With the rising popularity of electric vehicles (EVs), modern service systems, such as ride-hailing delivery services, are increasingly integrating EVs into their operations. Unlike conventional vehicles, EVs often have a shorter driving range, necessitating careful consideration of charging when fulfilling requests. With recent advances in Vehicle-to-Grid (V2G) technology—allowing EVs to also discharge energy back to the grid—new opportunities and complexities emerge. We introduce the Electric Vehicle Orienteering Problem with V2G (EVOP-V2G): a profit-maximisation problem where EV drivers must select a subset of customer requests while managing when and where to charge or discharge. This involves navigating dynamic electricity prices, charging station selection, and route constraints. We formulate the problem as a Mixed Integer Programming (MIP) model and propose two near-optimal metaheuristic algorithms: one evolutionary (EA) and the other based on large neighbourhood search (LNS). We compare these three algorithms with a greedy baseline on real-world data, showing that the proposed methods achieve up to twice the profit. V2G contributes about 20 % of the total profit in the default settings. MIP finds optimal solutions for small cases (30 orders, 3 stations) but does not scale well. EA and LNS give near-optimal results for small cases and handle large ones (900 orders, 70 stations) efficiently. Our work highlights a promising path toward smarter, more profitable EV-based mobility systems that actively support the energy grid.

With the rise of electric vehicles (EVs), new opportunities are emerging, including Vehicle-to-Everything (V2X) technology, which enables EVs to both charge from and discharge to the grid, homes, and other EVs. Leveraging V2X, EVs can act as "batteries-on-wheels," dynamically trading energy to minimize electricity costs and emissions based on real-time energy and mobility forecasts. For households, unlocking these benefits requires smart, automated management of EV charging and discharging. However, designing optimal schedules is a complex task involving dynamic and often uncertain variables such as emission rates, household electricity use, solar generation, electricity prices, and EV travel patterns. To tackle this challenge, we have developed an interactive dashboard that combines forecasting and optimization to support smarter energy decisions. To the best of our knowledge, this is the first system that integrates real-world minute-level forecasts, dynamic scheduling algorithms, and interactive spatiotemporal visualization. It enables users to compare V2X scenarios and explore the impact of forecasting accuracy and configuration strategies over time. The dashboard visualizes key variables and simulates optimal charging and discharging decisions. In this demo, we showcase results from a 31-day simulation at 5-minute intervals, using real-world data to illustrate the impact of various energy management strategies.

Electric Vehicles (EVs) are increasingly recognized not only as key assets for sustainable transportation but also as flexible, distributed energy resources. This dual role is enabled by the emergence of Vehicle-to-Everything (V2X) technologies, which allow EVs to bidirectionally charge and discharge energy across various domains, such as the grid, homes, buildings, other vehicles, and mobile devices. As global momentum builds toward decarbonizing both transportation and energy systems, the integration of V2X positions EVs at the intersection of these domains, offering new opportunities to enhance energy efficiency, grid resilience, and environmental sustainability. This tutorial provides a comprehensive introduction to the potential of EVs as both transportation and energy storage solutions, focusing specifically on practical applications and recent advancements in V2X integration. Participants will explore foundational concepts and practical use cases across individual and fleet scenarios, including energy-aware EV routing, smart charging, and coordinated energy management. By bridging transportation and energy domains, the tutorial offers participants insights into leveraging EVs to enhance mobility, resilience, and energy efficiency.

Finding shortest paths in a Euclidean plane containing polygonal obstacles is a well-studied problem motivated by a variety of real-world applications. The state-of-the-art algorithms require finding obstacle corners visible to the source and target, and need to consider potentially a large number of candidate paths. This adversely affects their query processing cost. We address these limitations by proposing a novel adaptation of hub labeling which is the state-of-the-art approach for shortest distance computation in road networks. Our experimental study conducted on the widely used benchmark maps shows that our approach is typically 1-2 orders of magnitude faster than two state-of-the-art algorithms.

Video games feature a dynamic environment where locations of objects (e.g., characters, equipment, weapons, vehicles etc.) frequently change within the game world. Although searching for relevant nearby objects in such a dynamic setting is a fundamental operation, this problem has received little research attention. In this paper, we propose a simple lightweight index, called Grid Tree, to store objects and their associated textual data. Our index can be efficiently updated with the underlying updates such as object movements, and supports a variety of object search queries, including k nearest neighbors (returning the k closest objects), keyword k nearest neighbors (returning the k closest objects that satisfy query keywords), and several other variants. Our extensive experimental study, conducted on standard game maps benchmarks and real-world keywords, demonstrates that our approach has up to 2 orders of magnitude faster update times for moving objects compared to state-of-the-art approaches such as navigation mesh and IR-tree. At the same time, query performance of our approach is similar to or better than that of IR-tree and up to two orders of magnitude faster than the other competitor.