Single-machine time-dependent scheduling problems with fixed rate-modifying activities and resumable jobs

Arigliano, Anna; Ghiani, Gianpaolo; Grieco, A; Guerriero, Emanuela

doi:10.1007/s10288-016-0333-z

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…In addition, we identified alternative approaches to model the learning effect, using a perceptron neural network learning algorithm [72]. We also found some articles corresponding to the inclusion of rate-modifying activities (RMA) [73][74][75][76][77][78][79][80], which changes the processing conditions in a system and adjusts the production rate of a resource. Two examples are maintenance activities or rest periods, which usually increase production rates.…”

Section: Dejong's Learning Effect (Dj-le)mentioning

confidence: 99%

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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In traditional scheduling problems, job processing times are considered constant and known in advance. This assumption is, however, a simplification when it comes to hand-intensive real-life production contexts because workers usually induce variability in the job processing times due to several factors such as learning, monotony, fatigue, psychological factors, etc. These effects can decrease or increase the actual processing time when workers execute a job. The academic literature has reported several modeling and resolution approaches to deal with the phenomenon in a variety of configurations. However, there is no comprehensive review of these research outputs to the best of our knowledge. In this paper, we follow a systematic approach to review relevant contributions addressing the scheduling problem with learning and deterioration effects. Modeling approaches for learning and deterioration effects, objective functions, and solution methods employed in the literature are the main topics for the taxonomy proposed in this review. A total of 455 papers from 1999 to 2021 are included and analyzed. Different areas of interest are presented, and some opportunities for future research are identified.

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