Real-Time Production Scheduling and Industrial Sonar and Their Application in Autonomous Mobile Robots

Burillo, Francisco ; Lambán, María-Pilar (Universidad de Zaragoza) ; Royo, Jesús-Antonio (Universidad de Zaragoza) ; Morella, Paula ; Sánchez, Juan-Carlos

Resumen: In real-time production planning, there are exceptional events that can cause problems and deviations in the production schedule. These circumstances can be solved with real-time production planning, which is able to quickly reschedule the operations at each work centre. Mobile autonomous robots are a key element in this real-time planning and are a fundamental link between production centres. Work centres in Industry 4.0 environments can use current technology, i.e., a biomimetic strategy that emulates echolocation, with the aim of establishing bidirectional communication with other work centres through the application of agile algorithms. Taking advantage of these communication capabilities, the basic idea is to distribute the execution of the algorithm among different work centres that interact like a parasympathetic system that makes automatic movements to reorder the production schedule. The aim is to use algorithms with an optimal solution based on the simplicity of the task distribution, trying to avoid heuristic algorithms or heavy computations. This paper presents the following result: the development of an Industrial Sonar algorithm which allows real-time scheduling and obtains the optimal solution at all times. The objective of this is to reduce the makespan, reduce energy costs and carbon footprint, and reduce the waiting and transport times for autonomous mobile robots using the Internet of Things, cloud computing and machine learning technologies to emulate echolocation.

Idioma: Inglés

DOI: 10.3390/app14051890

Año: 2024

Publicado en: Applied Sciences (Switzerland) 14, 5 (2024), 1890 [16 pp.]
ISSN: 2076-3417

Factor impacto JCR: 2.5 (2024)

Categ. JCR: ENGINEERING, MULTIDISCIPLINARY rank: 50 / 175 = 0.286 (2024) - Q2 - T1
Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 123 / 239 = 0.515 (2024) - Q3 - T2
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 283 / 460 = 0.615 (2024) - Q3 - T2
Categ. JCR: PHYSICS, APPLIED rank: 101 / 187 = 0.54 (2024) - Q3 - T2

Factor impacto SCIMAGO: 0.521 - Engineering (miscellaneous) (Q2) - Computer Science Applications (Q2) - Process Chemistry and Technology (Q2) - Instrumentation (Q2) - Materials Science (miscellaneous) (Q2) - Fluid Flow and Transfer Processes (Q2)

Tipo y forma: Article (Published version)

Área (Departamento): Área Ing. Procesos Fabricación (Dpto. Ingeniería Diseño Fabri.)