Journal
COMPUTERS & INDUSTRIAL ENGINEERING
Volume 60, Issue 4, Pages 790-795Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2011.01.016
Keywords
Scheduling; Single-machine; Truncation learning effect
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 sum-of processing-times-based learning in which the actual processing time of a job is a non-increasing function of the jobs already processed. However, the actual processing time of a given job drops to zero precipitously when the normal job processing times are large. Motivated by this observation, we propose a truncation learning model where the actual job processing time is a function which depends not only on the processing times of the jobs already processed but also on a control parameter. The use of the truncated function is to model the phenomenon that the learning of a human activity is limited. Under the proposed learning model, we show that some single-machine scheduling problems can be solved in polynomial time. In addition, we further provide the worst-case error bounds for the problems to minimize the maximum lateness and total weighted completion time. (C) 2011 Elsevier Ltd. All rights reserved.
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