Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Single-machine group scheduling with a time-dependent learning effect
Computers and Operations Research
Project Management: A Systems Approach to Planning, Scheduling, and Controlling
Project Management: A Systems Approach to Planning, Scheduling, and Controlling
Single-machine scheduling problems with the time-dependent learning effect
Computers & Mathematics with Applications
Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects
Information Sciences: an International Journal
Single-machine scheduling with sum-of-logarithm-processing-times-based learning considerations
Information Sciences: an International Journal
Single-machine scheduling problems with deteriorating jobs and learning effects
Computers and Industrial Engineering
Single machine scheduling with time-dependent deterioration and exponential learning effect
Computers and Industrial Engineering
Computers and Operations Research
Adaptive job routing and scheduling
Engineering Applications of Artificial Intelligence
Single-machine scheduling with a nonlinear deterioration function
Information Processing Letters
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
The single-machine total weighted tardiness scheduling problem with position-based learning effects
Computers and Operations Research
Solving a two-agent single-machine scheduling problem considering learning effect
Computers and Operations Research
SIAM Journal on Computing
Information Sciences: an International Journal
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
| Hi-index | 0.01 |
In this paper, the single processor scheduling problem to minimize the total weighted completion times is analysed, where the processing times of jobs are described by functions dependent on the sum of the normal processing times of previously processed jobs, which can model learning or aging (deteriorating) effects. We construct the exact pseudopolynomial time algorithm based on the dynamic programming, which solves the problem, where the processing time of each job is described by an arbitrary stepwise function. Moreover, the parallel metaheuristic algorithms are provided for the general version of the problem with arbitrary sum-of-processing time based models. The efficiency of the proposed algorithms is evaluated during numerical analysis.