Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling

In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. Thus, the multi-project (re)scheduling must achieve the most efficient possible resource usage withou...

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Detalles Bibliográficos
Autores principales:	Tosselli, Laura, Bogado, Verónica S., Martínez, Ernesto
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2018
Materias:	Ciencias Informáticas agent-based simulation multi-agent system multi-project (re)scheduling project-oriented fractal organization resource leveling nivelación de recursos organización fractal orientada a proyectos (re)scheduling de múltiples proyectos simulación basada en agentes sistema multi-agente
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/70117
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-70117
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas agent-based simulation multi-agent system multi-project (re)scheduling project-oriented fractal organization resource leveling nivelación de recursos organización fractal orientada a proyectos (re)scheduling de múltiples proyectos simulación basada en agentes sistema multi-agente
spellingShingle	Ciencias Informáticas agent-based simulation multi-agent system multi-project (re)scheduling project-oriented fractal organization resource leveling nivelación de recursos organización fractal orientada a proyectos (re)scheduling de múltiples proyectos simulación basada en agentes sistema multi-agente Tosselli, Laura Bogado, Verónica S. Martínez, Ernesto Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
topic_facet	Ciencias Informáticas agent-based simulation multi-agent system multi-project (re)scheduling project-oriented fractal organization resource leveling nivelación de recursos organización fractal orientada a proyectos (re)scheduling de múltiples proyectos simulación basada en agentes sistema multi-agente
description	In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. Thus, the multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem (RIP) is extended to incorporate indicators on agents’ payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.
format	Articulo Articulo
author	Tosselli, Laura Bogado, Verónica S. Martínez, Ernesto
author_facet	Tosselli, Laura Bogado, Verónica S. Martínez, Ernesto
author_sort	Tosselli, Laura
title	Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
title_short	Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
title_full	Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
title_fullStr	Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
title_full_unstemmed	Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
title_sort	multi-agent learning by trial and error for resource leveling during multi-project (re)scheduling
publishDate	2018
url	http://sedici.unlp.edu.ar/handle/10915/70117
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bdutipo_str	Repositorios
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Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling

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