A note on a makespan minimization problem with a multi-ability learning effect

In the scheduling literature the learning effect is perceived as a process of acquiring experience by a processor (e.g. a human worker) in one ability. However, in many real-life problems the processor, during execution of jobs, increases its experience in different, very often independent, abilitie...

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Bibliographic Details
Published in:Omega (Oxford) 2010-06, Vol.38 (3), p.213-217
Main Authors: Janiak, Adam, Rudek, RadosŁaw
Format: Article
Language:English
Subjects:
Applied sciences
Computational complexity
Exact sciences and technology
Inventory control, production control. Distribution
Job shops
Learning effect
Operational research and scientific management
Operational research. Management science
Scheduling
Scheduling algorithms
Scheduling Learning effect Single machine Computational complexity
Single machine
Studies
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Summary:In the scheduling literature the learning effect is perceived as a process of acquiring experience by a processor (e.g. a human worker) in one ability. However, in many real-life problems the processor, during execution of jobs, increases its experience in different, very often independent, abilities (skills). In consequence, it causes the overall growth of the efficiency of the processor. According to this observation, in this paper, we bring into scheduling a new approach called multi-ability learning that generalizes the existing ones and models more precisely real-life settings. On this basis, we focus on a makespan minimization problem with the proposed learning model and provide optimal polynomial time algorithms for its special cases, which often occur in management.
ISSN:0305-0483
1873-5274
DOI:10.1016/j.omega.2009.09.004