Abstract
Despite the wide spread of gamification as a means of influencing behavior, we do not yet fully understand its effectiveness in promoting sustainable behaviors among young people. This question becomes all the more relevant when it comes to influencing their mobility habits, considering the negative impact of motorized transportation on urban livability. As a consequence, the promotion of soft mobility has been on the policy agenda in many countries. In this study, we explore the potential of gamification and the use of rewards as a way to incentivize young citizens to adopt soft mobility over motorized transports. Our goal was to understand how a gamified app with a built-in reward system can influence the promotion of soft mobility among young people in cities, focusing particularly on walking and cycling. To achieve this, we adopted a quantitative research methodology, carrying out a structured survey in three schools enrolled in the Sharing Lisboa project. We used statistical tools based on partial least squares structural equation modeling (PLS-SEM) to analyze the data. We found that an app influences the users' perception of its usefulness, leading to a positive attitude towards its use. Contrary to what was initially assumed, the reward system only influences the perceived usefulness, suggesting that it is important to convince potential users to try the system but that it does not influence their attitude. Moreover, the instrumental attitude, which is related to the benefits and functions of an app, together with the subjective (injunctive/descriptive) norms and perceived behavioral control, have a positive influence on walking/cycling travel intention. Therefore, social pressure, especially from family and friends, is important for building the intention to travel by bicycle/on foot.

Abstract
Food waste reduction represents a potential opportunity to enhance environmental sustainability. This is especially important in fresh products such as fresh seafood, where waste levels are substantially higher than those of other food products. In this particular case, reducing waste is also vital to meet demand while conserving fisheries. This paper aims to promote more sustainable supply chains by proposing daily fresh fish demand forecasting models that can be used by grocery retailers to align supply and demand, and hence prevent the production of food waste. To accomplish this goal, we explored the potential of different machine learning models, namely Long Short-Term Memory networks, Feedforward neural networks, Support Vector Regression, and Random Forests, as well as a Holt-Winters statistical model. Demand censorship was considered to capture real demand. To validate the proposed methodology, we estimated the demand for fresh fish in a representative store of a large European retailing company used as a case study. The results revealed that the machine learning models provided accurate forecasts in comparison to the baseline models and the statistical model, with the Long Short-Term Memory networks model yielding, in general, the best results in terms of root mean squared error (27.82), mean absolute error (20.63) and mean positive error (17.86). Thus, the implementation of these types of models can thus have a positive impact on the sustainability of fresh fish species and customer satisfaction.

Abstract
Reducing office buildings' energy consumption can contribute significantly towards carbon reduction commitments since it represents similar to 40% of total energy consumption. Major components of this are lighting, electrical equipment, heating, and central cooling systems. Solid evidence demonstrates that individual occupants' behaviors impact these energy consumption components. In this work, we propose the methodology of using virtual choreographies to identify and prioritize behavior-change interventions for office users based on the potential impact of specific behaviors on energy consumption. We studied the energy-related office behaviors of individuals by combining three sources of data: direct observations, electricity meters, and computer logs. Data show that there are behaviors with significant consumption impact but with little potential for behavioral change, while other behaviors have substantial potential for lowering energy consumption via behavioral change.

Abstract
The emergence of Industry 4.0 is making production systems more flexible and also more dynamic. In these settings, schedules often need to be adapted in real-time by dispatching rules. Although substantial progress was made until the '90s, the performance of these rules is still rather limited. The machine learning literature is developing a variety of methods to improve them. However, the resulting rules are difficult to interpret and do not generalise well for a wide range of settings. This paper is the first major attempt at combining machine learning with domain problem reasoning for scheduling. The idea consists of using the insights obtained with the latter to guide the empirical search of the former. We hypothesise that this guided empirical learning process should result in effective and interpretable dispatching rules that generalise well to different scenarios. We test our approach in the classical dynamic job shop scheduling problem minimising tardiness, one of the most well-studied scheduling problems. The simulation experiments include a wide spectrum of scenarios for the first time, from highly loose to tight due dates and from low utilisation conditions to severely congested shops. Nonetheless, results show that our approach can find new state-of-the-art rules, which significantly outperform the existing literature in the vast majority of settings. Overall, the average improvement over the best combination of benchmark rules is 19%. Moreover, the rules are compact, interpretable, and generalise well to extreme, unseen scenarios. Therefore, we believe that this methodology could be a new paradigm for applying machine learning to dynamic optimisation problems.

Abstract
Purpose - The World State of Quality (WSQ) Project aims to evaluate, analyse, rank and categorise countries according to their performance in quality as a multidimensional concept. The Project involves the computation of an overall score for each country, obtained as a weighted average of ranking positions of 16 metrics, with weights determined by a panel of experts. Methodology-This work proposes an alternative strategy for that procedure, using a Benefit-of-the-Doubt (BoD) Composite Indicator approach under the framework of Data Envelopment Analysis (DEA). This strategy avoids the need of using subjective weights and normalising data by rank positions, using a more objective procedure to obtain the countries’ ranking. A new overall score of the World State of Quality is proposed, which allows the categorisation of countries’ performance. The novel insights resulting from the use of this methodology are discussed, including the identification of strengths and weaknesses of the various countries, and the peers that can be used for facilitating continuous improvements policies. Findings - The results show that the BoD approach and the original method used by the WSQ Project present comparable results. Countries’ strengths and weaknesses and their suitable peers and targets for benchmarking are presented with illustrative examples. Originality/value – A novel frontier approach for countries’ benchmarking regarding their performance in quality is proposed, incorporating new insights into the current method. © 2022 Universidade do Minho. All rights reserved.

Abstract
The process of sectorization aims at dividing a dataset into smaller sectors according to certain criteria, such as equilibrium and compactness. Sectorization problems appear in several different contexts, such as political districting, sales territory design, healthcare districting problems and waste collection, to name a few. Solution methods vary from application to application, either being exact, heuristics or a combination of both. In this paper, we propose two quadratic integer programming models to obtain a sectorization: one with compactness as the main criterion and equilibrium constraints, and the other considering equilibrium as the objective and compactness bounded in the constraints. These two models are also compared to ascertain the relationship between the criteria.