Abstract
Sea exploration is important for countries with large areas in the ocean under their control, since in the future it may be possible to exploit some of the resources in the seafloor. The sea exploration problem was presented by Pedroso et al. [13] (unpublished); we maintain most of the paper's structure, to provide the needed theoretical background and context. In the sea exploration problem, the aim is to schedule the expedition of a ship for collecting information about the resources on the seafloor. The goal is to collect data by probing on a set of carefully chosen locations, so that the information available is optimally enriched. This problem has similarities with the orienteering problem, where the aim is to plan a time-limited trip for visiting a set of vertices, collecting a prize at each of them, in such a way that the total value collected is maximum. In our problem, the score at each vertex is associated with an estimation of the level of the resource on the given surface, which is done by regression using Gaussian processes. Hence, there is a correlation among scores on the selected vertices; this is the first difference with respect to the standard orienteering problem. The second difference is the location of each vertex, which in our problem is a freely chosen point on a given surface. Results on a benchmark test set are presented and analyzed, confirming the merit of the approach proposed. In this paper, additional methods are presented, along with a small topological result and subsequent proof of the convergence of these same methods to the optimal solution, when we have instant access to the ground truth and the underlying function is piecewise continuous.

Abstract
The forests and woodlands of Guinea-Bissau are a biodiversity hotspot under threat, which are progressively being replaced by cashew tree orchards. While the exports of cashew nuts significantly contribute to the gross domestic product and support local livelihoods, the country's natural capital is under significant pressure due to unsustainable land use. In this context, official entities strive to counter deforestation, but the problem persists, and there are currently no systematic or automated means for objectively monitoring and reporting the situation. Furthermore, previous remote sensing approaches failed to distinguish cashew orchards from forests and woodlands due to the significant spectral overlap between the land cover types and the highly intertwined structure of the cashew tree patches. This work contributes to overcoming such difficulty. It develops an affordable, reliable, and easy-to-use procedure based on machine learning models and Sentinel-2 images, automatically detecting cashew orchards with a dice coefficient of 82.54%. The results of this case study designed for the Cantanhez National Park are proof of concept and demonstrate the viability of mapping cashew orchards. Therefore, the work is a stepping stone towards wall-to-wall operational monitoring in the region.

Abstract
The two-dimensional knapsack problem consists in packing rectangular items into a single rectangular box such that the total value of packed items is maximized. In this article, we restrict to 2-stage nonexact guillotine cut packings and consider the variant with splittable items: each item can be horizontally cut as many times as needed, and a packing may contain only a portion of an item. This problem arises in the packing of semifluid items, like tubes of small radius, which has the property to behave like a fluid in one direction, and as a solid in the other directions. In addition, the items are to be packed into stable stacks, that is, at most one item can be laid on top of another item, necessarily wider than itself. We establish that this variant of the two-dimensional knapsack problem is NP-hard, and propose an integer linear formulation. We exhibit very strong dominance properties on the structure of extreme solutions, that we call canonical packings. This structure enables us to design polynomial time algorithms for some special cases and a pseudo-polynomial time algorithm for the general case. We also develop a Fully Polynomial Time Approximation Scheme (FPTAS) for the case where the height of each item does not exceed the height of the box. Finally, some numerical results are reported to assess the efficiency of our algorithms.

Abstract
The recently-defined class of integer programming games (IPG) models situations where multiple self-interested decision makers interact, with their strategy sets represented by a finite set of linear constraints together with integer requirements. Many real-world problems can suitably be cast in this way, hence anticipating IPG outcomes is of crucial value for policy makers. Nash equilibria have been widely accepted as the solution concept of a game. Thus, their computation provides a reasonable prediction of games outcome. In this paper, we start by showing the computational complexity of deciding the existence of a Nash equilibrium for an IPG. Then, using sufficient conditions for their existence, we develop a general algorithmic approach that is guaranteed to return a Nash equilibrium when the game is finite and to approximate an equilibrium when payoff functions are Lipschitz continuous. We also showcase how our methodology can be changed to determine other types of equilibria. The performance of our methods is analyzed through computational experiments on knapsack, kidney exchange and a competitive lot-sizing games. To the best of our knowledge, this is the first time that equilibria computation methods for general IPGs have been designed and computationally tested.

Abstract
In this work, we study a flexible compensation scheme for last-mile delivery where a company outsources part of the activity of delivering products to its customers to occasional drivers (ODs), under a scheme named crowdshipping. All deliveries are completed at the minimum total cost incurred with their vehicles and drivers plus the compensation paid to the ODs. The company decides on the best compensation scheme to offer to the ODs at the planning stage. We model our problem based on a stochastic and dynamic environment where delivery orders and ODs volunteering to make deliveries present themselves randomly within fixed time windows. The uncertainty is endogenous in the sense that the compensation paid to ODs influences their availability. We develop a deep reinforcement learning (DRL) algorithm that can deal with large instances while focusing on the quality of the solution: we combine the combinatorial structure of the action space with the neural network of the approximated value function, involving techniques from machine learning and integer optimization. The results show the effectiveness of the DRL approach by examining out-of-sample performance and that it is suitable to process large samples of uncertain data, which induces better solutions.

Abstract
Socioeconomic changes have caused profound transformations in forest landscapes and increased abandonment of rural areas, leading to fuel accumulation and higher landscape homogeneity, and consequently, raising the rural fires risk. Rural fires risk is also fueled by climate change, due to heat waves and lack of precipitation. In this context, rural communities inhabiting forest areas are those who suffer the most, because rural fires, land degradation and climate change can disturb their food and economic strategy. These communities already suffer from underdeveloped rural infrastructure, and services, lack of labor and education opportunities, that trigger poverty and migration. Given this accelerating pace of change and increasing uncertainty, many fields of knowledge have been dedicated to contributing towards a more sustainable and inclusive future. In service research, transformative service research (TSR) literature plays a central role on understanding problems and finding solutions that improve well-being and create uplifting change through services. Similarly, the fire research field highlights the need for an integrated perspective to analyze all the aspects involved in rural fires occurrence, whether they are of an environmental or economic nature, or a sociological or demographic nature. This study aims to explore new services to cocreate value with forest-related rural communities, thus helping to manage forest areas and mitigate rural fires risks. A qualitative methodology was employed involving 28 participants related to fire management and forest areas and communities, including actors from industries, public entities, academics, the third sector. The data collected through individual interviews were transcribed, coded, and analyzed following a thematic analysis approach, with NVivo software support. Overall, the study emphasizes the need for an endogenous and adapted set of services to cocreate value with vulnerable communities in forest areas, which consequently enable rural fires mitigation. Given the high level of land abandonment and accumulation of residual materials that increases the risk of rural fires, the development of valuing and recovery solutions is a priority. Finally, this research can also help decision-makers and stakeholders to generate and support services that cocreate value with rural communities to a sustainable, safe and inclusive future.