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In the early years of school, pupils have a very positive approach towards any new topic. Very often, the first contact with mathematics in the Italian school risks giving a limited and somehow negative impression, generating long-lasting effects. In this paper, we report on a series of experiences carried out in a second-year class of a primary school that allowed us to introduce new concepts and approaches through games and puzzles. The aim was to stimulate the creativity and empower pupils. We have used simple materials and applied the "Puzzle-Based Learning" method. Despite the limited time devoted to this experience, the outcomes have been extremely positive.

We report on a playful introduction of optimization in a high school. The final event of this experience has been an orienteering competition, in which the decision-making component plays a central role. This type of event has been very involving, and it boosted the interest of the students toward operations research. After summarizing the activities in class that preceded the competition, we describe in detail the organization aspects and the outcome. We also provide a simple AMPL code that compares the choices made by the participants with their optimal trajectory, that is, the one maximizing the score with the same traveled distance.

Battery-assisted bicycles, or E-bikes, are part of a disruptive wave of transportation technology that uses electricity and rechargeable batteries to increase the velocity, the traveled distance and, as a consequence, the ridership. Biking and E-biking are globally recognized to have the potential to play an important role in the transition to a Net-Zero society. The widespread availability of E-bikes is significantly impacting several sectors of the tourist industry. Therefore, Touristic Administrations (TAs) now provide tourists with trail options and the corresponding charging infrastructure for E-bikers with different profiles. Our main objective is to provide TAs with a suitable decision-support tool that serves two purposes: (1) finding locations for charging stations by considering the difficulty and the cost of installing such stations in remote, often off-the-road locations; and (2) designing itineraries that are suitable for different categories of E-bikers. In the scientific literature, the first decision component has been mostly addressed in the context of electric cars, and it is not suitable for E-bikes. On the other hand, works on the second decision focused on muscular bikes, thus ignoring the first decision component. In this paper, we aim at closing this gap. We formulate this problem as a mixed-integer linear program. We develop an efficient branch-and-cut algorithm and present a comprehensive computational experiment. In particular, we provide a case study in the Asiago Sette Comuni Plateau in Italy, where the obtained charging stations and bike trails maximize a measure of attractiveness for three types of users. [ABSTRACT FROM AUTHOR]

In many major cities, metro lines constitute the backbone of urban public transport, providing an efficient and greener alternative to private mobility. An important feature that distinguishes metro lines from other public transport means, such as buses, is that metros are typically tightly resource constrained. The trains operating on a particular line are often specifically fitted for that line, making any capacity expansion extremely costly and time-consuming. Therefore, researchers and operators alike are seeking ways to make better use of existing resources. One possible way of doing so is by adapting timetables to forecasted demand while accounting for limited vehicle capacities. Thus, we consider a demand-driven nonperiodic timetabling problem for a two-directional metro line that minimizes the total passenger waiting time through the efficient scheduling of the available trains. Considering that passengers board trains using a well-mixed policy, we explicitly account for train capacities on a moment-to-moment basis. Last, we consider that trains are allowed to short turn. In this respect, we assume that trains must pass by a given station before short turning and are only allowed to idle after having short turned. We devise a polynomial time algorithm for assessing the total passenger waiting time generated by a given timetable and an effective lower bound that is evaluated in linear time. These are used in a variable neighborhood search algorithm, which is embedded in an iterated local search metaheuristic. Classical local search-based neighborhoods are not effective for our problem because they do not explicitly handle the vehicle scheduling decisions. To handle this challenge, we proposed three tailored neighborhoods. We validate our heuristic on the uncapacitated version of the problem. Considering a benchmark of 48 artificial instances with up to 20 stations, our heuristic achieved an average gap of 0.67% and found eight new best solutions. We also validated our heuristic on three sets of instances based on realistic lines from Milan, Madrid, and Beijing. Furthermore, we demonstrate the operational advantages of our optimized timetables in the capacitated version of the problem by comparing them with regular timetables and with exact solutions obtained for the uncapacitated case. Furthermore, we conduct a sensitivity analysis with respect to the capacity of the trains and investigate the impact of a priority boarding policy. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0271. [ABSTRACT FROM AUTHOR]