Minimizing the expected maximum lateness for a job shop subject to stochastic machine breakdowns

Gabriel Mauricio Zambrano-Rey, Eliana María González-Neira (), Gabriel Fernando Forero-Ortiz, María José Ocampo-Monsalve and Andrea Rivera-Torres
Additional contact information
Gabriel Mauricio Zambrano-Rey: Pontificia Universidad Javeriana
Eliana María González-Neira: Pontificia Universidad Javeriana
Gabriel Fernando Forero-Ortiz: Pontificia Universidad Javeriana
María José Ocampo-Monsalve: Pontificia Universidad Javeriana
Andrea Rivera-Torres: Pontificia Universidad Javeriana

Annals of Operations Research, 2024, vol. 338, issue 1, No 28, 833 pages

Abstract: Abstract This paper addresses a stochastic job shop scheduling problem with sequence-dependent setup times, aiming to minimize the expected maximum lateness. The stochastic nature is modeled by considering uncertain times between failures (TBF) and uncertain times to repair (TTR). To tackle this problem, a simheuristic approach is proposed, which combines a tabu search (TS) algorithm with Monte Carlo simulation. A total of 320 instances were used to conduct multiple experiments. Instances were generated with two distributions to study the behavior of stochastic TTR and TBF under log-normal and exponential distributions. Firstly, the performance of the simheuristic was evaluated for small instances by comparing it with the simulation of optimal solutions obtained with a mixed-integer linear programming (MILP) model. The simheuristic approach demonstrated an average improvement of around 7% compared to the simulation of MILP model solutions. Secondly, the simheuristic performance was evaluated for medium and large-size instances by comparing it with the simulation of the solutions obtained by the earliest due date (EDD) and process time plus work in the next queue plus negative slack (PT + WINQ + SL) dispatching rules. The results showed an average improvement of around 11% compared to EDD and 14% compared to PT + WINQ + SL. Furthermore, the results highlight that even when the two distributions have the same expected value and coefficient of variation, they can yield different expected maximum lateness values. This emphasizes the importance of precise distribution fitting when solving real cases to achieve effective scheduling performance.

Keywords: Stochastic job shop scheduling; Maximum lateness; Machine breakdowns; Simheuristics; Tabu search (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-023-05592-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:annopr:v:338:y:2024:i:1:d:10.1007_s10479-023-05592-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-023-05592-z

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().