Scheduling with step learning and job rejection Scheduling with step learning and job rejection

This paper focuses on job scheduling with step learning and job rejection. The step learning model aims to reduce the processing time for jobs starting after a specific learning date. Our objective is to minimize the sum of the maximum completion time of accepted jobs and the total rejection penalty...

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Veröffentlicht in:Operational research Jg. 25; H. 1; S. 6
Hauptverfasser: Song, Jiaxin, Miao, Cuixia, Kong, Fanyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2025
Springer Nature B.V
Schlagworte:
Algorithms
Approximation
Business and Management
Completion time
Computational Intelligence
Fines & penalties
Learning
Management Science
Operations Research
Operations Research/Decision Theory
Original Paper
Polynomials
Rejection
Scheduling
Fully polynomial-time approximation scheme
Scheduling
Step learning
Approximation algorithm
Rejection penalty
Pseudo-polynomial time algorithm
ISSN:1109-2858, 1866-1505
Online-Zugang:Volltext
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Zusammenfassung:This paper focuses on job scheduling with step learning and job rejection. The step learning model aims to reduce the processing time for jobs starting after a specific learning date. Our objective is to minimize the sum of the maximum completion time of accepted jobs and the total rejection penalty of rejected jobs. We examine special cases of processing times for both single-machine and parallel-machine scenarios. For the former, we design a pseudo-polynomial time algorithm, a 2-approximation algorithm and a fully polynomial-time approximation scheme (FPTAS) based on data rounding. For the latter, we present a fully polynomial-time approximation scheme achieved by trimming the state space. Additionally, for the general case of the single-machine problem, we propose a pseudo-polynomial time algorithm.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1109-2858
1866-1505
DOI:10.1007/s12351-024-00887-w