Towards a data-driven predictive-reactive production scheduling approach based on inventory availability
| Autor: | Renata Mariani Zanella, Enzo Morosini Frazzon, Satie Ledoux Takeda Berger |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Schedule Computer science 020208 electrical & electronic engineering Scheduling (production processes) 02 engineering and technology Raw material Work in process Industrial engineering Data-driven 020901 industrial engineering & automation Control and Systems Engineering Software deployment Anticipation (artificial intelligence) 0202 electrical engineering electronic engineering information engineering Production (economics) State (computer science) |
| Zdroj: | IFAC-PapersOnLine. 52:1343-1348 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2019.11.385 |
| Popis: | To survive in a competitive business environment, manufacturing systems require the proper deployment of advanced technologies coming from Industry 4.0. These technologies allow access to quasi-real-time data that provide a continuously updated picture of the production system, including the state of available inventory. Data-driven predictive-reactive production scheduling has the potential to support the anticipation and prompt reaction to overcome different kinds of disruptions that occur in production execution nowadays. This research paper aims to propose a conceptual model for a data-driven predictive-reactive production scheduling approach combining machine learning and simulation-based optimization, considering current inventory of raw material, work in process and final products inventory to characterize a job-shop production execution state. The approach supports decision-making in dynamic situations related to inventory availability that can affect production schedules. |
| Databáze: | OpenAIRE |
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