Coupled task scheduling with time-dependent processing times

Mostafa Khatami () and Amir Salehipour ()
Additional contact information
Mostafa Khatami: University of Technology Sydney
Amir Salehipour: University of Technology Sydney

Journal of Scheduling, 2021, vol. 24, issue 2, No 6, 223-236

Abstract: Abstract The single machine coupled task scheduling problem includes a set of jobs, each with two separated tasks, and there is an exact delay between the tasks. We investigate the single machine coupled task scheduling problem with the objective of minimizing the makespan under identical processing time for the first task and identical delay period for all jobs, and the time-dependent processing time setting for the second task. Certain healthcare appointment scheduling problems can be modeled as the coupled task scheduling problem. Also, the incorporation of time-dependent processing time for the second task lets the human resource fatigue and the deteriorating health conditions be modeled. We provide optimal solution under certain conditions. In addition, we propose a dynamic program under the condition that the majority of jobs share the same time-dependent characteristic. We develop a heuristic for the general case and show that the heuristic performs well.

Keywords: Coupled task scheduling; Time-dependent processing time; Simple linear processing time; Dynamic program; Heuristic; Healthcare scheduling (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10951-020-00675-2 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:jsched:v:24:y:2021:i:2:d:10.1007_s10951-020-00675-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10951

DOI: 10.1007/s10951-020-00675-2

Access Statistics for this article

Journal of Scheduling is currently edited by Edmund Burke and Michael Pinedo

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