A product-driven system with an evolutionary algorithm to increase flexibility in planning a job shop

Patricio Sáez, Carlos Herrera, Camila Booth, Sana Belmokhtar-Berraf and Victor Parada

PLOS ONE, 2023, vol. 18, issue 2, 1-12

Abstract: The scheduling of a job shop production system occurs using models to plan operations for a given period while minimizing the makespan. However, since the resulting mathematical models are computationally demanding, their implementation in the work environment is impractical, a difficulty that increases as the scale problem grows. An alternative approach is to address the problem in a decentralized manner, such that real-time product flow information feeds the control system to minimize the makespan dynamically. Under the decentralized approach, we use a holonic and multiagent systems to represent a product-driven job shop system that allows us to simulate real-world scenarios. However, the computational performance of such systems to control the process in real-time and for different problem scales is unclear. This paper presents a product-driven job shop system model that includes an evolutionary algorithm to minimize the makespan. A multiagent system simulates the model and produces comparative results for different problem scales with classical models. One hundred two job shop problem instances classified as small, medium, and large scale are evaluated. The results suggest that a product-driven system produces near-optimal solutions in short periods and improves its performance as the scale of the problem increases. Furthermore, the computational performance observed during the experimentation suggests that such a system can be embedded in a real-time control process.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281807 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 81807&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0281807

DOI: 10.1371/journal.pone.0281807

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().