Abstract
For eight years, the Text2Story Workshop series has fostered a vibrant research community dedicated to narrative understanding, advancing shared insights into the challenges of modelling narrative structure in text. While earlier approaches laid important foundations, recent progress in Transformers and Large Language Models (LLMs) has fundamentally reshaped the field. Building on the increasing prominence of LLM-based contributions in recent editions, the ninth edition of Text2Story expands the focus toward agentic AI, where systems plan, reason, and interact over time using narratives as internal representations. Recent advances, including long-context architectures, instruction and preference-tuned models, retrieval-augmented generation, and discourse-aware prompting, have broadened the applicability of LLMs to complex narrative tasks. Nevertheless, reliably capturing fine-grained narrative structures remains challenging, particularly for event chains, temporal and causal relations, character development, and perspective consistency. These challenges are amplified in interactive and agentic settings, where narrative coherence, controllability, and reliability are critical. This edition of Text2Story explores both the opportunities and limitations of LLMs and agentic systems for narrative understanding, including the analysis of narratives generated by LLMs themselves with respect to consistency, hallucination, bias, and control. Through a diverse program of research papers, works in progress, demos, resources, and keynote talks, the workshop continues to advance narrative understanding in the era of foundation and agentic models.

Abstract
This study addresses the optimization of automated order-picking systems in retail warehouses through the integration of Autonomous Mobile Robots (AMRs) in a parts-to-picker system. A mixed-integer programming model with a lexicographic objective function is developed to optimize AMR planning while maintaining predefined pallet loading sequences. The model aims to minimize the makespan and the number of stock pallets used, and to maximize the continuity of AMR-pallet pairing. Computational experiments across 81 instances demonstrate that the model consistently achieves optimal makespan values in scenarios of moderate complexity. The results indicate that, while increasing the number of AMRs provides limited benefits in simple configurations, it significantly improves performance in complex scenarios. The research contributes to the literature on warehouse automation by providing a solid foundation for the optimization of AMR-assisted order-picking.

Abstract
The adoption of Autonomous Mobile Robots (AMRs) has emerged as a promising solution to enhance efficiency and reduce operational costs for industrial companies. Given the significant cost of AMRs, it is crucial to determine the optimal number and characteristics before making significant investments. This study proposes a decision-support framework based on simulation to assess the impact of integrating AMR robots in a complex distribution center. Additionally, this framework aids decision-makers in determining the optimal fleet size of AMR robots and corresponding charging stations. A simulation model was developed using data from a leading retail company, focusing on pallet movement within the facility, comparing scenarios combining AMRs with other intralogistics implementations. This methodology incorporates uncertainty, variability (statistical distributions to create transportation orders, acceleration, demand and offer fluctuations) and implements fleet management, transportation capacity, demand matching, and resource utilization according to real case scenarios. The proposed model replicates accurate robot coordination and actual deployment environments, ensuring that the tested scenarios approximate the real-world conditions as much as possible. Preliminary findings show results supporting the decision-making for a fleet size to meet weekly production targets, optimize robot utilization, and coordinate charging instances to prevent production stops. Conclusions suggest that the proposed simulation approach is an effective tool for planning and implementing logistics solutions, enabling users to make informed decisions before investing.

Abstract

Abstract
This work studies a two-echelon distribution system, in the context of city logistics, where storage is not permitted at intermediate transfer locations. Therefore, vehicles operating at both echelons need to be synchronised in time and space, allowing loads to be directly transferred from the first to the second echelon vehicles. Moreover, the problem considers that vehicles operating at the first echelon can perform direct deliveries to customers, that load transfers may occur at some customers' locations, and that vehicles operating at the second echelon are able to perform multiple trips before returning to the depot at the end of the day. To address this problem, we propose a novel mixed integer programming (MIP) model for the two-echelon, multi-trip vehicle routing problem with satellite synchronisation and direct deliveries (2E-MTVRPSS-DD). We tighten this formulation with several sets of valid inequalities, including symmetry breaking constraints based on lexicographical ordering, vehicle rounded capacity constraints, and satellite rounded capacity constraints. We test the model using a commercial solver with newly generated instances, and present computational results, as well as an evaluation of the performance of the proposed valid inequalities. The results show that for relatively small instances, the proposed model is able to solve the problem optimally, but in general, is unable to solve large instances in acceptable computational time, even when considering the proposed valid inequalities. Nevertheless, we show that adding these valid inequalities has a positive impact in improving the model's linear relaxation, with better lower and upper bounds, and ultimately in improving the MIP gaps. Moreover, we show that adding symmetry breaking constraints based on lexicographical ordering has a negative impact, in terms of computational time, for the solver to find a first upper bound, and that this issue may be overcome by warm-starting the MIP model.

Abstract
essential for achieving the Sustainable Development Goals (SDGs), particularly in regions aiming to balance energy efficiency, waste management, and urban development. This study explores the application of multicriteria decision-making and statistical techniques to evaluate municipal sustainability, with a focus on renewable energy, using the Alto Minho region of Portugal as a case study. The analysis incorporates 12 SDG indicators across ten municipalities, addressing energy consumption, urban renewal, and waste management. Cluster analysis revealed distinct groups of municipalities, highlighting disparities in sustainability performance. Municipalities such as Melgaco and Moncao excelled in energy-related metrics, while others showed strengths in waste management and urban renewal. The Analytic Hierarchy Process (AHP) emphasized the importance of renewable energy indicators, revealing notable changes in rankings when energy-related criteria were prioritized. Ponte de Lima and Melgaco ranked highest under energy-focused weighting schemes, showcasing their leadership in energy efficiency and renewable adoption. The findings underscore the need for targeted policies to enhance sustainability across municipalities, particularly in regions lagging in energy performance.