Authors:
Max Gath
;
Otthein Herzog
and
Maximilian Vaske
Affiliation:
Institute Institute for Artificial Intelligence, Germany
Keyword(s):
Multiagent-based Simulation, Shortest-path Algorithms, PlaSMA, JADE, Planning and Scheduling, Autonomous Logistic Processes.
Related
Ontology
Subjects/Areas/Topics:
Agent Models and Architectures
;
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bioinformatics
;
Biomedical Engineering
;
Cooperation and Coordination
;
Distributed and Mobile Software Systems
;
Distributed Problem Solving
;
Enterprise Information Systems
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Information Systems Analysis and Specification
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Methodologies and Technologies
;
Multi-Agent Systems
;
Negotiation and Interaction Protocols
;
Operational Research
;
Planning and Scheduling
;
Simulation
;
Simulation and Modeling
;
Software Engineering
;
Symbolic Systems
Abstract:
The goods structure effect increases the complexity and dynamics of logistic processes. To handle the resulting challenges and requirements, planning and controlling of logistic processes have to be reliable and adaptive. Especially in these dynamic environments, Multiagent-Based Simulation (MABS) is a suitable approach to support decision makers in order to evaluate the companies' processes and to identify optimal decisions. This paper presents the PlaSMA multiagent simulation platform, which has been developed for the evaluation of logistics scenarios and strategic analyses. As shortest-path searches are an essential but cost intensive part of the agents for the simulation of transport processes, we focus on the parallel application of a state-of-the-art Hub Labeling algorithm, which is combined with Contraction Hierarchies. The results show, that the optimal number of concurrently running routing agents is restricted by available cores and/or the number of agents running physicall
y concurrently. Moreover, by slightly restricting the agents' autonomy a significant increase in runtime performance can be achieved without losing the advantages of agent-based simulations. This allows to simulate large real-world transport scenarios with MABS and low hardware requirements.
(More)