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2022

Rigas, Emmanouil S; Gerding, Enrico H; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick

Mechanism design for efficient offline and online allocation of electric vehicles to charging stations Journal Article

In: Energies, vol. 15, no. 5, 2022, (Funding Information: Funding: This research study was co-financed by Greece and the European Union (European Social Fund–ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Reinforcement of Postdoctoral Researchers-2nd Cycle? (MIS-5033021), implemented by State Scholarships Foundation (IKY). Copyright 2022 Elsevier B.V., All rights reserved.).

Abstract | Links | BibTeX | Tags: Charging, Electric Vehicles, Fixed price, mechanism design, Scheduling, VCG

@article{soton455806,

title = {Mechanism design for efficient offline and online allocation of electric vehicles to charging stations},

author = {Emmanouil S Rigas and Enrico H Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades},

url = {https://eprints.soton.ac.uk/455806/},

year  = {2022},

date = {2022-03-01},

journal = {Energies},

volume = {15},

number = {5},

abstract = {ensuremath<pensuremath>The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total COensuremath<subensuremath>2ensuremath</subensuremath> emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey?Clark?Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs? aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we observed that they had satisfactory scalability. Additionally, the VCG mechanism achieved an up to 2.2% improvement in terms of the number of vehicles that were charged compared to the fixed-price one and, in cases where the stations were congested, it calculated higher prices for the EVs and provided a higher profit for the stations, but lower utility to the EVs. However, in a theoretical evaluation, we proved that the variant of the VCG mechanism being proposed in this paper still guaranteed truthful reporting of the EVs? preferences. In contrast, the fixed-price one was found to be vulnerable to agents? strategic behavior as non-truthful EVs can charge instead of truthful ones. Finally, we observed the online algorithms to be, on average, at 95.6% of the offline ones in terms of the average number of serviced EVs.ensuremath</pensuremath>},

note = {Funding Information: 

Funding: This research study was co-financed by Greece and the European Union (European Social Fund–ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Reinforcement of Postdoctoral Researchers-2nd Cycle? (MIS-5033021), implemented by State Scholarships Foundation (IKY). 

Copyright 2022 Elsevier B.V., All rights reserved.},

keywords = {Charging, Electric Vehicles, Fixed price, mechanism design, Scheduling, VCG},

pubstate = {published},

tppubtype = {article}

}

Rigas, Emmanouil S; Gerding, Enrico H; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick

Mechanism design for efficient offline and online allocation of electric vehicles to charging stations Journal Article

Abstract | Links | BibTeX

@article{soton455806,

title = {Mechanism design for efficient offline and online allocation of electric vehicles to charging stations},

author = {Emmanouil S Rigas and Enrico H Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades},

url = {https://eprints.soton.ac.uk/455806/},

year  = {2022},

date = {2022-03-01},

journal = {Energies},

volume = {15},

number = {5},

abstract = {ensuremath<pensuremath>The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total COensuremath<subensuremath>2ensuremath</subensuremath> emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey?Clark?Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs? aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we observed that they had satisfactory scalability. Additionally, the VCG mechanism achieved an up to 2.2% improvement in terms of the number of vehicles that were charged compared to the fixed-price one and, in cases where the stations were congested, it calculated higher prices for the EVs and provided a higher profit for the stations, but lower utility to the EVs. However, in a theoretical evaluation, we proved that the variant of the VCG mechanism being proposed in this paper still guaranteed truthful reporting of the EVs? preferences. In contrast, the fixed-price one was found to be vulnerable to agents? strategic behavior as non-truthful EVs can charge instead of truthful ones. Finally, we observed the online algorithms to be, on average, at 95.6% of the offline ones in terms of the average number of serviced EVs.ensuremath</pensuremath>},

note = {Funding Information: 

Funding: This research study was co-financed by Greece and the European Union (European Social Fund–ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Reinforcement of Postdoctoral Researchers-2nd Cycle? (MIS-5033021), implemented by State Scholarships Foundation (IKY). 

Copyright 2022 Elsevier B.V., All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Show all

2022

Rigas, Emmanouil S; Gerding, Enrico H; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick

Mechanism design for efficient offline and online allocation of electric vehicles to charging stations Journal Article

Abstract | Links | BibTeX | Tags: Charging, Electric Vehicles, Fixed price, mechanism design, Scheduling, VCG

@article{soton455806,

title = {Mechanism design for efficient offline and online allocation of electric vehicles to charging stations},

author = {Emmanouil S Rigas and Enrico H Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades},

url = {https://eprints.soton.ac.uk/455806/},

year  = {2022},

date = {2022-03-01},

journal = {Energies},

volume = {15},

number = {5},

abstract = {ensuremath<pensuremath>The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total COensuremath<subensuremath>2ensuremath</subensuremath> emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey?Clark?Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs? aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we observed that they had satisfactory scalability. Additionally, the VCG mechanism achieved an up to 2.2% improvement in terms of the number of vehicles that were charged compared to the fixed-price one and, in cases where the stations were congested, it calculated higher prices for the EVs and provided a higher profit for the stations, but lower utility to the EVs. However, in a theoretical evaluation, we proved that the variant of the VCG mechanism being proposed in this paper still guaranteed truthful reporting of the EVs? preferences. In contrast, the fixed-price one was found to be vulnerable to agents? strategic behavior as non-truthful EVs can charge instead of truthful ones. Finally, we observed the online algorithms to be, on average, at 95.6% of the offline ones in terms of the average number of serviced EVs.ensuremath</pensuremath>},

note = {Funding Information: 

