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Decentralised Parallel Machine Scheduling for Multi-Agent Task Allocation

Decentralised Parallel Machine Scheduling for Multi-Agent Task Allocation
Decentralised Parallel Machine Scheduling for Multi-Agent Task Allocation
Multi-agent task allocation problems pervade a wide range of real-world applications, such as search and rescue in disaster management, or grid computing. In these applications, where agents are given tasks to perform in parallel, it is often the case that the performance of all agents is judged based on the time taken by the slowest agent to complete its tasks. Hence, efficient distribution of tasks across heterogeneous agents is important to ensure a short completion time. An equivalent problem to this can be found in operations research, and is known as scheduling jobs on unrelated parallel machines (also known as R||Cmax). In this paper, we draw parallels between unrelated parallel machine scheduling and multi-agent task allocation problems, and, in so doing, we present the decentralised task distribution algorithm (DTDA), the first decentralised solution to R||Cmax. Empirical evaluation of the DTDA is shown to generate solutions within 86–97% of the optimal on sparse graphs, in the best case, whilst providing a very good estimate (within 1%) of the global solution at each agent.
Macarthur, Kathryn
4c7db797-1679-4fd1-9dac-26f84bd5debd
Vinyals, Meritxell
6091b72e-8954-4bff-b9ba-21a58bd6fa37
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Macarthur, Kathryn
4c7db797-1679-4fd1-9dac-26f84bd5debd
Vinyals, Meritxell
6091b72e-8954-4bff-b9ba-21a58bd6fa37
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Macarthur, Kathryn, Vinyals, Meritxell, Farinelli, Alessandro, Ramchurn, Sarvapali and Jennings, Nick (2011) Decentralised Parallel Machine Scheduling for Multi-Agent Task Allocation. Fourth International Workshop on Optimisation in Multi-Agent Systems, Taiwan.

Record type: Conference or Workshop Item (Paper)

Abstract

Multi-agent task allocation problems pervade a wide range of real-world applications, such as search and rescue in disaster management, or grid computing. In these applications, where agents are given tasks to perform in parallel, it is often the case that the performance of all agents is judged based on the time taken by the slowest agent to complete its tasks. Hence, efficient distribution of tasks across heterogeneous agents is important to ensure a short completion time. An equivalent problem to this can be found in operations research, and is known as scheduling jobs on unrelated parallel machines (also known as R||Cmax). In this paper, we draw parallels between unrelated parallel machine scheduling and multi-agent task allocation problems, and, in so doing, we present the decentralised task distribution algorithm (DTDA), the first decentralised solution to R||Cmax. Empirical evaluation of the DTDA is shown to generate solutions within 86–97% of the optimal on sparse graphs, in the best case, whilst providing a very good estimate (within 1%) of the global solution at each agent.

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More information

Published date: 3 May 2011
Additional Information: Event Dates: May 3, 2011
Venue - Dates: Fourth International Workshop on Optimisation in Multi-Agent Systems, Taiwan, 2011-05-03
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 272234
URI: https://eprints.soton.ac.uk/id/eprint/272234
PURE UUID: c23f04db-7545-4250-9494-82a416fb3922
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 28 Apr 2011 11:22
Last modified: 13 Jun 2018 00:33

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