Single-machine scheduling

TL;DR: A new scheduling model in which deteriorating jobs and learning effect are both considered simultaneously is introduced, and it is shown that the problems to minimize total weighted completion time and maximum lateness are polynomially solvable under certain conditions.

TL;DR: This paper investigates the job scheduling problem of two agents competing for the usage of a common single machine with learning effect and develops several dominance properties and a lower bound based on a branch-and-bound to find the optimal algorithm.

TL;DR: This paper considered a single-machine scheduling problem with a linear deterioration assumption where the objective is to minimize the total weighted completion time of jobs from the first agent with the restriction that no tardy job is allowed for the second agent.

TL;DR: It is shown that the makespan minimization problem, the sum of the @dth power of job completion times minimization Problem can be solved by the smallest (normal) processing time first (SPT) rule, and the total weighted completion time and the maximum lateness problem can be solve in polynomial time under certain conditions.

TL;DR: It is shown that the problems of single-machine scheduling problems in which the processing time of a job is a function of its starting time and its resource allocation remain polynomially solvable under the proposed model.