A simheuristic approach using the NSGA-II to solve a bi-objective stochastic flexible job shop problem

Camilo Andrés Rodríguez-Espinosa, Eliana María González-Neira and Gabriel Mauricio Zambrano-Rey

Journal of Simulation, 2024, vol. 18, issue 4, 646-670

Abstract: This paper addresses a bi-objective problem in flexible job shop scheduling (FJSS) with stochastic processing times. Following the Just-In-Time philosophy, the first objective is to minimise deterministic Earliness+Tardiness, and the second objective is to minimise the Earliness+Tardiness Risk. The second objective function seeks to obtain robust solutions under uncertain environments. The proposed approach is a simheuristic that hybridises the non-dominated sorting genetic algorithm (NSGA-II) with Monte Carlo simulation to obtain the Pareto frontier of both objectives. The computational results demonstrate the effectiveness of the proposed algorithm under different variability environments.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/17477778.2023.2231877 (text/html)
Access to full text is restricted to subscribers.

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:taf:tjsmxx:v:18:y:2024:i:4:p:646-670

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjsm20

DOI: 10.1080/17477778.2023.2231877

Access Statistics for this article

Journal of Simulation is currently edited by Christine Currie

More articles in Journal of Simulation from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().