Single machine scheduling to maximize the number of on-time jobs with generalized due-dates

Enrique Gerstl and Gur Mosheiov ()
Additional contact information
Enrique Gerstl: The Hebrew University
Gur Mosheiov: The Hebrew University

Journal of Scheduling, 2020, vol. 23, issue 3, No 1, 289-299

Abstract: Abstract In scheduling problems with generalized due dates (gdd), the due dates are specified according to their position in the sequence, and the j-th due date is assigned to the job in the j-th position. We study a single-machine problem with generalized due dates, where the objective is maximizing the number of jobs completed exactly on time. We prove that the problem is NP-hard in the strong sense. To our knowledge, this is the only example of a scheduling problem where the job-specific version has a polynomial-time solution, and the gdd version is strongly NP-hard. A branch-and-bound (B&B) algorithm, an integer programming (IP)-based procedure, and an efficient heuristic are introduced and tested. Both the B&B algorithm and the IP-based solution procedure can solve most medium-sized problems in a reasonable computational effort. The heuristic serves as the initial step of the B&B algorithm and in itself obtains the optimum in most cases. We also study two special cases: max-on-time for a given job sequence and max-on-time with unit-execution-time jobs. For both cases, polynomial-time dynamic programming algorithms are introduced, and large-sized problems are easily solved.

Keywords: Scheduling; Single machine; Generalized due dates; NP-hardness; Heuristic; Branch-and-bound algorithm (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s10951-020-00638-7 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:23:y:2020:i:3:d:10.1007_s10951-020-00638-7

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

DOI: 10.1007/s10951-020-00638-7

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 ().