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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| Published in: | Operational research Vol. 25; no. 1; p. 6 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.03.2025
Springer Nature B.V |
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| ISSN: | 1109-2858, 1866-1505 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 6 |
| Author | Kong, Fanyu Song, Jiaxin Miao, Cuixia |
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| Keywords | Fully polynomial-time approximation scheme Scheduling Step learning Approximation algorithm Rejection penalty Pseudo-polynomial time algorithm |
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| SubjectTerms | 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 |
| Subtitle | Scheduling with step learning and job rejection |
| Title | Scheduling with step learning and job rejection |
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