Journal
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
Volume 64, Issue 1, Pages 147-156Publisher
PALGRAVE MACMILLAN LTD
DOI: 10.1057/jors.2012.46
Keywords
scheduling; truncated learning function; single-machine; flowshop
Funding
- NSC [NSC 99-2221-E-035-057-MY3]
- Natural Science Foundation for Young Scholars of Jiangxi, China [2010GQS0003]
- Science Foundation of Education Committee for Young Scholars of Jiangxi, China [GJJ11143]
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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. Journal of the Operational Research Society (2013) 64, 147-156. doi:10.1057/jors.2012.46 Published online 18 April 2012
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