Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Algorithms for Scheduling Independent Tasks
Journal of the ACM (JACM)
Single machine scheduling with a variable common due date and resource-dependent processing times
Computers and Operations Research
Single-machine group scheduling with a time-dependent learning effect
Computers and Operations Research
Single machine batch scheduling with deadlines and resource dependent processing times
Operations Research Letters
Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects
Information Sciences: an International Journal
Solution algorithms for the makespan minimization problem with the general learning model
Computers and Industrial Engineering
Computers & Mathematics with Applications
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
Experience-based approach to scheduling problems with the learning effect
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Scheduling problems with deteriorating jobs and learning effects including proportional setup times
Computers and Industrial Engineering
Scheduling with job-dependent learning effects and multiple rate-modifying activities
Information Processing Letters
Minimizing the makespan on a single machine with learning and unequal release times
Computers and Industrial Engineering
Computers and Operations Research
Scheduling problems with general effects of deterioration and learning
Information Sciences: an International Journal
A note on the learning effect in multi-agent optimization
Expert Systems with Applications: An International Journal
Some single-machine scheduling problems with a truncation learning effect
Computers and Industrial Engineering
Computers and Industrial Engineering
The single-machine total weighted tardiness scheduling problem with position-based learning effects
Computers and Operations Research
Two-machine flowshop scheduling with truncated learning to minimize the total completion time
Computers and Industrial Engineering
Scheduling with general position-based learning curves
Information Sciences: an International Journal
Two-agent scheduling with learning consideration
Computers and Industrial Engineering
Information Sciences: an International Journal
Information Sciences: an International Journal
Computers and Industrial Engineering
Information Sciences: an International Journal
Computers and Operations Research
Information Sciences: an International Journal
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In this paper, we bring into the scheduling field a new model of the learning effect, where in two ways the existing approach is generalized. First we relax one of the rigorous constraints, and thus in our model each job can provide different experience to the processor. Second we formulate the job processing time as a non-increasing k-stepwise function, that, in general, is not restricted to a certain learning curve, thereby it can accurately fit every possible shape of a learning function. Furthermore, we prove that the problem of makespan minimization with the considered model is polynomially solvable if every job provides the same experience to the processor, and it becomes NP-hard if the experiences are diversified. The most essential result is a pseudopolynomial time algorithm that solves optimally the makespan minimization problem with any function of an experience-based learning model reduced into the form of the k-stepwise function.