Funding: This research study was co-financed by Greece and the European Union (European Social Fund–ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Reinforcement of Postdoctoral Researchers-2nd Cycle? (MIS-5033021), implemented by State Scholarships Foundation (IKY). 

Copyright 2022 Elsevier B.V., All rights reserved.},

keywords = {Charging, Electric Vehicles, Fixed price, mechanism design, Scheduling, VCG},

pubstate = {published},

tppubtype = {article}

}

Rigas, Emmanouil S; Gerding, Enrico H; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick

Mechanism design for efficient offline and online allocation of electric vehicles to charging stations Journal Article

Abstract | Links | BibTeX

@article{soton455806,

title = {Mechanism design for efficient offline and online allocation of electric vehicles to charging stations},

author = {Emmanouil S Rigas and Enrico H Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades},

url = {https://eprints.soton.ac.uk/455806/},

year  = {2022},

date = {2022-03-01},

journal = {Energies},

volume = {15},

number = {5},

abstract = {ensuremath<pensuremath>The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total COensuremath<subensuremath>2ensuremath</subensuremath> emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey?Clark?Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs? aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we observed that they had satisfactory scalability. Additionally, the VCG mechanism achieved an up to 2.2% improvement in terms of the number of vehicles that were charged compared to the fixed-price one and, in cases where the stations were congested, it calculated higher prices for the EVs and provided a higher profit for the stations, but lower utility to the EVs. However, in a theoretical evaluation, we proved that the variant of the VCG mechanism being proposed in this paper still guaranteed truthful reporting of the EVs? preferences. In contrast, the fixed-price one was found to be vulnerable to agents? strategic behavior as non-truthful EVs can charge instead of truthful ones. Finally, we observed the online algorithms to be, on average, at 95.6% of the offline ones in terms of the average number of serviced EVs.ensuremath</pensuremath>},

note = {Funding Information: 

Funding: This research study was co-financed by Greece and the European Union (European Social Fund–ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Reinforcement of Postdoctoral Researchers-2nd Cycle? (MIS-5033021), implemented by State Scholarships Foundation (IKY). 

Copyright 2022 Elsevier B.V., All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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Selected Publications

Multi-agent signal-less intersection management with dynamic platoon formation

No data available.

https://eprints.soton.ac.uk/478647/

@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

AI Foundation Models: initial review, CMA Consultation, TAS Hub Response

No data available.

https://eprints.soton.ac.uk/477553/

@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}

The effect of data visualisation quality and task density on human-swarm interaction

No data available.

https://eprints.soton.ac.uk/479970/

@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

Demonstrating performance benefits of human-swarm teaming

Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.

https://eprints.soton.ac.uk/479903/

@inproceedings{soton479903,
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

2022

Rigas, Emmanouil S; Gerding, Enrico H; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick

Mechanism design for efficient offline and online allocation of electric vehicles to charging stations Journal Article

Abstract | Links | BibTeX

@article{soton455806,

title = {Mechanism design for efficient offline and online allocation of electric vehicles to charging stations},

author = {Emmanouil S Rigas and Enrico H Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades},

url = {https://eprints.soton.ac.uk/455806/},

year  = {2022},

date = {2022-03-01},

journal = {Energies},

volume = {15},

number = {5},

abstract = {ensuremath<pensuremath>The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total COensuremath<subensuremath>2ensuremath</subensuremath> emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey?Clark?Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs? aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we observed that they had satisfactory scalability. Additionally, the VCG mechanism achieved an up to 2.2% improvement in terms of the number of vehicles that were charged compared to the fixed-price one and, in cases where the stations were congested, it calculated higher prices for the EVs and provided a higher profit for the stations, but lower utility to the EVs. However, in a theoretical evaluation, we proved that the variant of the VCG mechanism being proposed in this paper still guaranteed truthful reporting of the EVs? preferences. In contrast, the fixed-price one was found to be vulnerable to agents? strategic behavior as non-truthful EVs can charge instead of truthful ones. Finally, we observed the online algorithms to be, on average, at 95.6% of the offline ones in terms of the average number of serviced EVs.ensuremath</pensuremath>},

note = {Funding Information: 

Funding: This research study was co-financed by Greece and the European Union (European Social Fund–ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Reinforcement of Postdoctoral Researchers-2nd Cycle? (MIS-5033021), implemented by State Scholarships Foundation (IKY). 

Copyright 2022 Elsevier B.V., All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}