Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Minimizing the makespan in a single machine scheduling problem with a time-based learning effect
Information Processing Letters
Single-machine group scheduling with a time-dependent learning effect
Computers and Operations Research
Computers and Industrial Engineering
Single machine scheduling with past-sequence-dependent setup times and learning effects
Information Processing Letters
Single-machine scheduling problems with the time-dependent learning effect
Computers & Mathematics with Applications
The learning effect: Getting to the core of the problem
Information Processing Letters
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 deteriorating jobs and learning effects
Computers and Industrial Engineering
Single-machine scheduling with sum-of-logarithm-processing-times-based learning considerations
Information Sciences: an International Journal
Some single-machine and m-machine flowshop scheduling problems with learning considerations
Information Sciences: an International Journal
Scheduling problems with deteriorating jobs and learning effects including proportional setup times
Computers and Industrial Engineering
Single-machine group scheduling problems under the effects of deterioration and learning
Computers and Industrial Engineering
Scheduling with job-dependent learning effects and multiple rate-modifying activities
Information Processing Letters
Computers & Mathematics with Applications
Computers and Operations Research
A note on the learning effect in multi-agent optimization
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Two-machine flowshop scheduling with truncated learning to minimize the total completion time
Computers and Industrial Engineering
Information Sciences: an International Journal
Unrelated parallel-machine scheduling with aging effects and multi-maintenance activities
Computers and Operations Research
Information Sciences: 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
An Agent Based Approach to Patient Scheduling Using Experience Based Learning
International Journal of Agent Technologies and Systems
Computers and Operations Research
Information Sciences: an International Journal
Several flow shop scheduling problems with truncated position-based learning effect
Computers and Operations Research
Information Sciences: an International Journal
Flowshop scheduling with a general exponential learning effect
Computers and Operations Research
Journal of Combinatorial Optimization
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The existence of the learning effect in many manufacturing systems is undoubted; thus, it is worthwhile that it be taken into consideration during production planning to increase production efficiency. Generally, it can be done by formulating the specified problem in the scheduling context and optimizing an order of jobs to minimize the given time criteria. To carry out a reliable study of the learning effect in scheduling fields, a comprehensive survey of the related results is presented first. It reveals that most of the learning models in scheduling are based on the learning curve introduced by Wright. However, further study about learning itself pointed out that the curve may be an "S"-shaped function, which has not been considered in the scheduling domain. To fill this gap, we analyze a scheduling problem with a new experience-based learning model, where job processing times are described by "S"-shaped functions that are dependent on the experience of the processor. Moreover, problems with other experience-based learning models are also taken into consideration. We prove that the makespan minimization problem on a single processor is NP-hard or strongly NP-hard with the most of the considered learning models. A number of polynomially solvable cases are also provided.