133235 20260217205444.0 doi 10.3390/app14051890 sideral 137945 ART-2024-137945 eng Burillo, Francisco Real-Time Production Scheduling and Industrial Sonar and Their Application in Autonomous Mobile Robots 2024 Access copy available to the general public Unrestricted 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. info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 2.5 2024 ENGINEERING, MULTIDISCIPLINARY 50 / 179 = 0.279 2024 Q2 T1 CHEMISTRY, MULTIDISCIPLINARY 123 / 239 = 0.515 2024 Q3 T2 MATERIALS SCIENCE, MULTIDISCIPLINARY 284 / 461 = 0.616 2024 Q3 T2 PHYSICS, APPLIED 101 / 187 = 0.54 2024 Q3 T2 0.521 2024 Computer Science Applications 2024 Q2 Engineering (miscellaneous) 2024 Q2 Process Chemistry and Technology 2024 Q2 Instrumentation 2024 Q2 Materials Science (miscellaneous) 2024 Q2 Fluid Flow and Transfer Processes 2024 Q2 5.5 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Lambán, María-Pilar Universidad de Zaragoza (orcid)0000-0003-1401-6495 Royo, Jesús-Antonio Universidad de Zaragoza (orcid)0000-0002-0692-5982 Morella, Paula Sánchez, Juan-Carlos 5002 515 Universidad de Zaragoza Dpto. Ingeniería Diseño Fabri. Área Ing. Procesos Fabricación 14, 5 (2024), 1890 [16 pp.] Appl. sci. Applied Sciences (Switzerland) 2076-3417 850245 http://zaguan.unizar.es/record/133235/files/texto_completo.pdf Versión publicada 2659347 http://zaguan.unizar.es/record/133235/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:133235 articulos driver 2026-02-17-20:17:27 ARTICLE