EconPapers    
Economics at your fingertips  
 

Min–max relative regret for scheduling to minimize maximum lateness

Imad Assayakh (), Imed Kacem () and Giorgio Lucarelli ()
Additional contact information
Imad Assayakh: Université de Lorraine
Imed Kacem: Université de Lorraine
Giorgio Lucarelli: Université de Lorraine

Annals of Operations Research, 2025, vol. 351, issue 1, No 28, 778 pages

Abstract: Abstract We study the single machine scheduling problem under uncertain parameters, with the aim of minimizing the maximum lateness. More precisely, the processing times, the release dates, and the delivery times of the jobs are uncertain, but an upper and a lower bound of these parameters are known in advance. Our objective is to find a robust solution, which minimizes the maximum relative regret. In other words, we search for a solution which, among all possible realizations of the parameters, minimizes the worst-case ratio of the deviation between its objective and the objective of an optimal solution over the latter one. Two variants of this problem are considered. In the first variant, the release date of each job is equal to 0. In the second one, all jobs are of unit processing time. Moreover, we also consider the min–max regret version of the second variant. In all cases, we are interested in the sub-problem of maximizing the (relative) regret of a given scheduling sequence. The studied problems are shown to be polynomially solvable.

Keywords: Scheduling; Maximum lateness; Min–max relative regret; Interval uncertainty (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-024-06122-1 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:351:y:2025:i:1:d:10.1007_s10479-024-06122-1

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

DOI: 10.1007/s10479-024-06122-1

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 ().

 
Page updated 2025-08-02
Handle: RePEc:spr:annopr:v:351:y:2025:i:1:d:10.1007_s10479-024-06122-1