DocumentCode :
2830504
Title :
Agent-based Pickup and Delivery Planning: The Learnable Evolution Model Approach
Author :
Wojtusiak, Janusz ; Warden, Tobias ; Herzog, Otthein
Author_Institution :
Machine Learning & Inference Lab., George Mason Univ., Fairfax, VA, USA
fYear :
2011
fDate :
June 30 2011-July 2 2011
Firstpage :
1
Lastpage :
8
Abstract :
The Dynamic Vehicle Routing Problem (DVRP) is an optimization problem in which agents deliver orders that are not known in advance to the routing. Partial solutions need to be adapted to continuously accommodate new orders within dynamically changing conditions. This research focuses on using a combination of multiagent-based autonomous control with non-Darwinian evolutionary optimization. In order to compile transport plans and render optimized decisions agents managing transport vehicles employ a guided evolutionary computation method, called the learnable evolution model (LEM). Implementation and experimental evaluation of the method is performed within the Plasma multiagent simulation platform.
Keywords :
evolutionary computation; logistics; DVRP; agent-based pickup planning; delivery planning; dynamic vehicle routing problem; learnable evolution model approach; logistic network; multiagent based autonomous control; non-Darwinian evolutionary optimization; partial solution; Containers; Cost accounting; Equations; Logistics; Optimization; Planning; Skeleton; Autono-mous Logistics; Evolutionary Computation; Learnable Evolution Model; Multiagent-based Simulation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Complex, Intelligent and Software Intensive Systems (CISIS), 2011 International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-61284-709-2
Electronic_ISBN :
978-0-7695-4373-4
Type :
conf
DOI :
10.1109/CISIS.2011.11
Filename :
5989011
Link To Document :
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