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Decentralised Parallel Machine Scheduling for Multi-Agent Task Allocation

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

Record type: Conference or Workshop Item (Paper)


Multi-agent task allocation problems pervade a wide range of real-world applications, such as search and rescue in disaster manage- ment, 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 het- erogeneous 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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Published date: 3 May 2011
Additional Information: Event Dates: May 3, 2011
Venue - Dates: Fourth International Workshop on Optimisation in Multi-Agent Systems, Taiwan, Province of China, 2011-05-03
Organisations: Agents, Interactions & Complexity


Local EPrints ID: 272234
PURE UUID: c23f04db-7545-4250-9494-82a416fb3922
ORCID for Sarvapali Ramchurn: ORCID iD

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Date deposited: 28 Apr 2011 11:22
Last modified: 18 Jul 2017 06:33

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Author: Kathryn Macarthur
Author: Meritxell Vinyals
Author: Alessandro Farinelli
Author: Sarvapali Ramchurn ORCID iD
Author: Nick Jennings

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