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Machine scheduling problems with a general learning effect. (English) Zbl 1190.90076

Summary: Recently, learning effects in scheduling problems have received growing attention. The position-based learning model seems to be a realistic assumption for the case where the actual processing of the job is mainly machine driven. In this paper, we consider the sum-of-processing-time-based learning model. We propose a learning model which considers both the machine and human learning effects, simultaneously. We first show that the position-based learning and the sum-of-processing-time-based learning models in the literature are special cases of the proposed model. Moreover, we present the solution procedures for some single-machine and some flowshop problems. Editorial remark: A counterexample for the main theorems was given in [ibid. 51, No. 5-6, 847–849 (2010; Zbl 1190.90069)].

MSC:

90B35 Deterministic scheduling theory in operations research
68T05 Learning and adaptive systems in artificial intelligence

Citations:

Zbl 1190.90069
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References:

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