Abstract
Interest in integrating lot-sizing and cutting stock problems has been increasing over the years. This integrated problem has been applied in many industries, such as paper, textile and furniture. Yet, there are only a few studies that acknowledge the importance of uncertainty to optimise these integrated decisions. This work aims to address this gap by incorporating demand uncertainty through stochastic programming and robust optimisation approaches. Both robust and stochastic models were specifically conceived to be solved by a column generation method. In addition, both models are embedded in a rolling-horizon procedure in order to incorporate dynamic reaction to demand realisation and adapt the models to a multistage stochastic setting. Computational experiments are proposed to test the efficiency of the column generation method and include a Monte Carlo simulation to assess both stochastic programming and robust optimisation for the integrated problem. Results suggest that acknowledging uncertainty can cut costs by up to 39.7%, while maintaining or reducing variability at the same time.

Abstract
One way to mitigate the high costs of doing science or business at sea is to create technological infrastructures possessing all the skills and resources needed for successful maritime operations, and make those capabilities and skills available to the external entities requiring them. By doing so, the individual economic and scientific agents can be spared the enormous effort of creating and maintaining their own, particular set of equivalent capabilities, thus drastically lowering their initial operating costs. In addition to cost savings, operating based on fully-fledged, shared infrastructures not only allows the use of more advanced scientific equipment and highly skilled personnel, but it also enables the business teams (be it industry or research) to focus on their goals, rather than on equipment, logistics, and support. This paper will describe the TEC4SEA infrastructure, created precisely to operate as described. This infrastructure has been under implementation in the last few years, and has now entered its operational phase. This paper will describe it, present its current portfolio of services, and discuss the most relevant assets and facilities that have been recently acquired, so that the research and industrial communities requiring the use of such assets can fully evaluate their adequacy for their own purposes and projects.

Abstract
The management of knowledge-driven artificial intelligence technologies is essential in order to evaluate their impact on human life and society. Social networks and tech use can have a negative impact on us physically, emotionally, socially and mentally. On the other hand, intelligent systems can have a positive effect on people's lives. Currently, we are witnessing the power of large language models (LLMs) like chatGPT and its influence towards the society. The objective of the workshop is to contribute to the advancement of intelligent technologies designed to address the human condition. This could include precise and personalized medicine, better care for elderly people, reducing private data leaks, using AI to manage resources better, using AI to predict risks, augmenting human capabilities, and more. The workshop's objective is to present research findings and perspectives that demonstrate how knowledge-enabled technologies and applications improve human well-being. This workshop indeed focuses on the impacts at different granularity levels made by Artificial Intelligence (AI) research on the micro granular level, where the daily or regular functioning of human life is affected, and also the macro granulate level, where the long-term or far-future effects of artificial intelligence on people's lives and the human society could be pretty high. In conclusion, this workshop explores how AI research can potentially address the most pressing challenges facing modern societies, and how knowledge management can potentially contribute to these solutions.

Abstract

Abstract
In this article the application of the discrete version of the bat algorithm to flowshop scheduling problems is presented and compared with Simulated Annealing, Local Search, as well as versions of each that start from constructive heuristics (Palmer and CDS). Bat algorithm is a novel metaheuristic, developed for continuous problems that has shown exceptional results. This paper intends to assess its effectiveness and efficiency for discrete problems when compared with other optimization techniques, including Simulated Annealing and Local Search, whose results are already proven. First, it was developed a literature review about those algorithms, then they were implemented in VBA with Microsoft Excel. Once implemented, the parameterization was carried out, ensuring an adequate application of the algorithms before they can be compared. Then, the methods were applied for 30 normally distributed instances, in order to draw broader conclusions. Finally, a statistical evaluation was carried out and concluded the inferiority of the Local Search in relation to the metaheuristics and the superiority of the hybrid version of the Bat Algorithm with CDS in relation to Simulated Annealing, with significantly better solutions, in an equal computation time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Conflict-free Replicated Data Types (CRDTs) are useful to allow a distributed system to operate on data even when partitions occur, and thus preserve operational availability. Most CRDTs need to track whether data evolved concurrently at different nodes and needs to be reconciled; this requires storing causality metadata that is proportional to the number of nodes. In this paper, we try to overcome this limitation by introducing a stochastic mechanism that is no longer linear on the number of nodes, but whose accuracy is now tied to how much divergence occurs between synchronizations. This provides a new tool that can be useful in deployments with many anonymous nodes and frequent synchronizations. However, there is an underlying trade-off with classic deterministic solutions, since the approach is now probabilistic and the accuracy depends on the configurable metadata space size.