breaks are not always large enough or do not present abnormal water consumption patterns. This study proposes an approach to generate synthetic data of water consumption with structural breaks which follows the statistical proprieties of real datasets from a hotel and a hospital. The parameters of the best-fit probability distributions (gamma, Weibull, log-Normal, log-logistic, and exponential) to real water consumption data are used to generate the new datasets. Two decreasing breaks on the mean were inserted in each new dataset associated with one selected probability distribution for each study case with a time horizon of 914 days. Three different change point detection methods provided by the R packages strucchange and changepoint were evaluated making use of these new datasets. Based on Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) performance indices, a higher performance has been observed for the breakpoint method provided by the package strucchange.

Abstract
This paper deals with multi-objective location-routing problems (MO-LRPs) and follows a sectorizationapproach, which means customers are divided into different sectors, and a distribution centre is opened for eachsector. The literature has considered objectives such as minimizing the number of opened distribution centres,the variances of compactness, distances and demands in sectors. However, the achievement of these objectivescannot guarantee the geographical separation of sectors. In this sense, and as the geographical separation ofsectors can have significant practical relevance, we propose a new objective function and solve a benchmarkof problems with the non-dominated sorting genetic algorithm (NSGA-II), which finds multiple non-dominatedsolutions. A comparison of the results shows the effectiveness of the introduced objective function, since, in thenon-dominated solutions obtained, the sectors are more geographically separated when the values of the objectivefunction improve.

Abstract
This paper compares the non-dominated sorting genetic algorithm (NSGA-II) and NSGA-III to solve multiobjective sectorization problems (MO-SPs). We focus on the effects of the parameters of the algorithms on their performance and we use statistical experimental design to find more effective parameters. For this purpose, the analysis of variance (ANOVA), Taguchi design and response surface method (RSM) are used. The criterion of the comparison is the number of obtained nondominated solutions by the algorithms. The aim of the problem is to divide a region that contains distribution centres (DCs) and customers into smaller and balanced regions in terms of demands and distances, for which we generate benchmarks. The results show that the performance of algorithms improves with appropriate parameter definition. With the parameters defined based on the experiments, NSGA-III outperforms NSGA-II. © 2020 IEEE.

Abstract
The beginning of the 21st century brought a new Industrial Revolution - the 4th Industrial Revolution which assumes that each physical object is equipped with an integrated technology that allows its connection with other objects. Therefore, Cyber-Physical Systems (CPSs) are becoming essential elements to be implemented in the companies' workspace in order to improve their production efficiency and flexibility by bringing digitalisation to the production processes. The implementation of these CPSs creates several changes for companies' operations that are expected to have a deep impact in human workers. A lack of studies on the social impact of the use of CPSs has been identified and, through a comprehensive literature review, this work aims at contributing for this discussion by defining a questionnaire about the topic. In the future, this questionnaire is intended to be sent to companies to collect data from their C-level managers about the use of CPSs applied to manufacturing.

Abstract
Tactical Sales and Operations Planning (S&OP) has emerged as an extension of the aggregate production planning, integrating mid-term decisions from procurement, production, distribution, and sales in a single plan. Despite the growing interest in the subject, past synthesizing research has focused more on the qualitative and procedural aspects of the topic rather than on modeling approaches to the problem. This paper conducts a review of the existing decision-making, i.e., optimization, models supporting S&OP. A holistic framework comprising the decisions involved in this planning activity is presented. The reviewed literature is arranged within the framework and grouped around different streams of literature which have been extending the aggregate production planning. Afterwards, the papers are classified according to the modeling approaches employed by past researchers. Finally, based on the characterization of the level of integration of different business functions provided by existing models, the review demonstrates that there are no synthesizing models characterizing the overall S&OP problem and that, even in the more comprehensive approaches, there is potential to include additional decisions that would be the basis for more sophisticated and proactive S&OP programs. We do expect this paper contributes to set the ground for more oriented and structured research in the field.