Multi-agent simulation of collective self-consumption: impacts of storage systems and large-scale energy exchanges

2021 ◽  
pp. 111543
Author(s):  
Jérémy Albouys-Perrois ◽  
Nicolas Sabouret ◽  
Yvon Haradji ◽  
Mathieu Schumann ◽  
Benoit Charrier ◽  
...  
Keyword(s):  
Large Scale ◽  
Storage Systems ◽  
Collective Self ◽  
Agent Simulation ◽  
Multi Agent ◽  
Energy Exchanges

Performance of a Parallel Multi-Agent Simulation using Graphics Hardware

2014 ◽  
Vol 6 (4) ◽  
pp. 72-91
Author(s):  
Timothy W. C. Johnson ◽  
John R. Rankin
Keyword(s):  
Large Scale ◽  
Autonomous Agents ◽  
Graphics Hardware ◽  
Processing Unit ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Processing Power ◽  
Agent Simulation ◽  
Multi Agent ◽  
Graphical Processing

Large-scale Agent-Based Modelling and Simulation (ABMS) is a field of research that is becoming increasingly popular as researchers work to construct simulations at a higher level of complexity and realism than previously done. These systems can not only be difficult and time consuming to implement, but can also be constrained in their scope due to issues arising from a shortage of available processing power. This work simultaneously presents solutions to these two problems by demonstrating a model for ABMS that allows a developer to design their own simulation, which is then automatically converted into code capable of running on a mainstream Graphical Processing Unit (GPU). By harnessing the extra processing power afforded by the GPU this paper creates simulations that are capable of running in real-time with more autonomous agents than allowed by systems using traditional x86 processors.

HGHA: task allocation and path planning for warehouse agents

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yandong Liu ◽  
Dong Han ◽  
Lujia Wang ◽  
Cheng-Zhong Xu
Keyword(s):  
Path Planning ◽  
Task Allocation ◽  
Large Scale ◽  
Rapid Development ◽  
Hierarchical Architecture ◽  
Simulation Platform ◽  
Content Type ◽  
Agent Simulation ◽  
Multi Agent ◽  
Logistics Demand

Purpose With the rapid development of e-commerce, logistics demand is increasing day by day. The modern warehousing with a multi-agent system as the core comes into being. This paper aims to study the task allocation and path-planning (TAPP) problem as required by the multi-agent warehouse system. Design/methodology/approach The TAPP problem targets to minimize the makespan by allocating tasks to the agents and planning collision-free paths for the agents. This paper presents the Hierarchical Genetic Highways Algorithm (HGHA), a hierarchical algorithm combining optimization and multi-agent path-finding (MAPF). The top-level is the genetic algorithm (GA), allocating tasks to agents in an optimized way. The lower level is the so-called highways local repair (HLR) process, avoiding the collisions by local repairment if and only if conflicts arise. Findings Experiments demonstrate that HGHA performs faster and more efficient for the warehouse scenario than max multi-flow. This paper also applies HGHA to TAPP instances with a hundred agents and a thousand storage locations in a customized warehouse simulation platform with MultiBots. Originality/value This paper formulates the multi-agent warehousing distribution problem, TAPP. The HGHA based on hierarchical architecture solves the TAPP accurately and quickly. Verifying the HGHA by the large-scale multi-agent simulation platform MultiBots.

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