Issue |
RAIRO-Oper. Res.
Volume 58, Number 5, September-October 2024
|
|
---|---|---|
Page(s) | 4589 - 4605 | |
DOI | https://doi.org/10.1051/ro/2024173 | |
Published online | 21 October 2024 |
A job scheduling and rejection problem considering self-contained and cross-functional jobs
1
Department of Intelligent Technology and Application, Hungkuang University, Taichung, Taiwan, R.O.C
2
Department of Information Management, National Taichung University of Science and Technology, No. 129, Section 3, Sanmin Road, North District, Taichung 404336, Taiwan, R.O.C
* Corresponding author: jywang@nutc.edu.tw
Received:
10
April
2024
Accepted:
30
August
2024
In today’s large projects and complex assembly lines, a single multi-skilled worker often needs to complete jobs requiring multiple areas of expertise. Even if a worker possesses all necessary skills, their proficiency can vary. This variability makes it challenging to assess a job’s cost-performance ratio before assignment. Larger problem sizes often involve many such jobs that need scheduling or rejection. Clearly, the processing times and workers in the presented problem are more complex than traditional scheduling problems with single-valued processing times and single-functional machines. Two important observations serve as the motivation. First, traditional genetic algorithms with fixed-length chromosomes may not effectively handle the complexity of self-contained and cross-functional jobs and multi-skilled workers. Second, traditional genetic algorithms cannot guarantee a certain level of solution quality. Motivated by these observations, a novel genetic algorithm is developed. This algorithm can quickly search the solution space using an outbreeding technique. Additionally, an upper bound is provided to ensure solution quality. Experimental results demonstrate that the proposed genetic algorithm is superior to others through comprehensive comparisons.
Mathematics Subject Classification: 68W50 / 68M20 / 68W25 / 90B40
Key words: Job scheduling / parallel machine scheduling / maximization problem / genetic algorithm / biodiversity
© The authors. Published by EDP Sciences, ROADEF, SMAI 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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