An agent-based simulation model using decoupled learning rules to (re)schedule multiple projects
Competitive pressures and business globalization have led many organizations, mainly technology-based and innovation-oriented companies, to adopt project-based organizational structures. In a multi-project context within enterprise networks, reaching feasible solutions to the multi-project (re)sched...
Guardado en:
| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/63483 |
| Aporte de: |
| Sumario: | Competitive pressures and business globalization have led many organizations, mainly technology-based and innovation-oriented companies, to adopt project-based organizational structures. In a multi-project context within enterprise networks, reaching feasible solutions to the multi-project (re)scheduling problem represents a major challenge, where autonomy and decentralization of the environment favor agent-based simulation This work presents and validates a simulation-based multi-agent model using the fractal company concept to solve the complex multi-project (re)scheduling problem in enterprise networks. The proposed agent-based model is tested trough a set of project instances that vary in project structure, project parameters, number of resources shared, unplanned events that affect them, etc. Results obtained are assessed through different scheduling goals, such project total duration, project total cost, leveling resource usage, among others to show that decoupled learning rules allows finding a solution which can be understood as a Nash equilibrium for the interacting agents and it is far better compared to the ones obtained with existing approaches. |
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