Parallel memetic approach to the problem of workforce distribution in dynamic multi-agent systems

This line is focused on a novel approach to workforce distribution in dynamic multi-agent systems based on backboard architectures. These environments entail quick adaptations to a changing environment that only fast greedy heuristics can handle. These greedy heuristics consist of a continuous re-planning, considering the current state of the system. As these decisions are greedily taken, the workforce distribution may be poor for middle and/or long term planning due to incessant wrong movements. The use of parallel memetic algorithms, which are more complex than classical, ad-hoc heuristics, can guide us towards more accurate solutions. In order to apply parallel memetic algorithms to such a dynamic environment, we propose a reformulation of the traditional problem, which combines predictions of future situations with a precise search mechanism, by enlarging or diminishing the time-frame considered. The size of the time-frame depends upon the dynamism of the system (smaller when there is high dynamism and larger when there is low dynamism). This work attempts to demonstrate how nearly optimal solutions each v seconds (size of the time-frame) can outperform continuous bad distributions when the right size of the time-frame is determined, and predictions and optimisations are properly carried out. Specifically, we propose a neural network for predicting future system variables and a parallel memetic algorithm to perform the assignment of incoming tasks to the right agents, which outperforms other conventional approaches. Additionally, we propose a modification of the resilient back-propagation algorithm and evolutionary operators based on meta-heuristics. To conclude, we will test out our method on a real-world production environment from Telefónica which is a large multinational telephone operator.

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parallel_memetic.txt · Last modified: 2015/01/05 19:43 by J. Ignacio Hidalgo