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

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 n...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of scheduling Ročník 23; číslo 3; s. 289 - 299
Hlavní autoři: Gerstl, Enrique, Mosheiov, Gur
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2020
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Business and Management
Calculus of Variations and Optimal Control; Optimization
Dynamic programming
Integer programming
Job shops
Operations Research/Decision Theory
Optimization
Polynomials
Scheduling
Supply Chain Management
NP-hardness
Scheduling
Generalized due dates
Branch-and-bound algorithm
Single machine
Heuristic
ISSN:1094-6136, 1099-1425
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1094-6136
1099-1425
DOI:10.1007/s10951-020-00638-7