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