Abstract

Abstract
One of the main challenges of one-way carsharing systems is to maximize profit by attracting potential customers and utilizing the fleet efficiently. Pricing plans are mid or long-term decisions that affect customers' decision to join a carsharing system and may also be used to influence their travel behavior to increase fleet utilization e.g., favoring rentals on off-peak hours. These plans contain different attributes, such as registration fee, travel distance fee, and rental time fee, to attract various customer segments, considering their travel habits. This paper aims to bridge a gap between business practice and state of the art, moving from unique single-tariff plan assumptions to a realistic market offer of multi-attribute plans. To fill this gap, we develop a mixed-integer linear programming model and a solving method to optimize the value of plans' attributes that maximize carsharing operators' profit. Customer preferences are incorporated into the model through a discrete choice model, and the Brooklyn taxi trip dataset is used to identify specific customer segments, validate the model's results, and deliver relevant managerial insights. The results show that developing customized plans with time- and location-dependent rates allows the operators to increase profit compared to fixed-rate plans. Sensitivity analysis reveals how key parameters impact customer choices, pricing plans, and overall profit.

Abstract
Experimental assessment of different missing data imputation methods often compute error rates between the original values and the estimated ones. This experimental setup relies on complete datasets that are injected with missing values. The injection process is straightforward for the Missing Completely At Random and Missing At Random mechanisms; however, the Missing Not At Random mechanism poses a major challenge, since the available artificial generation strategies are limited. Furthermore, the studies focused on this latter mechanism tend to disregard a comprehensive baseline of state-of-the-art imputation methods. In this work, both challenges are addressed: four new Missing Not At Random generation strategies are introduced and a benchmark study is conducted to compare six imputation methods in an experimental setup that covers 10 datasets and five missingness levels (10% to 80%). The overall findings are that, for most missing rates and datasets, the best imputation method to deal with Missing Not At Random values is the Multiple Imputation by Chained Equations, whereas for higher missingness rates autoencoders show promising results.

Abstract
The online channel, particularly in the food retail area, has been evolving positively and exponentially in the world, including Portugal. Currently, this type of purchase is increasingly part of people's daily lives, even more so with the emergence of the Covid-19 pandemic. Consequently, in Portugal, most companies adopt a multichannel strategy, where the physical store and the online store operate independently from each other. However, it is necessary to rethink this channel integration model, which may go through an omnichannel strategy, where the physical store and the online store operate as a single store, and where several advantages are already recognized in terms of the consumer's shopping experience. The main objective of this study is to understand the strategy implemented by the company studied, Pingo Doce, through an analysis and description of its channels. To better understand the strategy of the company under study, a semi-structured exploratory interview was carried out with one of the people in charge of Pingo Doce's digital channels, to understand the strategy used by the company and thus complement the data obtained through direct observation and bibliographic research. At the end of the work developed it was possible to understand the positioning of Pingo Doce in the online food retail area and their online and offline distribution strategies.

Abstract
Renewable energy and electric mobility are crucial in addressing current environmental and energy challenges. As the number of electric vehicles increases, more charging infrastructure connected to the electricity distribution network is required. This paper proposes an approach to sizing a fast charging station for electric vehicles. This challenge is addressed by including a battery energy storage system (BESS) and considering the self-production from a renewable energy source (solar energy) in the system. The aim is to minimise the total energy costs, avoid future infrastructure upgrades, and take advantage of the integration of renewable energy resources. The methodology used is a Biased Random Key Genetic Algorithm (BRKGA) based meta-heuristic. Computational experiments were conducted for the sizing of a charging station under four different scenarios that minimise energy costs. The results show that incorporating BESS can lead to a significant reduction in the costs related to the purchase of energy from the grid. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
We present in this proceeding the results of the test phase of the GRAVITY+ adaptive optics. This extreme AO will enable both high-dynamic range observations of faint companions (including exoplanets) thanks to a 40x40 sub-apertures wavefront control, and sensitive observations (including AGNs) thanks to the addition of a laser guide star to each UT of the VLT. This leap forward is made thanks to a mostly automated setup of the AO, including calibration of the NCPAs, that we tested in Europe on the UT+atmosphere simulator we built in Nice. We managed to reproduce in laboratory the expected performances of all the modes of the AO, including under non-optimal atmospheric or telescope alignment conditions, giving us the green light to proceed with the Assembly, Integration and Verification phase in Paranal.