Workforce Distribution in Dynamic Multiagent Systems

David Millán-Ruiz

ISBN 10: 3639712951 / ISBN 13: 9783639712957

Published by Scholars' Press Apr 2014, 2014

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This work describes a novel approach to the problem of workforce distribution in dynamic multi-agent systems based on blackboard architectures, focusing especially on a real-world scenario: the multi-skill call centre. Traditionally, to address such highly-dynamic environments, diverse greedy heuristics have been applied to provide solutions in real-time. Basically, these heuristics perform a continuous re-planning on the system, taking into account its current state at all times. As decisions are greedily taken, the distribution of the workforce may be poor in the medium and/or long term. The usage of parallel memetic algorithms, which are more sophisticated than standard ad-hoc heuristics, can lead us towards much more accurate solutions. In order to effectively apply parallel memetic algorithms to such a dynamic environment, we introduce the concept of adaptive time window. Thus, the size of the time window depends upon the level of dynamism of the system at a given time. This research proposes a set of tools to automatically determine the dynamism of the system, as well as a novel and precise prediction module based on a neural network and a powerful optimization method. 252 pp. Englisch. Seller Inventory # 9783639712957

Bibliographic Details

Title: Workforce Distribution in Dynamic Multiagent...

Publisher: Scholars' Press Apr 2014

Publication Date: 2014

Binding: Taschenbuch

Condition: Neu

About this title

Synopsis:

This work describes a novel approach to the problem of workforce distribution in dynamic multi-agent systems based on blackboard architectures, focusing especially on a real-world scenario: the multi-skill call centre. Traditionally, to address such highly-dynamic environments, diverse greedy heuristics have been applied to provide solutions in real-time. Basically, these heuristics perform a continuous re-planning on the system, taking into account its current state at all times. As decisions are greedily taken, the distribution of the workforce may be poor in the medium and/or long term. The usage of parallel memetic algorithms, which are more sophisticated than standard ad-hoc heuristics, can lead us towards much more accurate solutions. In order to effectively apply parallel memetic algorithms to such a dynamic environment, we introduce the concept of adaptive time window. Thus, the size of the time window depends upon the level of dynamism of the system at a given time. This research proposes a set of tools to automatically determine the dynamism of the system, as well as a novel and precise prediction module based on a neural network and a powerful optimization method.

About the Author:

Dr. David Millan is a senior product director, an IT strategist and a chief data scientist with 10 years of international experience in data science, BI, innovation and applied research. He plays different roles: CSO&VP Data Science at Pragsis, Co-Founder at Bidoop, Research Assistant at Complutense University of Madrid, Associate Lecturer at UTad.