Abstract
Sectorization refers to partitioning a large territory, network, or area into smaller parts or sectors considering one or more objectives. Sectorization problems appear in diverse realities and applications. For instance, political districting, waste collection, maintenance operations, forest planning, health or school districting are only some of the application fields. Commonly, sectorization problems respect a set of features necessary to be preserved to evaluate the solutions. These features change for different sectorization applications. Thus, it is important to conceive the needs and the preferences of the decision-makers about the solutions. In the current paper, we solve sectorization problems using the Genetic Algorithm by considering three objectives: equilibrium, compactness, and contiguity. These objectives are collected within a single composite objective function to evaluate the solutions over generations. Moreover, the Analytical Hierarchy Process, a powerful method to perceive the relative importance of several objectives regarding decision makers' preferences, is used to construct the weights. We observe the changes in the solutions by considering different sectorization problems that prioritize various objectives. The results show that the solutions' progress changed accurately to the given importance of each objective over generations.

Abstract
The fast-moving consumer goods sector relies on economies of scale. However, its assortments have been overextended as a means of market share appropriation and top-line growth. This paper studies the se-lection of the optimal set of products for fast-moving consumer goods producers to offer, as there is no previous model for product line selection that satisfies the requirements of the sector. Our mixed -integer programming model combines a multi-category attraction model with a capacitated lot-sizing problem, shared setups and safety stock. The multi-category attraction model predicts how the demand for each product responds to changes within the assortment. The capacitated lot-sizing problem allows us to account for the indirect production costs associated with different assortments. As seasonality is prevalent in consumer goods sales, the production plan optimally weights the trade-off between stocking finished goods from a long run with performing shorter runs with additional setups. Finally, the safety stock extension addresses the effect of the demand uncertainty associated with each assortment. With the computational experiments, we assess the value of our approach using data based on a real case. Our findings suggest that the benefits of a tailored approach are at their highest in scenarios typical fast-moving consumer goods industry: when capacity is tight, demand exhibits seasonal patterns and high service levels are required. This also occurs when the firm has a strong competitive position and consumer price-sensitivity is low. By testing the approach in two real-world instances, we show that this decision should not be made based on the current myopic industry practices. Lastly, our approach obtains profits of up to 9.4% higher than the current state-of-the-art models for product line selection.

Abstract
Economic efficiency assessments based on Data Envelopment Analysis are scarce compared to technical efficiency studies, even in for-profit firms. Some aspects justify this scarcity, such as the difficulty to estimate accurate prices, given their variability over time. In many situations, external benchmarking is hindered due to organizations' unique nature and the barriers to sharing information considered critical to competitiveness. The use of internal benchmarking can overcome some of these difficulties. This study conducted an internal benchmarking analysis of a broiler production system, focusing on cost efficiency. We conducted longitudinal case-based research over six years (2014-2019). The concepts of throughput accounting of the Theory of Constraints were applied to structure the DEA model (inputs, prices, and output). The Critical Incident Technique was used to explore the effects of interventions on the production system's cost efficiency. The results show that the broiler production system could reduce 32% of the total cost per unit of production if the balance of inputs suggested by the DEA evaluation was used. This work contributes to the literature by showing the potential of internal benchmarking to explore the evolution of cost efficiency over time. From a practical perspective, this study is important for managers by showing how to measure the impact of management actions on performance, providing valuable information to guide continuous improvement.

Abstract
Nowadays there are several countries running independent kidney exchange programmes (KEPs). These programmes allow a patient with kidney failure, having a willing healthy but incompatible donor, to receive a transplant from a similar pair where the donor is compatible with him. Since in general larger patient-donor pools allow for more patients to be matched, this prompts independent programmes (agents) to merge their pools and collaborate in order to increase the overall number of transplants. Such collaboration does however raise a problem: how to assign transplants to agents so that there is a balance between the contribution each agent brings to the merged pool and the benefit it gets from the collaboration. In this paper we propose a new Integer Programming model for multi-agent kidney exchange programmes (mKEPs). It considers the possible existence of multiple optimal solutions in each matching period of a KEP and, in consecutive matching periods, selects the optimal solution among the set of alternative ones in such a way that in the long-term the benefit each agent gets from participating in the mKEP is balanced accordingly to a given criterion. This is done by use of a memory mechanism. Extensive computational tests show the benefit of mKEPs, when compared to independent KEPs, in terms of potential increase in the number of transplants. Furthermore, they show that, under different policies, the number of additional transplants each agent receives can vary significantly. More importantly, results show that the proposed methodology consistently obtains more stable results than methodologies that do not use memory.

Abstract
Temporary transnational relocation is a growing type of migration. However, travel behavior adaptation of highly skilled temporary residents and its urban impacts have largely been ignored. This study extends the knowledge of mobility biographies, mobility cultures, and mobility of millennials by examining how temporary residents adapt their intra-urban travel behavior in response to a transnational relocation. The data used here comes from semi-structured interviews with students and researchers of nine different nationalities, aged between 19 and 31 years, temporarily living in Portugal (Lisbon or Porto). We found supporting evidence for the occurrence of residential self-selection, although prior information on study/workplace combined with low knowledge on neighborhood-level make it somewhat specific. Given their shortterm perspective, temporary residents are more prone to rely on public transport and non-motorized modes, having a low likelihood of purchasing vehicles. Thus, measures aimed at improving and facilitating the use of active modes can have an immediate effect on this group's travel behavior and contribute to reaching critical mass for these sustainable alternatives. Temporary residents are also a potentially interesting market segment for public transportation operators for increases in revenues, as they tend to display a relatively higher travel intensity and a wider diversity of activities and destinations. Finally, technology usage was found to reduce the stress-related to traveling to unfamiliar places by increasing the perceived spatial orientation, having the downside of generating a feeling of confidence that decreases the internalization of information. Providing timely and persuasive information at the very beginning of temporary residents' stay can help induce their travel behavior decisions.

Abstract
Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.