Vol. 15 No. 2 (2024)
Original Research Article

Comparing sequential and integrated models in Reconfigurable Manufacturing Systems optimization

Isabel Barros Garcia
https://orcid.org/0000-0003-1148-3522 (unauthenticated) Université de Technologie de Compiègne, Roberval, CS 60319, Compiègne, 60203, France
Joanna Daaboul
https://orcid.org/0000-0001-5822-7038 (unauthenticated) Université de Technologie de Compiègne, Roberval, CS 60319, Compiègne, 60203, France
Antoine Jouglet
https://orcid.org/0000-0001-9251-249X (unauthenticated) Université de Technologie de Compiègne, Heudiasyc, UMR CNRS 7253, CS 60319, Compiègne, 60203, France
Julien Le Duigou
https://orcid.org/0000-0001-8723-2506 (unauthenticated) Université de Technologie de Compiègne, Roberval, CS 60319, Compiègne, 60203, France

Published 2024-06-11

Keywords

  • Reconfigurable Manufacturing Systems,
  • Process Planning,
  • Layout,
  • Scheduling,
  • Optimization

Abstract

Consumers are increasingly seeking products with a higher degree of customization, and this has intensified the search for more flexible production systems. Unexpected phenomena, including those impacting the economy and public health, have shown the need for high reactivity by manufacturers. Reconfigurable Manufacturing Systems are one of the means to respond to new market needs. They are designed to be able to quickly change their structure and thus adapt their functionalities and capabilities. A central question with regard to these types of systems is when and how they should be reconfigured. It is, however, a question that needs to be addressed simultaneously alongside the classic resource allocation and scheduling problems, since the problems are interdependent. This paper proposes four models that address the problem sequentially, partially sequentially, and integrally. Our numerical analysis demonstrates the advantage and importance of treating these problems in an integrated manner. In addition, we provide and compare three exact methods for the integrated process planning, layout, and scheduling optimization problems. 

Article history: Received (December 21, 2023); Revised (May 13, 2024); Accepted (May 20, 2024); Published online (May 31, 2024)