Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33871
Title: Scheduling with a position-weighted learning effect
Authors: Cheng, TCE 
Kuo, WH
Yang, DL
Keywords: Learning effect
Makespan
Scheduling
Total completion time
Total tardiness
Issue Date: 2014
Source: Optimization letters, 2014, v. 8, no. 1, p. 293-306 How to cite?
Journal: Optimization Letters 
Abstract: In general, human learning takes time to build up, which results from a worker gaining experience from repeating similar operations over time. In the early stage of processing a given set of similar jobs, a worker is not familiar with the operations, so his learning effect on the jobs scheduled early is not apparent. On the other hand, when the worker has gained experience in processing the jobs his learning improves. So a worker's learning effect on a job depends not only on the total processing time of the jobs that he has processed but also on the job position. In this paper we introduce a position-weighted learning effect model for scheduling problems. We provide optimal solutions for the single-machine problems to minimize the makespan and the total completion time, and an optimal solution for the single-machine problem to minimize the total tardiness under an agreeable situation. We also consider two special cases of the flowshop problem.
URI: http://hdl.handle.net/10397/33871
ISSN: 1862-4472
DOI: 10.1007/s11590-012-0574-5
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