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X-WR-CALNAME:Centre for Mathematical Sciences
X-ORIGINAL-URL:https://math-sciences.org
X-WR-CALDESC:Events for Centre for Mathematical Sciences
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TZOFFSETFROM:+0000
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DTSTART:20150101T000000
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DTSTART;TZID=UTC:20151118T140000
DTEND;TZID=UTC:20151118T150000
DTSTAMP:20230528T191038
CREATED:20151212T221536Z
LAST-MODIFIED:20151212T221924Z
UID:482-1447855200-1447858800@math-sciences.org
SUMMARY:Dr Simon Martin\, University of Stirling
DESCRIPTION:A Multi-agent System Embedding Online Tensor Learning for flow shopScheduling \nIn this study\, we introduce a multi-agent system for flow shop\nscheduling with a novel online tensor learning component which\nidentifies recurring patterns of elements and shares this information\nbetween agents to guide the search process. \nEach agent instantiates the same heuristic and starts the search in\nparallel from a different point in the space for solving the given flow\nshop scheduling problem instance. The inter-agent communication protocol\nenables collection of incumbent solutions forming a 3 dimensional (3rd\norder) tensor. The resultant matrix after factorisation of the tensor is\nused by each agent as a seed to form a starting solution from which the\nsearch process continues. \nThe tensor based multi-agent approach is evaluated using well-known flow\nshop scheduling benchmarks. The results show that the use of tensor\nanalysis improves the overall performance of the approach when compared\nto a variant using another technique. Moreover\, the proposed approach\nproves to be promising outperforming standard heuristics and matching\noverall performance when compared to the state-of-the-art.
URL:https://math-sciences.org/event/dr-simon-martin-university-of-stirling/
CATEGORIES:Applied Mathematics,Seminars
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