Dynamic models for the control of job shop production systems

• Grant number: 19/12023-1
• Support type: Regular Research Grants
• Duration: September 01, 2019 - July 31, 2022
• Field of knowledge: Engineering - Production Engineering - Production Management
• Principal researcher: Juliana Keiko Sagawa
• Grantee: Juliana Keiko Sagawa
• Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Assoc. researchers: Gasper Music ; Marcelo Seido Nagano ; Roberto Fernandes Tavares Neto ; Tobias Sprodowski

Abstract

Time and flexibility have become important competitive factors. In order to gain competitive advantage from these factors, the companies' management and control systems must be able to cope with uncertainties and dynamic conditions. Nevertheless, several models developed for Production Planning and Control rely on averages and stationary conditions. A closed-loop dynamic model for production control of multiple products was developed by the coordinator of this project. The applied modeling methodology, based on Bond Graphs and Control Theory, allows several possibilities of improvement. In order to extend this body of knowledge, the present project proposes the following extensions/improvements: the implementation of different controllers to the model, aiming to improve the control performance; the modeling of the elements of the system as agents for the implementation of Model Predictive Control (MPC); the inclusion of other relevant variables, such as the set up times of the machines. The originality of the model under development motivates the continuity of this research. In addition, the review of recent international literature in the field shows that an increasing amount of dynamic models have been developed, especially for the analysis of production-inventory systems and Supply Chain dynamics. However, the use of Dynamic Modeling and Control Theory to solve Production Management problems in Brazil is still very incipient. This research aims to contribute for the development this field. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

SAGAWA, JULIANA KEIKO; NAGANO, MARCELO SEIDO. Integration, uncertainty and information: how do they affect planning performance?. REGE-REVISTA DE GESTAO, JAN 2021. Web of Science Citations: 0.

SPRODOWSKI, TOBIAS; SAGAWA, JULIANA KEIKO; MALUF, ARTHUR SARRO; FREITAG, MICHAEL; PANNEK, JURGEN. A multi-product job shop scenario utilising Model Predictive Control. EXPERT SYSTEMS WITH APPLICATIONS, v. 162, DEC 30 2020. Web of Science Citations: 0.