Agent-based dynamic scheduling model for product-driven production

• Autor(a) principal: Campos, João Thiago de G. A. A.
• Data de Publicação: 2020
• Outros Autores: Blumelova, Jana, Lepikson, Herman Augusto, Mendonça Freires, Francisco Gaudencio
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Operations & Production Management (Online)
• Texto Completo: https://bjopm.org.br/bjopm/article/view/1075
• topic: Product-Driven Production; Dynamic Scheduling; Agent-Based Production Control; Flexible Job Shop Scheduling; Agent-Based Modelling

Goal: This research provides specific solution for dynamic scheduling of product-driven production with unique level of detail and original architecture. Design / Methodology / Approach: Design process of scheduling problem-solving MAS is divided into three steps: agent encapsulation of entities participating in scheduling, including concept of agents and responsibilities they assume, system architecture and topology of the agents network, detailed design of decision scheme of individual agents. Results: Production processes take place in dynamic environment and have to react to numerous real-time events, hence reschedule the production by a new design and implement agent-based model in order to solve dynamic flexible job shop scheduling problem in product-driven production environment. Limitations of the investigation: Designed model counts with simple agents behaving on condition-action rules. These agents could be replaced by more sophisticated types of agents such as utility-based or learning agents. Also the implemented coordination mechanism ensuring global view on the scheduling problem is rather simple. Practical implications: Via simulations of realistic production scenarios it was expected to prove an applicability of specific solution of how bringing the intelligence to the lower levels of control system may be used in dynamic scheduling. Based on elaboration of theoretical basis, principles were identified as suitable or widely used in design of such model. Originality / Value: This research provides specific real-time architecture for a multi-agents dynamic scheduling of product-driven production with unique level of detail and scenarios analysis.