Chin-Chia Wu

TL;DR: A new scheduling model with learning effects in which the actual processing time of a job is a function of the total normal processing times of the jobs already processed and of the job's scheduled position is introduced, showing that the single-machine problems to minimize makespan and total completion time are polynomially solvable.

TL;DR: A new scheduling model in which both job deterioration and learning exist simultaneously is introduced, and polynomial-time optimal solutions for some special cases of the problems to minimize makespan and total completion time are presented.

TL;DR: A branch-and-bound and a heuristic algorithm are proposed to search for optimal and near-optimal solutions, respectively for a bi-criterion single-machine scheduling problem with a learning effect.

TL;DR: In this article, the authors consider a two-agent scheduling problem in which the actual processing time of a job in a schedule is a function of the sum-of-processing-times-based learning and a control parameter of the learning function.

TL;DR: This paper extends models in which the actual job processing time not only depends on its scheduled position, but also depends on the sum of the processing times of jobs already processed and shows that the single-machine makespan and the total completion time problems remain polynomially solvable under the proposed model.