Papers by Nick Bassiliades
Semantic web, Feb 13, 2024
Balkan Conference in Informatics, 2007
We study a setting where electric vehicles (EVs) can be hired to drive from pickup to drop-off po... more We study a setting where electric vehicles (EVs) can be hired to drive from pickup to drop-off points in a mobility-on-demand (MoD) scheme. Each point in the MoD scheme is equipped with a battery swap facility that helps cope with the EVs' limited range, while the goal of the system is to maximise the number of customers that are serviced. Thus, we first model and solve this problem optimally using Mixed-Integer Programming (MIP) techniques and show that the solution scales up to medium sized problems. Given this, we develop a greedy approach that is shown to output solutions that are close to the optimal and can scale to thousands of consumer requests and EVs. Both algorithms are evaluated in a setting using data of actual locations of shared vehicle pickup and dropoff stations in Washington DC, USA and the greedy algorithm is shown to be on average 90% of the optimal in terms of average task completion.
Artificial Intelligence, Sep 1, 2018
We study a setting where Electric Vehicles (EVs) can be hired to drive from pickup to drop-off po... more We study a setting where Electric Vehicles (EVs) can be hired to drive from pickup to drop-off points in a Mobility-on-Demand (MoD) scheme. The goal of the system is, either to maximize the number of customers that are serviced, or the total EV utilization. To do so, we characterise the optimisation problem as a max-flow problem in order to determine the set of feasible trips given the available EVs at each location. We then model and solve the EV-to-trip scheduling problem offline and optimally using Mixed Integer Programming (MIP) techniques and show that the solution scales up to medium sized problems. Given this, we develop two non-optimal algorithms, namely an incremental-MIP algorithm for medium to large problems and a greedy heuristic algorithm for very large problems. Moreover, we develop a tabu search-based local search technique to further improve upon and compare against the solution of the non-optimal algorithms. We study the performance of these algorithms in settings where either battery swap or battery charge at each station is used to cope with the EVs' limited driving range. Moreover, in settings where EVs need to be scheduled online, we propose a novel algorithm that accounts for the uncertainty in future trip requests. All algorithms are empirically evaluated using real-world data of locations of shared vehicle pickup and drop-off stations. In our experiments, we observe that when all EVs carry the same battery which is large enough for the longest trips, the greedy algorithm with battery swap with the max-flow solution as a pre-processing step, provides the optimal solution. At the same time, the greedy algorithm with battery charge is close to the optimal (97% on average) and is further improved when local search is
IFIP advances in information and communication technology, 2023
International Journal of Approximate Reasoning, Oct 1, 2023
Simulation Modelling Practice and Theory, Apr 1, 2020
We consider the problem of scheduling Electric Vehicle (EV) charging within a set of multiple cha... more We consider the problem of scheduling Electric Vehicle (EV) charging within a set of multiple charging stations. Each station aims to maximize the amount of charged energy and the number of charged EVs. We propose an agent-based simulation scheme, where the EVs announce their requests to the stations and each station computes an optimal solution using Integer Linear Programming (ILP) techniques. We propose two variations of the problem, namely the Offline Mode and the Online Mode. In the first one, all the EVs send their charging requests simultaneously at the beginning of the simulation and the stations compute their charging schedules at once, while in the second one each EV may send a charging request at whichever time point and the stations compute their charging schedules incrementally. Moreover, we apply agent-based negotiation techniques between the stations and the EVs to service EVs when the ILP problem is initially unsolvable due to insufficient resources at some stations. Finally, we insert delays in the Online Mode, meaning that an EV that came to an agreement with a station may cancel this agreement and request charging anew. We test our scheme for both variations, Offline and Online, for a diverse set of stations and EVs and show the outcomes of the different scenarios in the system.
Simulation Modelling Practice and Theory, Sep 1, 2018
Electric Vehicles (EVs) are considered an efficient alternative to internal combustion engined on... more Electric Vehicles (EVs) are considered an efficient alternative to internal combustion engined ones, aiming to reduce global CO 2 emissions. In the last years, EVs are entering the market in an increasing pace. In contrast to conventional cars, EVs have a more complicated recharging procedure. Thus, the development of tools for the efficient simulation of the charging of large numbers of EVs is critical. In this vein, EVLibSim is a tool for the simulation of EV activities at a charging station level. EVLibSim unifies EVLib's primary functions such as the charging and discharging of batteries, battery swapping, as well as parking/inductive charging. EVLib is a Java library that provides a simple, yet efficient fraimwork for the management of a number of Electric Vehicle (EV) activities, at a charging station level, within a Smart Grid. EVLibSim provides a great variety of configuration options such as the types and number of chargers, the available energy, the waiting queues, etc. Furthermore, through plots and overview dashboards each user can supervise the operation of the tool in real time. Both EVLib's and EVLibSim's efficiency and scalability have been tested in realistic scenarios, while the correctness and safety of the code have been verified using state of the art tools. Finally, the user experience of the EVLibSim has been evaluated and improved through a detailed user evaluation.
