Single-machine and two-machine flowshop scheduling problems with truncated position-based learning functions
Scheduling with learning effects has received growing attention nowadays. A well-known learning model is called ‘position-based learning’ in which the actual processing time of a job is a non-increasing function of its position to be processed. However, the actual processing time of a given job drops to zero precipitously as the number of jobs increases. Motivated by this observation, we propose two truncated learning models in single-machine scheduling problems and two-machine flowshop scheduling problems with ordered job processing times, respectively, where the actual processing time of a job is a function of its position and a control parameter. Under the proposed learning models, we show that some scheduling problems can be solved in polynomial time. In addition, we further analyse the worst-case error bounds for the problems to minimize the total weighted completion time, discounted total weighted completion time and maximum lateness.
Year of publication: |
2013
|
---|---|
Authors: | Wu, C-C ; Yin, Y ; Cheng, S-R |
Published in: |
Journal of the Operational Research Society. - Palgrave Macmillan, ISSN 0160-5682. - Vol. 64.2013, 1, p. 147-156
|
Publisher: |
Palgrave Macmillan |
Saved in:
Saved in favorites
Similar items by person
-
Yin, Y, (2014)
-
Wu, C-C, (2013)
-
Single-machine scheduling of proportionally deteriorating jobs by two agents
Gawiejnowicz, S, (2011)
- More ...