A note on scheduling on a single processor with speed dependent on a number of executed jobs
Information Processing Letters
When are we going to change the learning curve lecture?
Computers and Operations Research
Behind the Learning Curve: Linking Learning Activities to Waste Reduction
Management Science
Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects
Information Sciences: an International Journal
A new approach to the learning effect: Beyond the learning curve restrictions
Computers and Operations Research
Some scheduling problems with general position-dependent and time-dependent learning effects
Information Sciences: an International Journal
Single-machine scheduling with sum-of-logarithm-processing-times-based learning considerations
Information Sciences: an International Journal
Machine scheduling problems with a general learning effect
Mathematical and Computer Modelling: An International Journal
Information Sciences: an International Journal
Uniform parallel-machine scheduling to minimize makespan with position-based learning curves
Computers and Industrial Engineering
Information Sciences: an International Journal
Several flow shop scheduling problems with truncated position-based learning effect
Computers and Operations Research
Single machine scheduling with autonomous learning and induced learning
Computers and Industrial Engineering
Parallel-machine scheduling to minimize makespan with fuzzy processing times and learning effects
Information Sciences: an International Journal
Hi-index | 0.07 |
Learning effect in scheduling problems has received growing attention since Biskup [3] introduced the position-based model, where the learning curve is expressed as a power function of a job position. Hurley [11] pointed out that the actual processing time of a given job drops to zero precipitously as the number of jobs increases in the standard power model. Moreover, the learning rates show considerable variation within industries or firms. The variation extends not only across firms at a given time, but also within firms over time. For instance, the learning curves usually have an initial downward concavity, and no further improvements are made after some amount of production. Beside the standard power model, learning curve is seldom discussed in scheduling. In this paper, we offer a surprising simple yet realistic learning effect model which has the flexibility to describe different learning curves easily. For instance, the standard power model, the well-known S-shaped and the plateau functions are special cases of the proposed model. We then present the optimal solution for some scheduling problems.