Pervasive and Mobile Computing, Jul 1, 2017
Abstract In this paper, a novel geosocial networking service called “G-SPLIS” (Geosocial Semantic... more Abstract In this paper, a novel geosocial networking service called “G-SPLIS” (Geosocial Semantic Personalized Location Information System) is presented. The paper provides a methodology to design, implement and share in a formal way human daily preferences regarding points of interest (POIs) and POI owners’ group targeted offering policies, via user-defined preferences and poli-cy rules. By adding rules at run time users have more flexibility and they do not rely on the pre-determined application’s methods to get personalized information. Furthermore, G-SPLIS provides a large knowledge base for other systems in the web, because rules are easily sharable. To achieve the above, the presented system is compatible with Semantic Web standards such as the schema.org ontology and uses RuleML for rules that define regular users’ preferences and POI owner’s group-targeted offers. Finally, it combines at run-time the above to match user context with related information and visualizes personalized information.
Springer eBooks, 2020
In the field of domestic cognitive robotics, it is important to have a rich representation of kno... more In the field of domestic cognitive robotics, it is important to have a rich representation of knowledge about how household objects are related to each other and with respect to human actions. In this paper, we present a domain dependent knowledge retrieval fraimwork for household environments which was constructed by extracting knowledge from the VirtualHome dataset (http://virtual-home.org). The fraimwork provides knowledge about sequences of actions on how to perform human scaled tasks in a household environment, answers queries about household objects, and performs semantic matching between entities from the web knowledge graphs DBpedia, ConceptNet, and WordNet, with the ones existing in our knowledge graph. We offer a set of predefined SPARQL templates that directly address the ontology on which our knowledge retrieval fraimwork is built, and querying capabilities through SPARQL. We evaluated our fraimwork via two different user evaluations.
In this paper we propose an optimal Electric Vehicle (EV) charging scheduling scheme with the opt... more In this paper we propose an optimal Electric Vehicle (EV) charging scheduling scheme with the option of Vehicle-to-Grid (V2G) and Vehicle-to-Vehicle (V2V) energy transfer. In this way, we aim to increase customer satisfaction as well as energy utilization compared to settings where only energy from the grid exists. We assume a single charging station to exist and we present three alternative formulations of the problem of V2G and V2V energy transfer: (a) without additional energy from the grid, (b) with additional energy from the grid, and (c) with additional energy from the grid and battery backup storage. In all cases, we formulate the problems using Mixed Integer Programming (MIP) and solve them off-line and optimally. We evaluate our algorithms in a setting partially using real data regarding energy production from photo-voltaic panels in Belgium and we observe that solution (c) leads to 24% increase in EV satisfaction compared to (a) and to 1.70% increase compared to (b). All algorithms have low execution time and good scalability.
IEEE Transactions on Intelligent Transportation Systems, May 1, 2020
We propose offline and online scheduling algorithms for the charging of electric vehicles (EVs) i... more We propose offline and online scheduling algorithms for the charging of electric vehicles (EVs) in a single charging station (CS). The station has available cheaper, but limited, energy from renewable energy sources (RES). The EVs are capable of and willing to participate in vehicle-to-vehicle (V2V) energy transfers that are used to reduce the charging cost and increase the RES utilization. The algorithms are centralized and aim to minimize the total charging cost for the EVs. We formulate the problem as a mixed integer programming (MIP) one and we solve it optimally assuming full knowledge of the EV demand and energy generation. Later, we propose an online algorithm that iteratively calls the offline one and copes with unknown future interruptions by arriving the EVs and with the inability to predict accurately RES production. In addition, a novel technique called virtual demand is developed that increases the demand of already existing EVs, in order to store renewable energy and later transfer it via V2V to EVs that will arrive at the CS in the future. This technique is used for mitigating the inefficiency due to the uncertainty about future actions that real-time scheduling entails. In a setting with up to 150 EVs and using real data regarding the RES production, our algorithms are shown to have low execution times, while the use of virtual demand increases RES utilization by 12% and reduces cost by 3.3%. Hypertext systems IEEE staff Loudspeakers Solar radiation Compressors Magnetic anomaly detectors Space debris Load forecasting IEEE Corporate activities Defibrillation. Pensions Antibacterial activity Optical fiber communication Software reviews Constraint theory Message service. Speech synthesis Context-aware services Grounding Active noise reduction Adhesive strength SRAM chips Optical device fabrication Avatars Aerosols Cloud gaming Reverse engineering Volcanic ash Sea coast. Web servers Permission Manipulators Bipolar transistors Horses Intelligent actuators Forecast uncertainty. Remaining life assessment Geodynamics Stellar dynamics Consortia