Abstract
To get products delivered, clients and retailers agree on a delivery time window. We collaborated with an online retailer to develop a real-world application aimed at dynamically determining the delivery fee for each time window while ensuring the explainability of the pricing policy. This sequential decision-making problem arises as new customers continuously arrive. The objective is to maximize the final profit, given by the sum of baskets and delivery fees, discounted by the transportation and fleet costs. As multiple customers share the same delivery route, the costs are distributed among them, complicating the calculation of the marginal cost of each customer. Our study employs Genetic Programming (GP) to create explainable and easy-to-compute pricing policies to determine the delivery fees. These policies, expressed as mathematical formulas, rank price panels combinations of time slots and corresponding fees to identify optimal prices for each customer. The inputs to the GP algorithm capture the current state of the system, including factors such as capacity, customer location, and basket value. The resulting expressions offer operational managers a transparent pricing policy that allows them to maximize total profit.

Abstract
The increasing integration of renewable energy sources and decentralized generation requires demand-side flexibility to improve grid stability and balance local energy flows. Local Flexibility Markets (LFMs) provide a framework for optimizing flexibility transactions within energy communities. This paper presents a model for quantifying and pricing residential resources flexibility, enabling prosumers to submit bids in an LFM managed by the Community Manager. The methodology relies on a linear optimization problem, where a Home Energy Management System first determines optimal consumption baselines. Then an iterative sensitivity analysis estimates upward, and downward flexibility bands and sets offer prices per resource. The market operates as two asymmetric voluntary pools, clearing flexibility offers and requests. Results show that Battery Energy Storage Systems and Electric Vehicles provide the most effective flexibility, significantly reducing energy costs. Future research should improve pricing mechanisms and scalability to support LFM adoption in different residential settings.

Abstract
Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. This paper introduces a two-party quantum SMC protocol designed for evaluating binary Boolean functions using single qubits. Complexity analyses demonstrate a reduction of 66.7% in required quantum resources, achieved by utilizing single qubits instead of multi-particle entangled states. However, the quantum communication cost has increased by 40% due to the amplified exchange of qubits among participants. Furthermore, we bolster security by performing additional quantum operations along the y-axis of the Bloch sphere, effectively hiding the output from potential adversaries. We design the corresponding quantum circuit and implement the proposed protocol on the IBM Qiskit platform, yielding reliable outcomes.

Abstract
The sustainable utilization of forest biomass for bioenergy production is increasingly challenged by the variability and unpredictability of raw material availability. These challenges are particularly critical in regions like Central Portugal, where seasonality, dispersed resources, and wildfire prevention policies disrupt procurement planning. This study investigates two flexibility strategies-dynamic network reconfiguration and operations postponement-as policy relevant tools to enhance resilience in forest-to-bioenergy supply chains. A novel mathematical model, the mobile Facility Location Problem with dynamic Operations Assignment (mFLP-dOA), is proposed and solved using a scalable matheuristic approach. Applying the model to a real case study, we demonstrate that incorporating temporary intermediate nodes and adaptable processing schedules can reduce costs by up to 17% while improving operational responsiveness and reducing non-productive machine time. The findings offer strategic insights for policymakers, biomass operators, and regional planners aiming to design more adaptive and cost-effective biomass supply systems, particularly under environmental risk scenarios such as summer operation bans. This work supports evidence-based planning and investment in flexible logistics infrastructure for cleaner and more resilient bioenergy supply chains.

Abstract
Renewable energy resources are crucial for addressing global economic and environmental challenges. Energy communities, which unite consumers to pursue shared energy goals, present a promising solution for reducing energy costs and enhancing sustainability. This study analyzes the optimal sizing and operation of energy community resources, formulating the problem as mixed-integer linear programming (MILP) models. Two tools are employed: one for daily operation, calculating energy setpoints for community assets such as battery energy storage systems (BESS) and electric vehicles (EVs), and another for sizing photovoltaic (PV) panels and BESS capacities to minimize costs while optimizing local energy trades. Due to the high computational demands of MILP, three optimization methods are compared: deterministic, hybrid particle swarm optimization (PSO), and evolutionary PSO (EPSO). The hybrid PSO method handles binary and continuous variables efficiently, while EPSO introduces diversity to improve solution quality in complex scenarios. These metaheuristic approaches address the trade-off between solution accuracy and computational effort, providing reliable tools for decision-makers in energy communities.

Abstract
This workshop proposal is the third edition of a workshop which has been organised at EICS 2023 and EICS 2024. This edition aims to bring together researchers and practitioners interested in the engineering of interactive systems that embed AI technologies (as for instance, AI-based recommender systems) or that use AI during the engineering lifecycle. The overall objective is to identify (from experience reported by participants) methods, techniques, and tools to support the use and inclusion of AI technologies throughout the engineering lifecycle for interactive systems. A specific focus is on guaranteeing that user-relevant properties such as usability and user experience are accounted for. Contributions are also expected to address system-related properties, including resilience, dependability, reliability, or performance. Another focus is on the identification and definition of software architectures supporting this integration.