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
Some single-machine and m-machine flowshop scheduling problems with learning considerations
Information Sciences: an International Journal
Single-machine group scheduling with resource allocation and learning effect
Computers and Industrial Engineering
Scheduling problems with general effects of deterioration and learning
Information Sciences: an International Journal
Estimation of distribution algorithm for permutation flow shops with total flowtime minimization
Computers and Industrial Engineering
Computers and Industrial Engineering
Single-machine scheduling jobs with exponential learning functions
Computers and Industrial Engineering
Single-machine group scheduling with both learning effects and deteriorating jobs
Computers and Industrial Engineering
A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem
Computers and Industrial Engineering
Two-machine flowshop scheduling with truncated learning to minimize the total completion time
Computers and Industrial Engineering
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In traditional scheduling problems, the processing time for the given job is assumed to be a constant regardless of whether the job is scheduled earlier or later. However, the phenomenon named ''learning effect'' has extensively been studied recently, in which job processing times decline as workers gain more experience. This paper discusses a bi-criteria scheduling problem in an m-machine permutation flowshop environment with varied learning effects on different machines. The objective of this paper is to minimize the weighted sum of the total completion time and the makespan. A dominance criterion and a lower bound are proposed to accelerate the branch-and-bound algorithm for deriving the optimal solution. In addition, the near-optimal solutions are derived by adapting two well-known heuristic algorithms. The computational experiments reveal that the proposed branch-and-bound algorithm can effectively deal with problems with up to 16 jobs, and the proposed heuristic algorithms can yield accurate near-optimal solutions.