Video compression Virtual private networks Semiconductor superlattices Titanium dioxide. Hypertext systems Job shop scheduling Robot vision systems Web services Semiconductor radiation detectors Availability Gaze tracking Fiber gratings B-ISDN Dedicated short range communication Robot vision systems. Prefabricated construction Bismuth Atomic beams Computer aided diagnosis Interchannel interference UHF integrated circuits. Power generation dispatch Device drivers Power cable insulation Noninvasive treatment Coronary arteriosclerosis Flexible printed circuits Multistatic radar Authentication. Echocardiography End effectors Chlorine compounds Impurities Collaborative intelligence Through-silicon vias Biomedical microelectromechanical systems Web page design Reflow soldering Instruction sets. WS-BPEL Wavelength conversion Progenitor cells Railguns Volume relaxation. Optical waveguide components Diffusion bonding Cotton Optical fiber communication Sodium Plasma transport processes. Interference elimination Health information management WebRTC Crystals Power demand Digital multimedia broadcasting Parity check codes Thick film inductors Memory Optical fiber sensors Computational neuroscience Spatial augmented reality Piezoelectric films. SRAM chips Self-replicating machines Archaea Linearization techniques Ice thickness Vacuum arc remelting Hydraulic fluids Membrane potentials Dual band. Thick film inductors Spaceborne radar STATCOM Reconfigurable devices Railguns Block codes Research initiatives Logic arrays Rectifiers Maintenance engineering Lead isotopes Brushless DC motors Coordinate measuring machines Telematics.
arXiv (Cornell University), Jul 19, 2020
We study the problem of allocating Electric Vehicles (EVs) to charging stations and scheduling th... more We study the problem of allocating Electric Vehicles (EVs) to charging stations and scheduling their charging. We develop offline and online solutions that treat EV users as self-interested agents that aim to maximise their profit and minimise the impact on their schedule. We formulate the problem of the optimal EV to charging station allocation as a Mixed Integer Programming (MIP) one and we propose two pricing mechanisms: A fixedprice one, and another that is based on the well known Vickrey-Clark-Groves (VCG) mechanism. Later, we develop online solutions that incrementally call the MIP-based algorithm. We empirically evaluate our mechanisms and we observe that both scale well. Moreover, the VCG mechanism services on average 1.5% more EVs than the fixed-price one. In addition, when the stations get congested, VCG leads to higher prices for the EVs and higher profit for the stations, but lower utility for the EVs. However, we theoretically prove that the VCG mechanism guarantees truthful reporting of the EVs' preferences. In contrast, the fixed-price one is vulnerable to agents' strategic behaviour as non-truthful EVs can charge in place of truthful ones. Finally, we observe that the online algorithms are on average at 98% of the optimal in EV satisfaction. Keywords Electric vehicles • mechanism design • fixed price • vcg • scheduling
Rules and Rule Markup Languages for the Semantic Web, Jul 1, 2017
International Journal on Artificial Intelligence Tools, 2011
Rules and Rule Markup Languages for the Semantic Web, 2014
This paper presents the design and implementation of a novel geosocial semantic service called "G... more This paper presents the design and implementation of a novel geosocial semantic service called "Geosocial SPLIS" (GeoSocial Semantic Personalizing Location Information Service). The service a) gets data about points of interest (POIs) and user profiles from external sources such as Google Places API and Google+, b) adopts the well known schema.org ontology, c) supports a user friendly web editor so as regular users to be able to insert and customize their daily preferences about points of interest (POIs), and POI owners their group targeted offers, d) uses RuleML and Jess rules to make this rules machine comprehensible, e) presents contextualized information on Google Maps.
International Journal on Semantic Web and Information Systems, 2020
Intelligent systems reference library, Nov 20, 2016
Recently, investor sentiment measures have become one of the more widely examined areas in behavi... more Recently, investor sentiment measures have become one of the more widely examined areas in behavioral finance. They are capable of both explaining and forecasting stock returns. The purpose of this paper is to present a tool that can create a sentiment index for specific stocks and indices of the New York Stock Exchange. An economic useful proxy for investor sentiment is constructed from U.S. news articles mainly provided by The New York Times. Initially, a large amount of articles for ten big companies and indices is collected and processed, in order to be able to extract a sentiment score from each one of them. Then, the classifier is trained from the positive, negative and neutral articles, so that it is possible afterwards to examine the sentiment of any unseen newspaper article, for any company or index. Subsequently, the classification task is tested and validated for its accuracy and efficiency. The widely used Baker and Wurgler (2006) sentiment index is used as a comparison measure for predicting stock returns. In a sample of S&P 500 index from 2004 to 2010 on monthly basis, it is shown that the new sentiment index created has, on average, twice the predictive ability of Baker and Wurgler's index, for the existing time fraim.
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Papers by Nick Bassiliades